diff --git a/.github/workflows/lab-scheduling.yml b/.github/workflows/lab-scheduling.yml new file mode 100644 index 00000000..798c9cd9 --- /dev/null +++ b/.github/workflows/lab-scheduling.yml @@ -0,0 +1,97 @@ +name: Laboratory scheduling application + +on: + push: + branches: [contest/qubo-scheduling-app] + paths: + - 'pyqpanda-algorithm/pyqpanda_alg/LabScheduling/**' + - 'pyqpanda-algorithm/pyqpanda_alg/QAOA/**' + - 'pyqpanda-algorithm/example/LabScheduling/**' + - 'test/LabScheduling/**' + - '.github/workflows/lab-scheduling.yml' + pull_request: + branches: [develop, main] + paths: + - 'pyqpanda-algorithm/pyqpanda_alg/LabScheduling/**' + - 'pyqpanda-algorithm/pyqpanda_alg/QAOA/**' + - 'pyqpanda-algorithm/example/LabScheduling/**' + - 'test/LabScheduling/**' + - '.github/workflows/lab-scheduling.yml' + workflow_dispatch: + +permissions: + contents: read + +jobs: + cpu-tests: + name: CPU tests / Python ${{ matrix.python }} + runs-on: ubuntu-latest + timeout-minutes: 15 + strategy: + fail-fast: false + matrix: + include: + - python: '3.11' + constraints: constraints-py311.txt + - python: '3.12' + constraints: constraints-py312.txt + - python: '3.13' + constraints: constraints-py312.txt + env: + OMP_NUM_THREADS: '1' + OPENBLAS_NUM_THREADS: '1' + MPLBACKEND: Agg + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python }} + - name: Install tested dependencies inside project venv + run: | + python -m venv .venv + .venv/bin/python -m pip install -c pyqpanda-algorithm/example/LabScheduling/${{ matrix.constraints }} -e ./pyqpanda-algorithm -r pyqpanda-algorithm/example/LabScheduling/requirements-dev.txt + .venv/bin/python -m pip check + - name: Lint, format and type checks + run: | + .venv/bin/python -m ruff check --config test/LabScheduling/ruff.toml pyqpanda-algorithm/pyqpanda_alg/LabScheduling test/LabScheduling pyqpanda-algorithm/example/LabScheduling + .venv/bin/python -m ruff format --check --config test/LabScheduling/ruff.toml pyqpanda-algorithm/pyqpanda_alg/LabScheduling test/LabScheduling pyqpanda-algorithm/example/LabScheduling + .venv/bin/python -m mypy --config-file test/LabScheduling/mypy.ini --python-version ${{ matrix.python }} pyqpanda-algorithm/pyqpanda_alg/LabScheduling + - name: Mathematical and real CPU simulator tests + run: | + .venv/bin/python -m pytest -c test/LabScheduling/pytest.ini test/LabScheduling --timeout=60 --cov=pyqpanda-algorithm/pyqpanda_alg/LabScheduling --cov-fail-under=95 + - name: End-to-end medium example + run: | + .venv/bin/python -m pyqpanda_alg.LabScheduling pyqpanda-algorithm/example/LabScheduling/data/medium.json --output reports/medium.json + - name: Existing upstream algorithm tests + run: | + .venv/bin/python -m pytest -c test/LabScheduling/pytest.ini -o 'python_files=Test_*.py' test --ignore=test/LabScheduling --timeout=60 + - name: Booking cascade and unchanged control + run: | + .venv/bin/python pyqpanda-algorithm/example/LabScheduling/booking_demo.py + - name: Coupled booking tradeoff, unchanged control and weight sensitivity + run: | + .venv/bin/python pyqpanda-algorithm/example/LabScheduling/tradeoff_demo.py --sensitivity + - name: Arc pruning crosses the simulator resource boundary + run: | + .venv/bin/python -m pyqpanda_alg.LabScheduling pyqpanda-algorithm/example/LabScheduling/data/support-chain.json --reduce --pruning arc --output reports/support-chain.json + - name: Expected failure states remain explicit + run: | + .venv/bin/python - <<'PY' + import json + import subprocess + import sys + from pathlib import Path + + data = Path('pyqpanda-algorithm/example/LabScheduling/data') + for name, code, status in [ + ('support-chain', 5, 'resource_limit'), + ('infeasible', 3, 'infeasible'), + ]: + output = Path('reports') / (name + '-failure.json') + result = subprocess.run([ + sys.executable, '-m', 'pyqpanda_alg.LabScheduling', + str(data / (name + '.json')), '--reduce', '--output', str(output), + ], check=False) + assert result.returncode == code, (name, result.returncode) + assert json.loads(output.read_text())['status'] == status + PY diff --git a/.gitignore b/.gitignore index 62a6beb8..9b985c91 100644 --- a/.gitignore +++ b/.gitignore @@ -44,3 +44,12 @@ pyqpanda-algorithm/pyqpanda_alg/QSolver/*.lib /pyqpanda-algorithm/pyqpanda_alg/QSolver/libcurl.dll .ccls-cache + +# Local scheduling development artifacts +.venv/ +.pytest_cache/ +.ruff_cache/ +.mypy_cache/ +.coverage +htmlcov/ +allure-results/ diff --git a/AWARD_REVIEW.md b/AWARD_REVIEW.md new file mode 100644 index 00000000..da0d23bf --- /dev/null +++ b/AWARD_REVIEW.md @@ -0,0 +1,165 @@ +# LabReschedule 竞争力复审与增强方案 + +日期：2026-09-21。队伍：youshen。性质：本地评审建议，不是新增功能交付或获奖承诺。 +PR #92 已存在；本轮不推送、不更新 PR、不操作比赛平台。 +本文保留增强前的评审意见；后续已获批准的实现与验证见 +[增强教程](Tutorials/LabScheduling/ENHANCEMENTS.md)。 + +## 先校准目标 + +[官网奖项总览](https://qcloud.originqc.com.cn/learning/zh/2026ccf)目前将开源创新赛道 +列为 10 队获奖，每队 1000 元、价值 1.8 万元机时及证书，没有列出“特等奖”。 +官网的特等奖属于其他赛道，不能把它们套用到本作品。 +[赛题 #13](https://github.com/OriginQ/pyqpanda-algorithm/issues/13)要求完整创新应用、 +PR 到 develop、维护者评审，未给出可核实的分项评分权重。 +因此下文是争取本赛道领先作品的工程判断，不是官方评分表。 + +## 当前判断 + +当前版本是工程基础扎实、数学可核验的小规模完整应用。但仅凭两个手工样例、 +三种子和“均找到最优”，还不足以支撑领先竞争力。主要缺口是： + +1. 样例过易：中等例只有 4 个任务、81 种 one-hot 组合。 +2. 业务价值尚未验证：没有真实用户反馈或公开可追溯的业务数据；显式候选需要手工提供。 +3. 量子收益不稳定：成功样本掩盖了概率分布差异；可行率不等于最优命中率。 +4. 扩展边界硬：整体超过 16 比特即无法运行量子路径，没有问题分解与重建机制。 +5. 创新主要在场景组合；QUBO、W 初态、XY 混合器本身已有研究，不能列作原创算法。 + +现有 56 项测试、98.46% 覆盖率、独立精确认证和诚实呈现失败仍是重要基础。 +其他开放作品已有多实例、资源审计或硬件证据；堆文档、加测试数量不能替代核心贡献。 +参照对象是 [PR #53](https://github.com/OriginQ/pyqpanda-algorithm/pull/53) 和 +[PR #60](https://github.com/OriginQ/pyqpanda-algorithm/pull/60) 的公开正文；未独立认证其全部实验。 + +## 本轮实际复核的新证据 + +复用归档的最终参数，在当前 PyQPanda3 CPU 模拟器中重新计算完整分布。 +均匀基线在每个任务域独立等概率选择。两例均只有一个全局最优排程。 +下表是给定已训练分布时、独立采样的理论命中指标，不含训练成本。 + +| medium 方法 | 单次最优概率 | 达到至少 95% 最优命中率需要的 shots | +|---|---:|---:| +| 均匀 one-hot | 1.2346% | 242 | +| XY，seed 7 | 4.6071% | 64 | +| XY，seed 19 | 9.1657% | 32 | +| XY，seed 42 | 0.3066% | 976 | + +计算公式为 `P(hit in S)=1-(1-p_opt)^S`。均匀基线在 512 shots 下已达 +99.8271%；因此原来的 512 shots 最优值对比区分度很低。 +simple 的 seed 19 同样存在退化：最优概率 1.4736%，低于均匀基线 11.1111%。 +原始复核数据保存在本地 `reports/award-review/lab-award-probe.json`。 + +另做了不修改正式代码的线路实验：只在 XY 成本相位中删除 one-hot 惩罚， +保留原惩罚缩放、初态和混合器。原因是整个演化都处于每任务恰选一次的子空间， +该罚项在此处恒为零；其指数作用为恒等算符。 + +| 实例，p=1 | CNOT 原始→简化 | 原生线路深度原始→简化 | +|---|---:|---:| +| simple | 34→22（降低 35.3%） | 36→24 | +| medium | 78→54（降低 30.8%） | 45→35 | + +每例检查 15 组参数，覆盖 p=1/2/3；概率最大绝对差分别为 3.61e-16、1.18e-16。 +该探索验证不是正式回归测试，也不证明有噪声硬件执行等价。 +它沿用已有约束子空间思想，不是新发明的量子算法。 +原始数据：`reports/award-review/lab-award-circuit-probe.json`。 +背景参考：[Wang 等，XY-mixers](https://arxiv.org/abs/1904.09314)。 + +## 推荐的主线 + +把作品聚焦为：**把设备故障后的重排问题，经过有保证的约束化简，转成可在有限 +量子资源下求解的子问题，并对资源节省、解质量和失败边界给出完整证据。** +优先保证可证明的节省与真实可用性，再研究随机优化收益。 + +| 优先级 | 工作 | 价值 | 预计工作量 | 关键风险 | +|---|---|---|---|---| +| P0 | XY 成本线路删除恒零 one-hot 惩罚 | 已有确定门数节省证据 | 0.5–1 天 | 不可用于 X 分支；仍保留完整 QUBO 审计 | +| P0 | 安全候选化简、冲突分量分解、原问题重建 | 从小演示走向受资源约束的工具 | 2–3 天 | 不得把启发式冻结称为全局等价 | +| P0 | 多实例留出评测、低 shots 曲线、强经典基线 | 证明有效范围，暴露退化 | 1–2 天，可与实现交叉 | 调参泄漏、预算比较失真 | +| P1 | 面向预约业务的数据入口与变更说明 | 用户可直接理解和复用 | 0.5–1 天 | 合成数据冒充真实需求 | + +### 化简必须守住的数学边界 + +- 单候选任务固定，向相邻任务传播不兼容候选删除；空域是不可行证明。 +- 二元约束的无支持候选可以安全删除，但局部一致性不能证明全局可行。 +- 同任务候选支配可作为后续项：更低或相等成本、冲突邻域为子集时，可保留更优候选； + 一般只保证最优值保留，不能声称所有可行排程都原样保留。实现须处理等价候选稳定保留。 +- 在任务冲突图上做连通分量分解。没有跨分量约束且目标可加时，子问题最优值可相加； + QAOA 子问题未最优时，组装解也没有全局最优保证。每个组件仍需实际检查可行性。 +- 固定变量必须回填，成本偏移必须累计，最终在原始输入上重新检查全部约束。 +- 某个分量仍超过资源上限时明确报告，不静默截断候选，不把任意切块叫精确分解。 +- “只重排直接受停机影响的任务”通常不保持最优：冲突会传播。 + 如增加固定其余预约的局部启发式，必须作为单独模式报告边界、失败和次优差距。 + +### 实验设计与验收 + +1. 先冻结生成器、实例清单和参数选择规则。建议 24 个留出实例，覆盖候选规模、 + 冲突密度、停机强度及链式工序；另设开发集，不看留出结果再调默认参数。 +2. 至少 10 个优化种子；shots 取 8/16/32/64/128/512。用固定训练分布作解析曲线， + 再用独立采样种子核验；不要把同一分布的重复抽样当独立训练样本。 +3. 对照包括原始 XY、简化 XY、均匀 one-hot、业务贪心/修复，以及独立经典优化器。 + 可用已有 SciPy 的 MILP，从原始时段/日历/前序独立构建约束，避免复用 QUBO + 编译后的冲突图及剪枝作为唯一输入；不借 QUBO 罚函数作唯一裁判。 + 超时输出 incumbent、bound 和 gap，不能冒充最优证书。 +4. 报告最优命中率、可行率、目标差距、变更预约数量、最大时间移动量、比特数、 + 二比特门数、深度、峰值内存和端到端时间。训练评估次数和模拟开销单独列出。 + 采样预算对齐不代表总计算预算对齐；不能把训练完成后的 shots 节省叫整体加速。 +5. 化简在至少 100 个小规模生成实例上用穷举核对最优值、可行性和重建；加入 + 连锁删除、不可行、完全连通、完全独立、单候选及相等成本等反例测试。 +6. 简化线路加入 p=1/2/3、随机参数、不同域大小和单候选的真实 CPU 回归， + 建议分布误差阈值 1e-10，记录门数下降；原始 X 路径保留完整约束。 +7. 报告所有预先登记实例，包括未改善、资源超限和采样失败。 + 若量子效果不稳，收缩为“可证明资源压缩与可审计求解”，不包装成稳定优于经典。 + +### 业务展示 + +提供原预约→故障→调整后预约的离线甘特图，标出每个变更的原因及成本构成。 +补一个从时间窗、可用设备生成显式候选的入口，或先给出透明的导入模板。 +如没有可公开真实数据，就标为合成压力测试；需求访谈和匿名实际试用有价值， +但不能由模型编造。保留成本权重解释，避免把加权成本最小误称为变更人数最少。 + +## 截至 9 月 28 日的执行顺序 + +- 9/21：完成复审，冻结原版本及评价口径；本轮仅本地文档和探索证据。 +- 9/22：线路简化正式实现与回归；冻结基准生成规则、开发/留出划分。 +- 9/23–24：安全化简、分解、重建；穷举反例和独立经典求解器。 +- 9/25–26：多实例盲测、全部失败记录、成本拆分和业务展示。 +- 9/27：干净环境复现、完整相关测试、最终 diff 与评委演示。 +- 9/28：展示增强版本与真实结果，再按用户最新授权决定是否更新已有 PR。 + +若化简无法按时证明正确，缩减为单候选传播和严格独立分量；先交付确定可靠的收益。 +若 9/23 前分量分解仍不稳定，暂不加入，保底包为：XY 相位简化、独立 MILP、 +至少 12 个实例 × 10 个优化种子的探索性证据，以及一个连锁改约案例。 +不优先增加 Web/数据库、预测模型、新 QAOA 变体、复杂噪声模型或真机依赖。 +不为凑规模拼接互不相关小例后声称解决了同规模一般调度。 + +## 独立审查记录 + +本轮按用户要求委派一个子 agent 完成独立代码、实验和规则审查。 +详细原稿保存于本地 `reports/award-review/independent-review.md`，未提交到 Git。 +其中概率和门数复核由主 agent 实际运行后提供，子 agent 将其纳入综合评审。 +两个审查视角的共同结论及分歧后的优先级已在本文汇总。 + +## 增强后的主工程师收敛判断（2026-09-21） + +上文是历史审查；本节依据已交付成果更新工程判断，不是官方评分或新增独立评审。 + +| 原缺口 | 当前证据 | 仍然存在的边界 | +|---|---|---| +| 少量手工样例 | 36 个冻结异构实例、多种子、全部失败记录及完整重放 | 仍是小规模合成数据 | +| 量子资源上限 | 精确分量回填、可选 AC-3、18→12 受控案例、可行集合保持证明 | 不解决一般大连通调度 | +| 量子收益难解释 | 同总预算与均匀随机对照，低 shots 概率及退化公开 | 不存在已证实的端到端量子优势 | +| 业务入口和解释 | CSV 时间窗、原预约变更表、成本阈值与并列最优 | 没有真实用户试用或经济收益证据 | +| 可复现与交付 | 三版本 CI、独立 wheel 安装、源文件指纹、失败退出码 | 官方工作流仍需维护者批准 | + +再次审查找到并修复了一个实质边界：大量原始候选被日历剪枝后已可量子求解， +却因独立 MILP 超过 2048 原始候选而丢失整个报告。现在显式记录该基线超预算， +保留实际样本和可用的穷举证书；两个认证路径都不可用时不填写最优差距。 +五项新增回归在修复前四失败、一通过，修复后全部通过；五个既有案例的全部 +非计时报告字段与修复前一致。当前 113 项应用测试通过，核心覆盖率 98.92%。 + +工程判断：已达到完整参赛交付和接受维护者实质评审的标准。现阶段再追加通用 +算法、重复 PR 或更大合成实验，预期价值低于处理评审意见和取得一次真实试用。 +本轮结束主动功能扩张；后续仅由可复现缺陷、实际需求或维护者意见触发代码修改。 +奖项由主办方决定，当前材料不能支持获奖概率或“必进前十”的判断。 + +对外展示以 REVIEW_GUIDE 的五分钟路线与六个答辩问题为准。参赛平台回执、 +官方审核以及未获奖是否发证明仍应单独核验，不能由工程测试通过替代。 diff --git a/PLAN.md b/PLAN.md new file mode 100644 index 00000000..c6b4e610 --- /dev/null +++ b/PLAN.md @@ -0,0 +1,472 @@ +# 2026 本源杯：LabReschedule 约束调度应用 + +## 调研与定题（2026-09-21） + +- 工作目录最初为空；已克隆官方仓库，以 `upstream/develop` + `5f973efccb84bc193157d1ccebe32137e307293b` 创建 + `contest/qubo-scheduling-app`。不推送、不创建 PR，最终由用户确认。 +- 阅读 develop 的 README、CONTRIBUTING、QUBO/QAOA 源码、测试和 CI， + 并核对 main 的贡献指南。新增内容沿用 `pyqpanda_alg`、`example`、 + `test`、`Tutorials` 结构，不重构上游模块。 +- [赛题 #13](https://github.com/OriginQ/pyqpanda-algorithm/issues/13) + 明确允许完整创新应用，参与期 4 月 10 日至 9 月 30 日，PR 基分支 develop， + 标题 `【创新应用】+简要描述`。按用户目标于 9 月 28 日前备妥。 +- [贡献指南](https://github.com/OriginQ/pyqpanda-algorithm/blob/main/CONTRIBUTING.md) + 要求测试、用户文档及索引；本地完成后经个人 fork 提交。 +- [比赛主页](https://qcloud.originqc.com.cn/learning/zh/2026ccf) 为 JavaScript + 页面，初次抓取仅得到启用 JavaScript 提示，不能据此声称已核实平台报名细则。 +- 查阅 [PyQPanda3 AI 参考](https://github.com/OriginQ/pyqpanda3-skill)： + 使用整数比特编号、CPUQVM、PyQPanda3 API；以安装版本和实际测试核对参考代码。 +- GitHub 匿名 REST API 限流，改读 GitHub HTML；逐页读取开放 PR 第 1–3 页， + 第 4 页为空；另查已关闭 PR、开放 Issues #8/#13/#38 和 develop 源码。 + 未发现考试/实验室重排同题。最接近 + [PR #60](https://github.com/OriginQ/pyqpanda-algorithm/pull/60) + 是小麦灌溉预测和水预算调度，已阅读其正文；本项目不做水预算、预测或农业应用。 + 开放 PR #45/#69 涉及 QUBO 默认参数和多项式解析，避免依赖其修复或重复提交。 + 去重是当前快照结论，提交前须再查。 + +## 选题与范围 + +**LabReschedule：设备停机后的共享实验室预约最小扰动重排。** +面向校园小组实验：每项实验选择一个允许的设备/开始时刻候选，持续多个时隙； +考虑设备停机、清洗占用、小组互斥、工序先后。目标权衡时刻偏好与原预约变更。 +输入显式候选而非全时间网格，消除停机候选，减少比特数并保留剪枝原因。 + +1. 严格 JSON 校验，清晰报告错误输入与有效但不可行的问题。 +2. 稀疏候选一位有效编码，目标及约束转为二次项；给出有证明的惩罚上界。 +3. PyQPanda3 CPU 上运行 QAOA，复用 pyqpanda_alg 相位线路和 XY 混合器组件。 + XY 保持每项任务的选择数；X 混合器提供消融对照。 +4. 独立任务候选笛卡尔积穷举，认证可行性与最优值；不向量子求解器传入最优解。 +5. 固定种子参数初始化与模拟概率采样，仅从真实采样集合选择量子候选； + 输出违反明细、目标、概率质量、耗时、最优差距及模型规模。 +6. 简单、中等、不可行数据；一条命令运行；多种子实验、数学恒等式测试、 + 位序/单候选/空候选/错误输入/采样失败测试；中文教程和演示流程。 + +不引入 Web、数据库、云服务或真实硬件。小规模端到端正确性优先； +默认量子规模限制为 16 比特，经典认证限制候选组合数，明确指数复杂度。 +不声称量子加速或获奖保证。 + +## 风险与应对 + +- 上游测试配置缺 `[pytest]` 节；依赖声明可能缺 pandas/scikit-learn。 + 先记录原始失败，再用外部配置运行，不修改无关上游问题。 +- 原库量子算法接口与技能示例可能不一致：用真实线路小测试确认， + 显式比特映射，绕开符号名字排序和常数多项式边界。 +- QAOA 可能找不到可行或最优解：保留原始结果，不静默修复/替换； + 可行概率、均匀抽样基线及经典认证分别展示。 +- XY 只保持恰选一次，不能保证设备/小组/先后约束，仍需惩罚和独立检查。 +- 主页面平台细则、个人报名信息和 GitHub fork 未核实；准备提交材料， + 不伪造个人信息，外部发布前由用户确认。 + +## 实施与验收顺序 + +1. 环境：项目 `.venv`，Python 3.12；开发依赖和原始测试基线。 +2. 模型：输入、候选剪枝、独立校验、QUBO、经典认证与数学测试。 +3. 量子：CPUQVM + 上游组件、种子控制、采样报告和 CLI。 +4. 打磨：多规模/多种子/混合器对照、质量检查、中文教程与实验归档。 +5. 提交：逻辑拆分本地 commits、diff/秘密/缓存检查、PR 描述、 + `SUBMISSION_CHECKLIST.md`，呈现成果后等待 push/PR/平台提交确认。 + +每阶段实际结果追加到本文件及实验验证报告。 + +## 阶段结果 + +- 调研完成：直接读取官方主页前端资源，补充核实开源赛道报名和 PR 均截至 + 9 月 30 日；未登录核实个人报名。70 项开放 PR 完整标题快照见 + `Tutorials/LabScheduling/research_pr_snapshot.json`，相关差异见 `RESEARCH.md`。 +- 环境完成：Python 3.12.3，项目 `.venv`；PyQPanda3 0.4.1，editable + pyqpanda_alg 2.0.0；没有全局安装。开发环境约束已锁定。 +- 上游基线：初始缺 Allure 插件，且 import 缺 pandas；在应用开发依赖补齐 + pandas/scikit-learn/Allure。原配置虽然缺节标题，当前 pytest 9 能解析， + 不把该事实错误报告为必然配置崩溃。独立配置下原有 18 项有效测试全部通过。 + 多数其他测试文件已被上游注释，不计作通过。详见验证报告。 +- 实现完成：输入、剪枝、QUBO/Ising、CPUQVM QAOA、XY/X、独立精确基线、 + 均匀 one-hot 基线、采样证据、CLI、多种子基准与静态图表。 +- 测试与打磨：应用数学模型与 Ising 对角能量逐状态验证；种子 7/19/42 + 在 simple/medium 的默认 XY 均采到经典最优，X 对照在 medium 有非零差距。 + 不宣称量子优势；原始 JSON/CSV、中文教程、聚焦 CI 与验证说明随项目提交。 +- 交付与提交信息：详见 `SUBMISSION_CHECKLIST.md` 和 `PR_DESCRIPTION.md`。 + 尚未推送、创建 PR 或向平台提交，按用户要求保留最终确认步骤。 + +## 发布授权更新（2026-09-21） + +用户已批准推送至个人 fork 并向官方 develop 创建 PR，确认官网报名完成， +队伍名称 `youshen`。上述“等待确认/尚未发布”为最初交付阶段的历史记录； +当前执行进度以 `SUBMISSION_CHECKLIST.md` 为准。 + +## 发布结果（2026-09-21） + +以 GitHub 账号 `youshen2025` 发布个人 fork 功能分支，创建官方 develop 的 +[正式 PR #92](https://github.com/OriginQ/pyqpanda-algorithm/pull/92)，队伍 `youshen`。 +初版发布时远程与本地提交一致，GitHub 报告可合并。Actions 已触发，但等待上游维护者 +批准运行，不能计作 CI 通过。官网报名由用户确认；比赛平台 PR 链接与回执仍待核对。 + +## 2026-09-21 增强实施 + +用户批准按独立复审继续。实现 XY 恒零 one-hot 相位删除（完整 QUBO 保留）、 +单候选传播/严格独立冲突分量/原变量回填、原始输入 MILP 与 CSV 预约窗口导入。 +采用 12 个冻结合成评价实例 × 10 个优化种子；保留三个不可行实例和量子退化种子。 +增强报告 schema 2，与旧归档结果区分。证明、接口和结果见 +[Tutorials/LabScheduling/ENHANCEMENTS.md](Tutorials/LabScheduling/ENHANCEMENTS.md)。 + +增强完成时先保留本地提交供用户审阅；用户随后批准继续，现已推送增强版并更新 PR #92。 +发布前刷新 upstream/develop，仍为 `5f973ef`；PR 为 Open、非草稿、目标 develop， +GitHub 报告可合并。增强提交 `9c23ddb` 的 Actions 运行 `35576581365` 等待维护者批准， +尚未执行远程测试。未操作比赛平台，提交回执仍待核对。 +官网奖项归属的复核和原始建议见 [AWARD_REVIEW.md](AWARD_REVIEW.md)。 + +## 第二轮优化（2026-09-21，用户批准继续） + +保持核心算法不变，补齐业务与评价代表性、逐步对照和审阅体验。 +先以 `94efa84` 冻结 36 个异构输入（每格 4 数据种子），再完成 360 条配对运行， +其中 24 可行、12 不可行；不删除退化种子或纯经典传播情形。 +三单取舍案例将全部决策留在 8 比特组件，实际量子样本成本 12，独立认证最优； +高价备用设备对照成本 14。逐阶段资源与全部原始可行集合均经过核验。 +第二次完整重放完全复现非计时字段；76 应用测试通过，覆盖率 98.44%。 +独立 fork CI 将使用同一 workflow，不能替代上游批准或比赛平台回执。 +用户本轮批准优化与后续发布，继续更新现有 PR，不另开重复贡献。 +本轮已发布冻结协议 `94efa84`、验证实现 `798f1bc`、证据与审阅指南 `198ddfb`。 +个人 fork Actions `35583744393` 在 `198ddfb` 上全部通过；官方 PR 工作流仍待 +维护者批准，不能将独立 fork 运行视为上游审核通过。 + +## 第三轮优化计划（2026-09-21，用户要求继续） + +补齐原评价明确留下的预算可比性缺口：冻结同总采样/训练上限协议，新增 +精确化简后的均匀随机基线，区分结构化简收益与量子分布收益。不修改核心 +算法及已有评价数据，不增加无关应用。协议见 +[MATCHED_BUDGET_PROTOCOL.md](Tutorials/LabScheduling/MATCHED_BUDGET_PROTOCOL.md)。 + +第三轮实施完成：同预算 360 条配对记录全部重放，非计时字段一致；原始直接 +路径还与旧 V2 的 360 条记录一致。22 个量子可行实例中分量 QAOA 220/220 +采到最优，两个随机基线同样 220/220，因此报告明确不声称高 shots 量子优势。 +低 shots 条件分布收益与退化实例同时保留。当前 82 测试、98.44% 核心覆盖率、 +32 文件 Ruff 与 11 模块 Mypy 通过。详细结果见 MATCHED_BUDGET.md。 + +本轮已发布至 PR #92，提交 `a3716ed` 的个人 fork CI `35586823475` 全部通过， +Ubuntu / Python 3.12.14 测得 82 passed、覆盖率 98.44%，三个演示均成功。 +上游 CI `35586829560` 仍待维护者批准。后续补录仅修改本次发布记录。 + +## 第四轮：输入与报告完整性（2026-09-21） + +用户要求继续审查。已在临时副本复现两处问题：CLI 的同路径输出会覆盖输入； +报告结束时二次读文件计算哈希，外部修改输入会使指纹与实际求解数据不一致。 +修复范围限定为输入快照、严格文本解析入口、输出别名检查和完整写入后替换。 +新增回归覆盖同路径/符号链接/硬链接、求解期间修改或删除输入、CRLF 原字节指纹、 +写入/替换失败时旧报告保留及临时文件清理。QUBO、线路、优化器与旧评测结果不变。 + +修复 `bf4ca76` 与验证说明 `36adc9f` 已发布至 PR #92。本地/最新 wheel 隔离安装 +均通过 92 测试，原有 18 测试也再次通过。个人 fork CI `35588841891` 全部成功， +覆盖率 98.87%；上游 CI `35588846744` 仍待维护者批准。最后补录只修改文档。 + +## 第五轮：可选无支持候选化简（2026-09-21） + +用户允许继续优化或换方向。继续实验室故障重排主线：已有完整证据链，换题尚无 +明确收益。新增可选 AC-3 删除，补齐单候选传播无法处理的无支持候选；所有删除 +记录完整对方当前域和冲突原因。保留默认路径及旧评价归档，验证全部可行集合 +与成本，而非只检查最优值。预先记录范围见 ARC_PROTOCOL.md；不扩展无关功能。 + +第五轮实现与本地验证完成：可选 arc 路径保留全部可行集合及成本，200 个随机 +小问题和既有 V2 全部实例验证通过。V2 有 5/36 最大组件进一步缩小；新增构造 +案例从一个 18 比特组件降至 12 比特，无固定决策，三种子均实际采到最优成本 3。 +默认化简对 40 个输入与上一版完全一致。完整记录重放一致，102 项应用测试、 +98.92% 覆盖率、18 上游测试及隔离 wheel 完整测试通过。主线保持调度应用， +没有新增无关贡献；证明和证据见 ARC_PRUNING.md，远程验证随发布更新。 + +第五轮已更新 PR #92。`4eb272a` 的个人 fork CI `35597416071` 全部成功： +102 项测试、98.92% 覆盖率及四类 CPU 示例通过。上游 `35597421975` 仍待 +维护者批准，PR 为 Open、可合并；平台回执未操作。末次提交仅补录发布记录。 + +## 9 月 22–28 日收尾规划（2026-09-21 更新） + +### 当前判断与范围 + +本轮为后续规划。基线为已发布 `220daf7`：102 项应用测试、18 项上游测试， +核心覆盖率 98.92%；`4eb272a` 的个人 fork CI 通过，后续仅补录文档。 +再次读取 PR #92：Open、可合并，尚无评论或评审。最后一次实际执行的上游 +工作流仍待维护者批准；空的当前 statusCheckRollup 不视为 CI 已通过。 + +继续 LabReschedule 主线。当前竞争力在于应用建模、可证明的资源缩减、真实量子 +样本与独立认证；同预算结果不支持高 shots 量子优势，真实用户证据仍缺失。 +不以新增算法数量或覆盖率小数点作为下一阶段目标。整个 PR 目前 177 个文件、 +48,120 行新增，包含大量原始实验归档；审阅成本需要控制，不再另开无关功能线。 + +剩余工作的完成条件：评委能在锁定环境中按一条主要演示路线理解业务取舍、 +检查量子样本、核验经典证书，并能准确定位失败边界；维护者可定位核心 diff； +参赛提交状态与证据齐全。合并和评奖属于外部结果，不作为可承诺完成的事项。 + +### P0：一项业务解释增强，最多半天实现、半天验证 + +**问题：改约协调成本多大时，值得付费使用备用设备？** +复用现有 `tradeoff.json`，只改变三项任务的统一 `change_cost=w`， +预先固定 `w=0,1,2,3,4,5,6`。保留原有时间窗、前序、设备和偏好成本。 +不引入新求解器、新成本语义或连续成本；不更改核心默认参数。 + +- 两个已知候选排程的解析成本分别是：整体后移 `6+3w`，备用设备 `12+w`。 + 两条曲线在 `w=3` 相交。这里只是待穷举核验的两方案比较，不能据此预先 + 宣称它们覆盖全部全局最优解；必须枚举全部可行排程并验证并列最优。 +- 首次求解前提交输入变换、参数网格和验收规则。沿用已知开发案例，明确称为 + 敏感性分析，不包装成新留出评测或实际用户调研。 +- 七个输入各运行 seed 7/19/42，共 **21 次**真实 CPU QAOA；p=1、2 restarts、 + 每次最多 60 次评价、512 shots。保留全部未命中和非最优结果，不挑种子调参。 +- 每次量子输出继续只取实际样本；随后用穷举与原始输入 MILP 认证。 + 报告偏好成本、改约成本、改约数量、实际目标、认证最优、gap 和耗时。 +- 核验每个输入的完整可行集合不因成本变化而改变；在阈值处累计所有并列最优， + 不把不同但同优的排程计作失败。量子采样不会必然覆盖所有同优方案。 +- 交付一张成本曲线、一张七行决策表和有限原始记录，纳入现有 tradeoff 演示 + 与教程导航；不增加一套平行的产品流程，不做 Web 或交互界面。 +- 验收：七组成本和阈值经全枚举核对；21 次样本来源与 gap 可查；固定环境二次 + 重放除计时外一致。即使 QAOA 某次未采到最优，也如实保留并解释。 + +价值是说明权重怎样表达业务选择，不是证明量子加速。成本单位仍为用户定义的 +非负整数偏好分，不把合成权重当作已调查的货币价格或真实协调成本。 + +### P0：审阅与提交完整性，9 月 24–28 日完成 + +1. **9/24 功能冻结**：敏感性分析完成即冻结新功能。之后仅处理正确性、安装、 + 文档矛盾或维护者指出的具体问题；修改哪一层，就补哪一层的必要验证。 +2. **9/24–25 审阅收敛**：更新现有 REVIEW_GUIDE.md 与 PR 正文，形成一条主要 + 路线：成本取舍 → 权重阈值 → 18→12 资源边界 → 证书/失败。 + 清楚区分源码、测试与生成证据；维持既有历史指纹，不重写旧结果。 + 用已有资料合并重复说明，不另加一串评审文档。不重写已发布 Git 历史。 +3. **9/25 发布候选验收**：从干净 checkout 按教程建立项目 `.venv`、安装 wheel， + 运行主路线与相关测试。记录源码提交、输入哈希、依赖、命令、退出码和实际时间。 + 本机演示目标为安装完成后 5 分钟内跑完；安装下载耗时单列，不作跨机器承诺。 + 继续以已锁定的 Python 3.12 为参赛基准，其他版本不未经实测宣称兼容。 +4. **9/26–27 反馈窗口**：再次检查 PR 的实际评论、评审和上游 Actions；若有反馈， + 优先修复。没有反馈时保持候选版稳定，不靠反复改动制造进展。 + 计划本身不创建自动轮询任务，也不代用户向维护者发送催审消息。 +5. **9/28 最终交付**：核对 PR 标题/队伍 youshen/目标 develop/提交指纹/CI， + 准备简短的 3–5 分钟讲解提纲与可复制命令，更新 SUBMISSION_CHECKLIST.md。 + 主演示失败时展示实际报告、失败原因和独立证书，不用经典解替换量子结果。 + +报名已经由用户确认，作品提交回执尚未确认。需按实际平台表单核对是否还要 +填写 PR 链接、作品说明或附件；只对实际存在的字段准备材料，不虚构额外规则。 +平台最终提交仍是单独的外部操作，先备妥可审阅内容，再按用户授权处理。 +[赛题 #13](https://github.com/OriginQ/pyqpanda-algorithm/issues/13)当前还说明 PR 合并后 +可通过本源量子云社区联系官方小助手领取对应激励；这是合并后的条件性步骤， +不能把 PR 已创建当作已合并或已获奖。 + +### P1：真实业务验证，依赖外部可用条件 + +如果能由用户协调到一名实验室预约管理者或实际使用者，安排一次约 15–20 分钟 +的试用，优先使用 3–5 条经许可的匿名预约，或让对方审阅现有合成案例。 +只记录其确认过的约束定义、成本权重解释、输入困难和结果可用性；注明试用日期、 +范围和样本量，不索取身份信息，不把单人反馈推广成一般有效性。 +没有可用真实数据或试用者时，保留“全部合成数据”的边界，不阻塞 9/28 交付。 +由用户协调对外接触；本计划不授权自动发信、联系实验室或发布访谈内容。 + +### P2：赛后候选项与停止条件 + +支配剪枝、噪声模拟、新混合器、更大通用调度、额外 Web/云服务均留到赛后评估。 +暂不另开独立代码贡献 PR：只有发现可复现、影响本应用、没有同题开放 PR 的 +上游缺陷时，才重新做去重、最小复现和贡献范围判断。 + +敏感性分析若超出一天或迫使修改核心求解语义，缩减为独立经典阈值认证加现有 +真实量子示例，明确两者来源；不牺牲当前可运行版本。 +不追加新的大规模参数搜索来寻找漂亮结果，不为凑测试数扩写低价值断言。 +9/28 起至 9/30 保留缓冲用于必要修复和实际提交核验；没有外部反馈或新缺陷时， +以已验证发布候选完成参赛准备。 + +## 第六轮完成：成本解释与发布候选（2026-09-21） + +按后续规划提前完成限定增强：`5ebcdc3` 冻结权重与参数，`51a5067` 实现现有 +tradeoff 演示的 `--sensitivity`。七个权重、三种子共 21 次真实量子求解均采到 +认证最优，w=3 处两方案同优，完整九个可行排程和所有原始记录保留并成功重放。 +默认行为、核心代码和旧归档不变。108 项应用测试通过，核心覆盖率 98.92%。 + +已提前完成干净 worktree + 全新项目 `.venv` + wheel 安装验证；108 测试通过， +主演示和资源边界共约 9.38 s，加上两个失败状态共约 13.45 s，下载依赖另计。 +REVIEW_GUIDE.md 现在给出主要演示顺序，成本曲线与七行表并入已有业务教程。 +进入新功能冻结阶段：后续优先处理实际缺陷、维护者反馈及提交核验。 +真实试用仍需外部条件；平台实际作品提交与回执未操作、不标为完成。 + +本轮已发布至 PR #92，`3846495` 的个人 fork CI `35600289671` 全部成功， +108 测试、98.92% 覆盖率和全部 CPU 演示通过。官方 `35600294948` 仍待维护者 +批准，PR 保持 Open、可合并、无评论或评审；末次提交仅补录发布记录。 + +## 奖励目标下的下一步决策（2026-09-21 再核查） + +决策：保留共享实验室重排主线，继续冻结新功能；下一项工程工作优先做安装 +兼容性核查与合并准备，暂不开第二个参赛项目或独立代码贡献 PR。 + +### 本次核查事实 + +- [官网](https://qcloud.originqc.com.cn/learning/zh/2026ccf)列出的开源创新赛道 + 奖励为 10 队、每队 1,000 元及价值 1.8 万元机时和证书。 + [任务 #13](https://github.com/OriginQ/pyqpanda-algorithm/issues/13)说明维护者 + 审核与合并流程；没有给出按 PR 数量累加奖金的规则，不据此承诺奖金或名次。 +- 当前 71 个开放 PR。QUBO 二进制规范化 #69、默认层数 #45、QAOA 风险目标 #40、 + 示例及导出修复 #39 已有提交，不能作为新的同题贡献。 +- 我方 #92 仍 Open、可合并、没有评论或评审；个人 fork CI 成功，官方运行仍 + `action_required`。维护者是否批准运行、审核及合并属于外部待办。 +- 当前 PR 为 193 文件、50,618 新增行。其中实验归档占 33,830 行，核心代码 + 11 文件共 1,751 行。新增功能会继续增加审阅范围，应优先保持证据可定位。 +- 已验证 WSL / Ubuntu Python 3.12；尚未完成 3.11、3.13 安装与运行验证。 + 当前数据全部合成，512 shots 下均匀随机对照也达到全部最优，不能声称量子优势。 + +### 下一项工作的范围和验收标准 + +1. **安装兼容性核查（最多一个工作日）**：先核对 PyQPanda3 和锁定依赖的 + Python 版本与 wheel 可用性，再选择确实可安装的 3.11 / 3.13 环境验证。 + 所有环境使用项目内 `.venv`；至少验证 wheel 导入、主要演示与失败退出码， + 可用环境运行相关测试。不为通过矩阵而大改上游依赖；不可用组合明确记录原因， + 不把包元数据或安装成功当成运行测试通过。保留已通过的 3.12 推荐路线。 +2. **合并准备**：保持现有 REVIEW_GUIDE 核心代码路线，检查 PR 描述能否让维护者 + 快速定位实现、数学证明、回归与生成数据；不擅自删除历史证据或改写实验结果。 + 有评审意见时优先响应；平台若另有作品链接或回执要求，按实际要求核验。 +3. **真实使用证据（外部条件具备时）**：沿用前述一次匿名小样本试用计划， + 由用户协调联系，未发生的试用不写成结果。不通过继续增加合成种子替代此证据。 +4. **独立代码贡献的启动条件**：限于实际使用暴露的共享 QUBO / QAOA 缺陷； + 先具备最小复现、去重和影响说明，再决定单独修复。补丁须有修复前失败、修复后 + 通过的回归与原接口兼容验证。没有新缺陷就停止，不为增加 PR 数量另造模块。 + 多类或多项作品如何计奖尚未确认，不预设第二个 PR 带来第二份奖励。 + +本节是下一步执行计划，不代表兼容性扩展或真实试用已经完成；既有 108 项应用 +测试与 CI 结论保持原验证环境和提交范围，不因计划扩展而扩大支持声明。 + +## 第七轮完成：兼容性核查与合并准备（2026-09-21） + +新增两个干净 wheel 环境，各通过 108 项应用测试、18 项原有算法测试、核心 +覆盖率 98.92% 及主要 CPU 演示、失败状态。3.11 原锁安装失败，限定调整四项 +依赖版本后通过；3.13 直接沿用原 3.12 锁。实际安装版本与固定文件逐项核对， +安装的核心文件与源码逐字节核对。覆盖率按包名收集失败也如实归档，改用安装 +目录验证通过，未把工具链行为扩大为上游算法缺陷。 + +新增 COMPATIBILITY.md 与小型机器验证记录，主教程提供准确的版本选择；CI +扩展三版本、原有算法回归和失败状态；PR 开头标出核心代码/测试审阅范围。 +本轮没有发现需要独立算法修复 PR 的新证据，不另开代码贡献，不改核心实现。 +发布后继续核对远程矩阵；实际试用、平台回执及官方审核仍属外部待办。 + +用户另询问未获奖能否取得证明：公开规则仅明确 10 队的获奖证书，没有查到 +普发参赛证明的承诺。需官方确认，不能把 GitHub 贡献记录等同于官方证书， +也不因未查到说明就断言未获奖者绝无证明。 + +发布提交 `5678e41` 的 fork CI `35604218576` 已三版本全部成功（每项 108 应用 +测试、18 原有测试、98.92% 覆盖率和全部演示）。官方 `35604226057` 仍待批准。 +PR #92 的兼容性与审阅说明已更新；后续继续保持功能冻结，优先响应实际评审。 + +## 第八轮：最终实质审查与停止条件（2026-09-21） + +用户授权继续自主优化。重新审查输入、剪枝、求解、原始认证与 CLI 的交界， +发现 2049+ 原始候选即使已剪枝到 2 比特，报告也会被 MILP 的预算异常中断。 +`1a54465` 修复报告层预算预检，直接 MILP API 的上限保持不变。新增五项回归 +先确认四失败、一通过，再确认全部通过；完整应用测试 113 项、覆盖率 98.92%。 +对 simple、medium、infeasible、tradeoff、support-chain 的原有路径重放， +全部非计时报告字段与修复前一致，未更改量子参数或以经典答案填充样本。 + +把答辩的六个关键问题并入既有 REVIEW_GUIDE；更新历史竞争力审查的完成状态， +明确保留真实用户验证和量子优势两项证据边界，不编造外部使用反馈。 +后续完成新 wheel 与三版本 CI 后即结束本轮开发，不无限增加功能或另造参赛项目。 +新的工作应由实际缺陷、维护者评审或真实使用反馈触发；审核与奖项不是本地可完成项。 + +最终新 wheel 在干净环境通过 113 应用测试、18 原有测试及完整演示。 +发布提交 `e89c78b` 的 fork CI `35606032579` 三版本全部通过（各 113/18 测试、 +98.92% 覆盖率及全部演示）；官方 `35606039667` 待批准。PR #92 已更新。 +本轮工程收敛条件已满足，保留后续评审和实际提交核验缓冲。 + +## 评委首次阅读与重放入口核查（2026-09-22） + +重新检查 PR #92：仍开放、可合并，无新评论或评审；官方 CI 仍待批准。 +未发现新的算法缺陷。本轮聚焦两项交付问题：主教程首先呈现历史基线，未突出 +完整业务案例；历史重放虽有指纹，却缺少对应检出命令。主教程改用已有取舍图与 +证据导航，审阅指南区分当前两次运行和历史归档核验，给出匹配提交及独立环境步骤。 + +逐项核验三个归档版本的 17/14/17 项源码指纹；当前版本主演示实际执行两次， +每次 21 条敏感性记录，严格比较除计时外全部一致。文档本地链接和 diff 检查通过。 +未修改核心实现、实验输入、协议、历史结果或测试；不重跑并重复计数已有的 +113 项应用测试。继续保持功能冻结，后续投入优先考虑真实试用反馈和维护者评审。 + +## 小范围试用材料准备（2026-09-22） + +用户批准准备试用，并明确暂时没有可协调的使用者，先备好材料。本轮复用现有 +CSV 导入、JSON CLI 与原始约束检查接口，新增 USER_TRIAL.md 和空白反馈模板， +不新增求解模块。流程覆盖案例适配、去标识录入、查看结果前记录人工排法与权重、 +保留首次失败、参与者核验及公开范围；真实试用状态保持未开展。 + +文档四条命令以合成模板逐条执行成功，得到目标值 6、零违反、gap 0、4 比特 +组件和三项传播固定任务。重复初始化/转换会拒绝覆盖，原文件指纹保持一致。 +本地演练及空白反馈位于 Git 忽略的 reports/user-trial；没有收集、联系或公开 +真实参与者信息。后续由用户协调使用者后，再按同一流程记录实际反馈。 + + +## 独立库边界修复与联合验证（2026-09-22） + +用户随后授权继续开展其他代码贡献，并要求自主推进有证据的优化。按此新增授权， +审查应用相关的 QAOA / QUBO 与 Grover 路径，先查重、CPU 复现，再开独立修复。 +主应用保持功能冻结；下列 PR 各自基于官方 develop，不将库改动混入应用分支。 + +| PR | 已确认的问题 | 本地验证 | +| --- | --- | --- | +| [#95](https://github.com/OriginQ/pyqpanda-algorithm/pull/95) | 单比特 XY 不守恒、奇数环缺闭合边 | 24 CPU 回归，独立分支完整 42 测试 | +| [#96](https://github.com/OriginQ/pyqpanda-algorithm/pull/96) | 稀疏编号宽度、Hamiltonian 项接口、QUBO 零系数变量丢失 | 32 CPU/解析分布回归，完整 50 测试 | +| [#97](https://github.com/OriginQ/pyqpanda-algorithm/pull/97) | QUBO 结果缺符号位及零位寄存器 | 40 编码/oracle/位数回归，完整 57 测试 | +| [#98](https://github.com/OriginQ/pyqpanda-algorithm/pull/98) | 自适应搜索漏掉已测得的同值最小解 | 16 确定性 CPU 回归，完整 34 测试 | + +所有新回归保留修复前失败记录，Python 3.11 / 3.13 聚焦检查也通过。独立临时 +checkout 合并应用与四项修复，Python 3.11.16 / 3.12.3 / 3.13.11 各通过 242 项 +测试（26.28 / 29.41 / 25.09 s），应用覆盖率均为 98.92%。这些总数包含原有 +测试与应用测试，不把不同版本运行次数当成独立用例数。临时集成提交未发布。 + +另以 1/2/3 变量和常数 -2/0/3 验证 QUBO/Grover 完整路径，9 个案例均得到 +非空、无重复且目标值正确的实际采样解。无需真实硬件、凭据或模拟器替身。 +纯算法修复不声称创新算法或量子优势，也不假定 PR 数量与奖金数量对应。 + +四个 PR 当前开放且可合并，未获得官方 CI 或维护者审核结论；应用 #92 仍无新 +评审意见。同步赛题 #13 的 `【任务类型】` 标题格式。下一步优先审查交付材料 +与源代码一致性；无复现证据时不增加功能或改动已验证的实验原件。 + +补充复查发现 NumPy int32/int64/uint64 系数可能在求和或绝对值时溢出，使寄存器 +被错误分配为 1 位。已在现有 #97 中先转换为 Python 整数再计算界，追加 8 个 +修复前全部失败的分配回归。大位数案例不执行状态向量模拟，最终联合结果如上。 + +合并交互检查：在另一独立 checkout 中，将我方版本与第三方开放 PR #45 +（`f829357`）、#69（`6f0fcb2`）、#36（`e1992fb`）合并，全部自动合并无冲突； +Python 3.12 的 264 项测试通过（37.15 s），应用覆盖率 98.92%。常数输入与默认 +层数、常数输入与 GAS 同值解、二进制幂规范化与符号位三个组合 smoke 检查通过。 +这只验证指定提交的本地兼容性；没有把他人的改动加入我方 PR，也不是替其审核。 + + +## 日历空域的独立 MILP 提前判定（2026-09-22） + +确认性能问题：原始选项已因停机日历全部不可用时，MILP 仍构造两两约束再交给 +求解器。512 项合成单候选任务产生 131328 行。新增线性扫描，以原始日历独立 +证明某任务无可用选项，直接返回 infeasible、任务、原因与 constraints=0，不导入 +编译器、约束图或量子结果。每项仍有可用选项的问题继续原有完整建模。 + +在完全相同且已核对指纹的 128/256/512 任务输入上各测三次；512 案例的局部 +调用中位耗时由约 0.323 s 到 0.000219 s，构造行数降为零。此测量只说明该类 +显然不可行输入的构造开销，不作为一般优化速度、量子优势或预注册实验结果。 + +6 项回归包含三项修复前失败的提前判定断言、真实 HiGHS 的相接边界/剩余候选， +以及 512 任务 CLI 的非零退出与持久化报告。新 wheel 隔离安装通过 119 应用与 +18 原有测试（137 passed，32.30 s，98.93%），11 个核心模块与源文件字节一致； +质量检查和主演示通过。下一步核对本轮代码提交的三版本 CI，保留历史归档及其 +固定提交的重放入口；此前的 242 项库集成记录仍对应优化前应用代码 7e4e663。 + + +继续检查同一构造路径，发现日历上界已经固定为零的变量仍生成配对行。由 +$y_i=0$ 和 $0\le y_j\le1$ 可直接推出 $y_i+y_j\le1$，故这类行冗余。 +保留所有原始变量/上界/索引，只在日历可用选项之间构造不兼容约束，独立性不变。 +512 原始候选且仅 4 个日历可用候选的可行案例，构造从 64520 行降为 4 行， +目标仍为 3，返回原始选项 [254,255]；不将局部构造改进称为量子加速。 + +追加两项实际 HiGHS 回归，修改前均因冗余行数断言失败，修改后通过；完整应用 +121 passed / 19.87 s / 98.93%。新 wheel 全新隔离安装并校验 11 模块字节， +139 项组合测试通过（121 应用 + 18 原有，26.86 s），主演示与 21 次敏感性通过。 +质量检查通过。日历空域版本 b0a92d0 的三版本 CI 已通过；本次冗余行优化的新 +提交仍须完成自己的三版本 CI，不借用上一提交的远程结果。 + +## 结束主动优化（2026-09-22） + +用户最新指示为“差不多了，你觉得够了就结束吧”。判断当前端到端应用、数学 +验证、独立经典基线、可复现实验、演示和四项聚焦库贡献已形成完整交付，停止 +主动增加功能或寻找新方向。获奖取决于官方评审，不作保证。 + +最终代码 `6c43e40` 的 fork CI 35697552146 三版本各 121 应用 + 18 原有测试 +通过，应用覆盖率 98.93%。3.11 首次上游随机 GAS 测试失败，仅重跑失败任务 +一次后通过；两轮记录完整保留，没有调整原算法或断言。当前应用加四个修复的 +Python 3.12 集成 250 项通过，27.68 s，98.93%。最后仅提交文档记录并同步 #92。 + +全部五个 PR 已发布且当前无合并冲突，未取得官方评审或合并结论。队伍 youshen +报名由用户确认；平台作品回执未核验，暂无真实试用参与者，材料及合成演练已齐备。 +后续只按维护者意见、真实缺陷或使用反馈继续，工程主动优化到此结束。 diff --git a/PR_DESCRIPTION.md b/PR_DESCRIPTION.md new file mode 100644 index 00000000..746beb24 --- /dev/null +++ b/PR_DESCRIPTION.md @@ -0,0 +1,165 @@ +# 【创新应用】新增基于QUBO的共享实验室最小扰动重排应用 + +参赛队伍：**youshen**，已完成官网报名。关联 #13；目标分支 develop。 + +## 解决的问题与应用贡献 + +设备临时停机后，如何重排实验、满足清洗/小组互斥/工序前序，同时降低原预约 +变更成本？新增完整离线应用 LabReschedule，从 JSON 或 CSV 预约窗口生成 +可追溯 QUBO，在 PyQPanda3 CPU 上求解，再由原始输入 MILP 与小规模穷举核验。 + +完整取舍示例：烘箱故障后，三个实验可整体后移（成本 12、改约三单），也可 +付费使用备用设备（成本 14、改约一单）。三项决策全部留在 8 比特组件， +没有预处理固定项；默认实际量子样本得到成本 12、零违反，随后由双经典基线认证。 +原有 CSV 连锁传播案例也保留，展示被迫改约的删除原因链。 + +建议从[审阅路线](https://github.com/youshen2025/pyqpanda-algorithm/blob/contest/qubo-scheduling-app/Tutorials/LabScheduling/REVIEW_GUIDE.md)开始，按路线定位核心代码、 +数学证明、关键测试及实验记录；数据与图表占 diff 的大部分。审阅路线还区分 +当前代码重复运行与历史归档核验，列出匹配的源码提交和独立环境操作命令。 +另提供[小范围试用流程](https://github.com/youshen2025/pyqpanda-algorithm/blob/contest/qubo-scheduling-app/Tutorials/LabScheduling/USER_TRIAL.md) +和空白反馈表；已通过合成数据演练，尚未取得真实使用者反馈。 + +首次审阅可先看 11 个核心模块与 11 个测试模块（另含 3 个检查配置），再按审阅路线抽查生成的实验 +归档。最近修复报告层的基线预算处理，量子线路与默认训练/采样参数保持不变。 + +贡献集中在可证明的资源节省与可审计求解： + +- 原始候选日历剪枝、单候选传播、严格独立冲突分量、固定成本和原变量回填； + 不启发式冻结“未直接受故障影响”的预约，不静默截断或以经典解替代量子样本。 +- 可选 AC-3 删除没有任何对方候选支持的选择，保存完整删除依据；构造的 + 四工序组件从 18 降到 12 比特，三个固定种子实际采到成本 3，全部决策仍在量子组件。 +- 在现有业务演示中增加七组改约权重，解释何时使用备用设备；21 次真实量子 + 运行与完整可行集合认证，阈值处接纳全部并列最优，不改变核心默认参数。 +- XY 线路中删去可达子空间上恒零的 one-hot 罚项，保留完整 QUBO 审计和原缩放。 + medium 的原生 CNOT 从 78 降至 54，深度 45 降至 35；相同参数分布等价。 +- 独立原始输入 MILP 不使用 QUBO 的剪枝表或冲突图；区分最优证书、不可行、 + 限时 incumbent、量子抽样失败和组件超限，记录所有失败。 +- 第一批 12 个实例 × 10 个种子保留；第二批独立冻结 36 个异构实例，每格 4 个 + 数据种子、每例 10 个优化种子，比较日历剪枝/传播/分解/相位精简各阶段。 +- 补充同总训练上限/采样预算对照，并加入化简后的均匀随机基线，区分结构化简 + 与量子分布收益；完整保留随机持平或胜出的结果，不以额外预算制造优势。 +- 输入只读一次，报告指纹绑定实际求解字节；CLI 拒绝输入/输出同文件（含链接）， + 写完临时报告再替换，避免误覆盖输入或因写入失败破坏旧报告。 +- 原始 MILP 超过候选预算时明确跳过该基线，保留量子结果和仍可用的穷举证书； + 两者都无法认证时 gap 为 null。回归覆盖 2048/2049 边界、不可行及双基线超限。 + +QUBO、XY、传播和 MILP 均来自已有理论；本贡献是应用建模、编译、验证与复现流程， +不声称发明新的通用量子算法或量子加速。题目区别于开放灌溉应用 #60； +已有调研快照和 scope 在 PLAN.md 与教程中说明。 + +## 数学与架构 + +每个候选一个二进制变量，成本为偏好加原预约变更费用。完整 QUBO 为 +`Q = sum(c_i*x_i) + A*sum_t(sum_Dt(x_i)-1)^2 + A*sum_conflicts(x_i*x_j)`。 +对非负成本取 `A=1+各任务最大存活候选成本之和`；存在可行解时，任一不可行 +位串能量高于所有可行解。`x=(I-Z)/2` 转为 Ising，成本相位按同一 A 缩放。 +W 初态与域内 XY 交换保持每任务单激发，故可只在 XY 相位中删除 one-hot 罚项。 +X/H/RX 对照仍保留完整罚项。原生 CRY 与 CNOT 分别报告，不作硬件分解计数承诺。 + +默认传播只删与必选候选冲突的选择；可选弧一致性删除与另一任务所有存活候选 +都冲突的选择。两者均保留全部可行排程；组件无跨界禁止对，回填与原可行排程一一对应， +成本等于固定偏移加组件成本。近似组件求解没有全局最优保证。 + +主要改动集中于 `pyqpanda_alg/LabScheduling/`、`example/LabScheduling/`、 +`test/LabScheduling/`、`Tutorials/LabScheduling/` 及文档索引/聚焦 CI。 +复用上游 W 态、XY 交换与成本相位组件，未修改上游算法或进行无关重构。 + +## 安装与演示 + +在仓库根目录使用 Python 3.12 虚拟环境： + +```bash +python3.12 -m venv .venv +.venv/bin/python -m pip install -c pyqpanda-algorithm/example/LabScheduling/constraints-py312.txt -e ./pyqpanda-algorithm -r pyqpanda-algorithm/example/LabScheduling/requirements-dev.txt +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg .venv/bin/python pyqpanda-algorithm/example/LabScheduling/tradeoff_demo.py --sensitivity +``` + +普通 JSON CLI： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 .venv/bin/python -m pyqpanda_alg.LabScheduling pyqpanda-algorithm/example/LabScheduling/data/medium.json --reduce --output reports/medium.json +``` + +输出到项目 reports；不需要 Key、GPU、真机、Web 或数据库。pandas/scikit-learn +补充项用于上游导入链，声明限定在应用开发依赖内。 + +Linux x86_64 上 Python 3.11 / 3.13 也已分别通过独立 wheel 安装、108 项应用 +测试、18 项原有算法测试和完整演示。3.11 安装时换用 `constraints-py311.txt`； +3.13 沿用上述约束。版本、失败记录和范围见 +[安装兼容性](https://github.com/youshen2025/pyqpanda-algorithm/blob/contest/qubo-scheduling-app/Tutorials/LabScheduling/COMPATIBILITY.md)。 + +## 测试与实测证据 + +WSL / Python 3.12.3 / PyQPanda3 0.4.1： + +- 当前 121 项应用测试通过，核心行覆盖率 98.93%；原有 18 项有效测试见下方矩阵。 +- Ruff lint/format、Mypy 11 模块通过；真实 CPUQVM 测试，无模拟器 mock。 +- 原始日历可直接证明某任务没有候选时，MILP 提前报告不可行，避免构造两两 + 约束；不调用 HiGHS，也不读取 QUBO 剪枝结果。6 项新回归包括真实边界问题和 + 512 项任务的 CLI 输出；有可用选项的问题继续建模与求解。另用 2 项真实 MILP + 回归验证固定零变量的冗余配对行可被省去，原始索引及最优值保持不变。 +- 最新 wheel 在项目内全新的 `.venv` 安装，确认 site-packages 导入 + 与核心文件哈希；121 项应用测试及 18 项原有测试通过。含敏感性分析、资源边界 + 和两个失败状态的完整命令路线验证成功，安装记录见 VALIDATION.md。 +- 原有 64/4096 态 QUBO/约束等价与 4096 态 Ising 验证保留。 +- 100 个随机小问题核对化简前后全部可行集合和回填成本，再与原始 MILP 比较。 +- 可选弧一致性在 200 个随机小问题及既有 V2 全部 36 个输入上核对全部原始 + 可行集合与成本；5 个 V2 实例的最大组件缩小，其他 31 个没有该项收益。 + 18→12 边界案例的三种子与结构审计完整重放；保留局部有支持但全局不可行反例。 + 命令加 `--reduce --pruning arc`；证明和归档见 `Tutorials/LabScheduling/ARC_PRUNING.md`。 +- 改约成本敏感性协议先以 `5ebcdc3` 冻结：w=0–6、seed 7/19/42，共 21 次真实 + QAOA 均采到经独立认证的最优；阈值 w=3 时两个方案同为 15，完整重放一致。 + 这是已知合成案例上的业务解释，不是新留出集或量子优势证明。 +- simple/medium 各 30 组 p=1/2/3 随机角度验证 XY 相位精简；单候选混合域另测。 +- 12 个冻结评价实例含 9 个可行、3 个不可行，穷举和 MILP 结论全部一致。 + 可行实例 86/90 个训练种子的单次最优概率高于均匀 one-hot，4 个退化，全部保留。 + 原始/精简线路在相同已训练参数上的最大概率差为 2.23e-16 以下。 +- 120 次训练再次复现，除耗时外参数、counts、分布指标和独立采样结果完全一致。 +- 20/35/50 任务的合成链仅运行经典 MILP，目标 40/71/100；明确标记未执行量子求解。 +- 第二批支持 2–4 候选、1–3 时隙时长、0–2 清洗时间和三类故障；36 个原排程 + 均验证无故障时合法。故障后 24 可行、12 不可行，全部原始可行集合与回填成本一致。 +- 第二批 240 个可行训练种子有 206 个最优概率高于均匀、34 个退化；一个实例连 + 种子均值也退化，均保留。分解可能增加总训练/采样预算，不作同预算优势声明。 +- 360 条配对求解记录完整重放，除耗时外完全一致；其中纯经典传播明确标注。 +- 同总预算补充评价重新运行全部 360 组，并完整重放。22 个仍需量子的可行实例 + × 十种子中，分量 QAOA 最优 220/220、直接 QAOA 217/220；两条随机基线也为 + 220/220，不宣称 512 shots 下量子优于随机。低 shots 条件曲线及退化实例见 + `Tutorials/LabScheduling/MATCHED_BUDGET.md`，曲线不包括训练成本。 + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 .venv/bin/python -m pytest -c test/LabScheduling/pytest.ini test/LabScheduling --cov=pyqpanda-algorithm/pyqpanda_alg/LabScheduling --cov-fail-under=95 +``` + +证明、接口、协议、全部失败记录、资源与命中曲线见 +`Tutorials/LabScheduling/ENHANCEMENTS.md`；安装及 CI 实际状态见 VALIDATION.md。 +旧 schema 1 开发基线独立保留，新版为 schema 2，默认使用精简 XY。 + +个人 fork 的 [Ubuntu / Python 3.11–3.13 CI](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35697552146) +在代码提交 `6c43e4059c07516f5cd1df5d912f417faa37bdeb` 上最终全部通过：各全新安装、 +质量检查、121 项应用测试（98.93% 覆盖率）、18 项原有测试及全部 CPU 演示。 +3.12 / 3.13 首次通过；3.11 首次应用测试通过，但未修改的上游 GAS 随机搜索 +测试失败（17 通过、1 失败），仅重跑该失败矩阵任务一次后全部通过。保留首次 +失败日志，未修改原测试或算法来规避失败；完整记录见 VALIDATION.md。 +官方 [35697558446](https://github.com/OriginQ/pyqpanda-algorithm/actions/runs/35697558446) +仍待上游批准；fork CI 不代替上游审批。后续收尾提交仅更新验证文档。 + +## 限制 + +每个量子组件最多 16 比特，总编译候选最多 256；精确枚举最多一百万组合， +MILP 最多 2048 个原始候选。报告分别记录超预算跳过状态，限时或无证书不冒充最优。 +只处理非负整数成本、确定时长、单位容量、单小组任务。训练与概率诊断为 +指数复杂度的无噪声模拟；训练后的 shots 命中曲线不是端到端加速。 +第一批每格一个数据种子，第二批每格四个，仍是小规模合成证据，没有实际实验室用户验证。 +经典基线在此规模更快，量子效果并非每个种子改善，硬件噪声下不保证相位精简等价。 + + +## 独立库兼容验证 + +另行修复的 #95/#96/#97/#98 分别处理 XY 边界、QAOA 寄存器、QUBO 符号位和 +GAS 同值解；这些改动不包含在本应用 PR 中，本应用也不依赖它们才能运行。 +2026-09-22 在独立 checkout 合并应用代码 `7e4e663` 与上述修复，Python 3.11 / 3.12 / +3.13 各通过 242 项测试，应用覆盖率均为 98.92%。这是本地兼容性证据， +没有扩大原 fork CI 的覆盖范围或宣称官方审核通过。 + +上述集成随后加入当前两项 MILP 构造优化，在 Python 3.12 上通过 250 项测试， +应用覆盖率 98.93%；三版本的 242 项历史记录仍对应先前明确标注的应用提交。 diff --git a/README.md b/README.md index a12fbe0d..9ba30c51 100644 --- a/README.md +++ b/README.md @@ -154,3 +154,7 @@ pyqpanda_alg采用Python作为主要语言，对系统的环境要求如下：

本源量子官方小助手

+ +## 应用案例 + +- [LabReschedule：基于 QUBO 的共享实验室最小扰动重排](Tutorials/LabScheduling/README.md) diff --git a/SUBMISSION_CHECKLIST.md b/SUBMISSION_CHECKLIST.md new file mode 100644 index 00000000..8d8c692a --- /dev/null +++ b/SUBMISSION_CHECKLIST.md @@ -0,0 +1,240 @@ +# 本源杯提交检查表 + +交付日期：2026-09-21；用户目标 9 月 28 日前备妥，比赛截止 9 月 30 日。 +当前工作分支：`contest/qubo-scheduling-app`；目标：`upstream/develop`。 + +## 已完成的本地工作 + +- [x] 核对比赛主页、赛题 #13、README、main/develop 贡献指南。 +- [x] 逐页检查开放 PR、开放 Issues、develop 源码；记录 70 项开放 PR 快照。 +- [x] 区分最近的灌溉应用 #60，采用共享实验室故障重排题目。 +- [x] PLAN.md 写明范围、数学风险和实施结果。 +- [x] 项目内 `.venv`，Python 3.12.3；无全局依赖安装。 +- [x] 固定依赖版本、种子、线程设置；无 Key、Token 或真实硬件要求。 +- [x] JSON 输入、严格错误处理、简单/中等/不可行三个数据集。 +- [x] QUBO 完整公式、变量映射、惩罚系数证明、Ising 转换及位序说明。 +- [x] PyQPanda3 CPUQVM 实际运行，复用 pyqpanda_alg 线路组件。 +- [x] 独立精确基线、均匀 one-hot 对照、XY/X 对照；量子结果只来自采样。 +- [x] 输出目标、原始成本、违反数/原因、耗时、采样证据及最优差距。 +- [x] 当前 121 项应用测试通过；覆盖率 98.93%；原有 18 有效测试的验证见各轮记录。 +- [x] Ruff、格式、Mypy、pip check；wheel 构建及安装目录运行成功。 +- [x] 中文评委教程、几分钟演示流程、12 组实验 JSON/CSV 和静态 SVG。 +- [x] 提供 PR 标题、完整描述、develop 目标和聚焦 CI。 +- [x] 按建模、量子应用、文档证据整理本地提交；工作区最终保持干净。 +- [x] Git diff/相对链接/公开 API/缓存/秘密/旧版 API 检查。 + +## 发布进度 + +- [x] 用户已审阅并明确批准 push/创建 PR（2026-09-21）。 +- [x] 用户已确认官网报名完成，队伍名称 **youshen**（2026-09-21）。 +- [x] 发布准备时再次刷新开放 PR 和 upstream/develop；与初始快照一致。 +- [x] 个人 fork `youshen2025/pyqpanda-algorithm` 已创建，`origin` 已配置为其 HTTPS 地址。 +- [x] upstream/develop 仍为 `5f973ef`，无需合并，代码和测试结论不变。 +- [x] 推送功能分支，通过个人 fork 向官方 develop 创建 [PR #92](https://github.com/OriginQ/pyqpanda-algorithm/pull/92)。 +- [x] 使用 `PR_DESCRIPTION.md` 第一行标题及其正文，关联 #13，队伍名称 youshen。 +- [x] 增强版本已推送，PR 描述已更新；核对 PR 为 Open、非草稿、目标 develop，GitHub 报告可合并。 +- [ ] 远程 CI 通过：当前工作流等待上游维护者批准运行，尚未执行测试。 +- [ ] 按比赛平台要求补充队伍/PR 链接并保存提交回执，不以本地提交代替报名。 +- [x] 9 月 28 日前完成 PR 准备/提交：实际于 2026-09-21 创建 PR #92。 +- [ ] 最迟 9 月 30 日前完成规定平台流程并核对提交回执。 + +分支已推送至个人 fork，再由 PR 合入官方仓库： + +```bash +# 2026-09-21 经用户批准已执行。 +git push -u origin contest/qubo-scheduling-app +``` + +PR 标题：`【创新应用】新增基于QUBO的共享实验室最小扰动重排应用`。 +审阅入口：[中文教程](Tutorials/LabScheduling/README.md)、 +[验证报告](Tutorials/LabScheduling/VALIDATION.md)、[PR 描述](PR_DESCRIPTION.md)。 +合并、评奖和平台审核结果由主办方决定；本清单不把尚未发生的审核标为完成。 + +发布记录（2026-09-21）：通过 GitHub CLI 浏览器登录确认账号 `youshen2025`， +已成功推送分支并创建正式 PR，先前连接应用权限阻碍已不影响本地发布。 +首次 [Actions 运行](https://github.com/OriginQ/pyqpanda-algorithm/actions/runs/35572665883) +状态为 `action_required`，页面明确显示等待维护者批准，jobs 数量为 0。 +本地测试通过不等同于远程 CI 通过；后续需在 PR 页面跟进维护者批准及实际运行结果。 +用户已确认官网报名；本次没有登录比赛平台或代填 PR 链接，平台回执仍待核对。 + +## 增强版发布检查 + +- [x] 单独委派过独立竞争力评审，原建议见 AWARD_REVIEW.md。 +- [x] XY 恒零罚项删除，保持完整 QUBO 与相同缩放；真实 CPU 等价回归。 +- [x] 安全单候选传播、冲突组件、固定成本/原变量回填；100 个小问题全可行集验证。 +- [x] 原始输入 MILP，限时/界/差距状态明确；未复用 QUBO 冲突表。 +- [x] CSV 预约窗口、三单连锁改约及无故障对照、变更表和离线甘特图。 +- [x] 冻结 12 个实例 × 10 优化种子，归档所有 120 次训练及退化结果。 +- [x] 20/35/50 任务仅经典扩展检查，明确未执行量子求解。 +- [x] 重写本地 PR 描述，新增增强教程，区分历史 schema 1 与当前 schema 2。 +- [x] 增强版 wheel 在项目内全新虚拟环境安装，71 测试、业务演示和 medium CLI 成功。 +- [x] 用户审阅增强版结果后批准继续（2026-09-21）；增强提交与完整 PR 描述已发布到 PR #92。 +- [ ] 增强版推送后核实上游 Actions 实际运行结果及维护者意见。 + +增强代码发布提交为 `9c23ddb`（实现 `22fbbd1`、实验 `8e55c17`、文档 `9c23ddb`）。 +对应 [Actions 运行](https://github.com/OriginQ/pyqpanda-algorithm/actions/runs/35576581365) +仍为 `action_required`，jobs 数量为 0，等待上游维护者批准；不能标为 CI 通过。 +后续提交仅同步这次发布记录。比赛平台 PR 链接与提交回执仍待核对。 + +官网开源创新赛道目前列有 10 个获奖名额，没有列出“特等奖”。本作品以提升 +本赛道竞争力为目标，不把其他赛道奖项或未经验证的量子优势作为参赛承诺。 + +## 第二轮优化检查 + +- [x] 用户批准聚焦原作品的评测、业务案例、逐步对照和审阅体验优化。 +- [x] 36 个异构输入在评测前冻结，旧版 120 次结果保留不变。 +- [x] 360 条配对记录、全部失败/退化/纯经典情形公开，并完整重放核对。 +- [x] 8 比特业务案例保留三项决策，9 个可行取舍公开，实际样本与独立证书一致。 +- [x] 76 测试、98.44% 覆盖率、质量检查；纯 wheel 环境运行新增测试与演示成功。 +- [x] 新增 REVIEW_GUIDE.md，缩短评委与维护者定位核心贡献的路径。 +- [x] 个人 fork 独立 Ubuntu CI 实际通过：[运行 35583744393](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35583744393)，提交 `198ddfb`。 +- [x] 第二轮优化已推送至 PR #92，PR 描述更新，GitHub 报告可合并。 + +fork CI 的安装、质量检查、76 项 CPU 测试和三类演示全部通过。 +上游运行 `35583752436` 仍等待维护者批准；平台 PR 链接与回执仍待核对。 +后续仅补录本发布记录，保留被验证代码不变。 + +## 第三轮：公平预算与随机结构对照 + +- [x] 运行前冻结同总预算协议 `eb9c5c3`；明确沿用已知数据，不冒充新盲测。 +- [x] 两量子路径同总评价上限 120、总 shots 512；不足时显式停止。 +- [x] 加入直接/分量均匀随机对照，拆分经典结构与量子分布收益。 +- [x] 全部 360 条记录完整重放，除计时外一致；旧基准与核心实现未修改。 +- [x] 如实披露随机同样达到 220/220，低 shots 理论曲线不含训练成本。 +- [x] 82 项应用测试、98.44% 核心覆盖率、Ruff/Mypy/pip check 通过。 +- [x] 新增可执行重放校验及同总预算分析，旧结论按原预算保留。 +- [x] 本轮已推送并更新 PR #92；`a3716ed` 的个人 fork + [CI 35586823475](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35586823475) + 全部通过（82 测试和三类演示）。官方 CI 仍待维护者批准。 + +## 第四轮：输入与输出完整性 + +- [x] 临时副本复现并修复同文件输出覆盖输入、求解后哈希错配问题。 +- [x] 同路径/符号链接/硬链接输出求解前拒绝，完整临时写入后替换报告。 +- [x] 输入快照供解析与哈希共同使用，保留严格 JSON 验证。 +- [x] 十项针对性回归覆盖错误路径、CRLF 字节指纹、外部修改/删除、写入与替换失败。 +- [x] 本地 92 测试、98.87% 核心覆盖率；原有 18 测试、Ruff/Mypy/pip check 通过。 +- [x] 最新 wheel 构建与隔离环境重新安装，92 测试通过，完整取舍演示成功。 +- [x] 修复已发布至 PR #92；`36adc9f` 的个人 fork + [CI 35588841891](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35588841891) + 全部通过，92 测试、98.87% 覆盖率和三类演示成功。上游 CI 仍待批准。 + +## 第五轮：可选无支持候选化简 + +- [x] 评价前以 `086840d` 记录范围与专门构造的资源边界数据。 +- [x] 可选 AC-3、完整删除证据、原可行集合/成本保持证明；默认路径兼容。 +- [x] 200 个小问题及全部 V2 输入验证；18→12 比特案例的三种子实际运行与认证。 +- [x] 保留无收益实例、纯经典传播和局部有支持但全局不可行反例。 +- [x] 37 个输入完整审计及实际量子记录二次重放，非计时字段一致。 +- [x] 102 应用测试、98.92% 核心覆盖率、18 上游测试、Ruff/Mypy/pip check 通过。 +- [x] 最新 wheel 在项目内隔离环境完整 102 测试通过，新增聚焦 CI 示例。 +- [x] 优化已更新 PR #92，`4eb272a` 的个人 fork + [CI 35597416071](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35597416071) + 全部通过（102 测试、98.92% 覆盖率和四类 CPU 示例）。上游 CI 仍待维护者批准。 + +## 第六轮：业务成本解释与发布候选 + +- [x] 权重与参数执行前冻结；七个权重 × 三种子，21 次真实 CPU 求解及完整重放。 +- [x] 全部九个原始可行排程跨权重不变，阈值处两方案同优并正确计分。 +- [x] 明确合成成本为偏好分；保留非最优和无可行样本的诊断分支及测试。 +- [x] 七行决策表、成本曲线、派生输入及完整量子记录；接入现有演示可选参数。 +- [x] 108 项应用测试、98.92% 核心覆盖率，Ruff/Mypy/pip check 通过。 +- [x] 干净 worktree 新建 `.venv` 安装 wheel，108 测试通过，主演示与失败状态均验证。 +- [x] 收敛现有审阅路线和讲解顺序，安装后本机全路线约 13.45 s。 +- [x] 本轮已发布至 PR #92；`3846495` 的个人 fork + [CI 35600289671](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35600289671) + 全部通过：108 测试、98.92% 覆盖率及包含权重敏感性的全部 CPU 演示。 + 官方 CI 仍待维护者批准，平台作品提交回执仍待核对。 + +## 第七轮：安装兼容性与合并准备 + +- [x] Python 3.11 / 3.13 各在干净 checkout 的项目 `.venv` 安装 wheel。 +- [x] 两版本各通过 108 项应用测试、18 项原有测试，核心覆盖率均为 98.92%。 +- [x] 核对 site-packages 导入、核心文件字节、固定依赖版本和 pip check。 +- [x] 主要演示、21 次权重敏感性及超限/不可行退出码验证通过。 +- [x] 3.11 使用独立约束文件，仅调整四项不兼容依赖；3.12 原环境保留。 +- [x] 原锁安装失败与覆盖率收集失败保留说明，不抹去失败记录。 +- [x] 扩展三版本 CI 和上游回归/失败状态检查，补充明确的安装兼容性入口。 +- [x] 发布提交 `5678e41` 的个人 fork [CI 35604218576](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35604218576) + 三版本全部通过；各 108 应用测试、18 原有测试、98.92% 覆盖率及全部演示。 +- [ ] 向官方确认：未入选获奖队伍的有效提交是否提供参赛或贡献证明。 + +最后一项只记录待确认问题，本轮未代用户联系官方，不预设证书保底。 +本轮官方 CI `35604226057` 仍待批准，个人 fork 通过不等同于官方审核通过。 + +## 第八轮：实质边界修复与最终审查 + +- [x] 复现原始候选过多但量子模型很小时，MILP 预算异常丢弃整个报告的问题。 +- [x] 保留实际结果，分别记录认证预算；无最优证书则 gap 为 null。 +- [x] 新增五项回归，先确认四失败、一通过，修复后全部通过。 +- [x] 完整 113 项应用测试、98.92% 覆盖率、Ruff/Mypy 通过。 +- [x] 五个既有端到端报告除计时外与修复前一致，历史实验原件保留。 +- [x] 答辩六问并入原审阅路线，更新竞争力评审的完成状态和残余限制。 +- [x] 新 wheel 干净安装：113 应用测试、18 原有测试、98.92% 覆盖率及完整演示通过。 +- [x] `e89c78b` 的个人 fork [CI 35606032579](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35606032579) + 三版本各 113 应用测试、18 原有测试、98.92% 覆盖率及全部演示通过。 + +本轮完成后结束主动功能扩张；只根据实际缺陷、真实反馈或维护者意见修改代码。 +本轮官方 CI `35606039667` 仍待批准，实际作品提交回执及证书规则仍待外部确认。 + +## 评委入口核查（2026-09-22） + +- [x] 主教程优先展示完整业务案例，保留历史基线的明确链接。 +- [x] 明确当前两次运行与历史归档核验的区别，提供对应提交和独立环境命令。 +- [x] 当前版本两次各 21 条敏感性记录严格重放通过；三个历史版本 48 项指纹匹配。 +- [x] 文档本地链接、diff 检查通过；核心代码和已验证测试范围保持不变。 + +## 试用准备（2026-09-22） + +- [x] 试用操作说明、现有 CSV 模板入口及空白反馈表准备完成。 +- [x] 文档全部命令以合成数据演练通过；重复初始化与导入拒绝覆盖原记录。 +- [x] 记录用户当前暂无参与者；材料与演练不计为真实试用证据。 +- [ ] 获得真实使用者确认的数据与反馈，核查模型适配及人工排法的可比性。 +- [ ] 取得明确的公开范围后，整理相应去标识证据；目前无真实材料可发布。 + + +## 独立库贡献检查（2026-09-22） + +- [x] 用户已授权继续其他代码贡献；先查重与复现，每项独立分支、聚焦提交。 +- [x] 发布 [XY 边界 #95](https://github.com/OriginQ/pyqpanda-algorithm/pull/95)、 + [QAOA 寄存器 #96](https://github.com/OriginQ/pyqpanda-algorithm/pull/96)、 + [QUBO 符号位 #97](https://github.com/OriginQ/pyqpanda-algorithm/pull/97)、 + [GAS 同值解 #98](https://github.com/OriginQ/pyqpanda-algorithm/pull/98)。 +- [x] 说明复现输入、行为变化、数学语义、实际 CPU 回归及已知限制。 +- [x] Python 3.11 / 3.12 / 3.13 联合集成各 242 测试通过，应用覆盖率 98.92%。 +- [x] 应用和库修复保持独立，临时集成没有混入参赛应用分支。 +- [x] 四个库 PR 写明队伍 youshen、任务 #13 和目标 develop；标题类型符合赛题。 +- [ ] 等待上述 PR 的维护者评审、官方 CI 与合并，不将本地通过当作官方通过。 + +这些贡献不替代应用的真实试用、平台提交回执或官方奖项判定。 + + +## 日历不可行输入的构造预算优化（2026-09-22） + +- [x] 在独立 MILP 路径从原始选项证明日历空域，保留原因、任务和原始规模。 +- [x] 6 项回归覆盖清洗/停机边界、剩余可用选项和 512 任务 CLI 报告。 +- [x] 新 wheel 隔离安装，11 个核心模块字节一致；119 应用 + 18 原有测试通过， + 应用覆盖率 98.93%；Ruff/format/Mypy/pip check 通过。 +- [x] 实际中等示例、成本取舍与 21 次敏感性、资源边界及不可行退出检查通过。 +- [x] 日历空域版本 `b0a92d0` 的 fork [CI 35696834868](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35696834868) + 三版本各 119 应用 + 18 上游测试、98.93% 覆盖率及全部演示通过；官方仍待批准。 + + +## 固定零变量的冗余约束消除（2026-09-22） + +- [x] 仅省去日历上界为零变量的配对行，保留全部原始变量、目标和选项编号。 +- [x] 2 项新回归修复前失败；完整 121 应用测试通过，覆盖率 98.93%。 +- [x] 新 wheel 隔离安装，139 项组合测试通过（121 应用 + 18 原有），主演示及 + 21 次敏感性运行通过；Ruff/format/Mypy/pip check 通过。 +- [x] 代码 `6c43e40` 的三版本 [CI 35697552146](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35697552146) + 最终各 121 应用 + 18 原有测试通过，覆盖率 98.93%；3.11 上游随机 GAS 测试 + 首次失败、失败任务重跑一次通过，两轮证据保留于 VALIDATION.md。 +- [x] 当前应用与四个独立修复的 Python 3.12 临时集成 250 项通过，未混入应用 PR。 + +## 收尾状态（2026-09-22） + +用户要求可以结束后，停止主动功能扩展，冻结当前代码；最终提交仅补齐验证记录。 +应用 #92 与四项库贡献 #95/#96/#97/#98 已发布，当前均开放且可合并。 +参赛报名已由用户确认，队伍 youshen。平台作品提交回执尚未核验；官方 CI 批准、 +维护者评审与真实试用仍按上方未完成项保留，不将这些外部事项冒充已完成。 +下一次代码改动应由实际缺陷、维护者意见或真实试用反馈驱动。 diff --git a/Tutorials/LabScheduling/ARC_PROTOCOL.md b/Tutorials/LabScheduling/ARC_PROTOCOL.md new file mode 100644 index 00000000..02cbd178 --- /dev/null +++ b/Tutorials/LabScheduling/ARC_PROTOCOL.md @@ -0,0 +1,23 @@ +# 无支持候选化简：评价前范围记录 + +2026-09-21，队伍 youshen。本文件在首次运行新增模式评价前提交。 +这是既有应用的可选增强，不是新的调度场景或原创 AC-3 算法。 + +- 默认 `singleton` 路径保持不变；新增 `--reduce --pruning arc`。 +- 规则：若某候选与另一任务当前域内每一个候选都冲突，则它不可能出现在 + 任何全局可行排程中。用确定性 AC-3 队列迭代；保存当时完整对方域和逐对原因。 +- 逐个删除保持所有原始可行排程与成本；不以局部有支持推断全局可行。 +- 结构测试使用固定随机种子 0–199，最多 5 任务，覆盖空域、停机、清洗、 + 同组和前序；枚举原始语义可行集合，再核验全部回填解及成本。 +- 回归包含：多轮支持消失、无需单候选的不可行证书、两时隙三任务的全局不可行 + 反例、完全独立任务、空问题、无收益实例、无效模式与 CLI 参数组合。 +- 新增 `support-chain.json` 是刻意构造的资源边界演示：四工序、显式候选窗口， + 末工序的截止窗口排除前面晚时段。它不属于随机留出评测，不代表平均提升。 + 对照停机前合法原预约。比较默认与 arc 的组件大小、固定项及经典最优值。 + arc 运行真实 CPU QAOA，seed 7/19/42，p=1、2 restarts、maxiter=60、512 shots； + 不根据结果调参或挑种子。超出 16 比特的默认路径只报告资源限制，不强行模拟。 +- 在既有冻结 V2 全部 36 个实例上比较两种化简的资源和可行集合；不改变旧记录， + 不称为新留出集。不重新训练 360 次来混淆已有同预算结论。 +- 保存逐删除证据、输入/源码指纹、完整实际量子 counts 与参数；二次重放比较 + 全部非计时字段。成功解必须通过独立原始输入 MILP 和实际样本来源检查。 +- 若结构收益有限，同样完整报告；不据此宣称量子加速或超过经典优化器。 diff --git a/Tutorials/LabScheduling/ARC_PRUNING.md b/Tutorials/LabScheduling/ARC_PRUNING.md new file mode 100644 index 00000000..b5592e7c --- /dev/null +++ b/Tutorials/LabScheduling/ARC_PRUNING.md @@ -0,0 +1,119 @@ +# 无支持候选删除：让一个超限组件进入可运行范围 + +保留实验室故障重排方向，新增可选 `--reduce --pruning arc`。它在经典预处理中 +剔除不可能出现在任何可行排程里的候选，再按原方式运行真实 CPU QAOA。 +默认 `singleton`、旧实验和预算结论保持原貌。 + +## 一条命令观察变化 + +按[主教程](README.md#1-三分钟演示)安装，在仓库根目录运行： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 .venv/bin/python -m pyqpanda_alg.LabScheduling pyqpanda-algorithm/example/LabScheduling/data/support-chain.json --reduce --pruning arc --output reports/support-chain.json +``` + +预期 `status="feasible"`、`absolute_gap=0`、`quantum.best.objective=3`； +`quantum.reduction.component_qubits=[12]`，固定变量为空，四项任务都由量子样本决定。 +删除 `--pruning arc` 重跑，将得到 `status="resource_limit"`、退出码 5： +原有单候选传播留下一个 18 比特组件，超过本应用配置的每组件 16 比特上限。 +报告仍保留经典认证，不能把认证解当作实际量子输出。 + +[输入](../../pyqpanda-algorithm/example/LabScheduling/data/support-chain.json)是专门构造 +的边界案例：四道按序实验共用烘箱，原预约开始于 0/2/4/6；设备在 `[0,1)` +停机，第一单必须改约。末工序候选只到时隙 6，前三道工序中的晚窗口不能接到 +末工序，逐层删除。它不是新留出集，也不代表一般实例的平均收益。 + +| 阶段 | 候选/量子组件 | 固定任务 | 全部可行排程数 | +|---|---|---:|---:| +| 原始输入 | 19 个候选，含停机无效项 | — | 15 | +| 日历剪枝 + 原单候选传播 | 单组件 18 比特 | 0 | 15 | +| 追加弧一致性 | 单组件 12 比特 | 0 | 15 | + +种子 7/19/42，p=1、每种子 2 次初始化、每次最多 60 次评价、512 shots， +三次实际 CPU 运行均得到成本 **3**、**0** 违反、**0** 最优差距；各实际评价 120 次。 +只将第一单从 0 移到 1，偏好移动成本 1 + 改约成本 2，其余三单保持原预约。 +原预约在无故障时经独立原始语义检查合法、成本 0；停机后违反一条日历约束。 +这里的最优来自实际采样，随后独立穷举与原始输入 MILP 认证，不是经典修补。 +这些固定种子的结果不构成一般 QAOA 最优保证。 + +## 规则、证明和审计字段 + +设当前存活候选域为 $D_t$，编译后的禁止候选对集合为 $E$。 +对 $i\in D_t$，若存在另一任务 $u$ 满足 + +$$ +\forall j\in D_u,\quad \{i,j\}\in E, +$$ + +则删除 $i$。任一全局可行排程必须从 $D_u$ 选一个候选，因而无法同时选 $i$。 +从原始域出发逐次应用这个论证，删除始终不移除任何原可行排程。 +该规则不依赖成本，因此保留全部可行排程及其原成本，包括所有并列最优解。 +空域给出不可行证据；否则进入原单候选固定和独立分量分解，沿用原变量回填证明。 + +实现采用 AC-3 的有向弧队列：修改 $D_t$ 后重新检查可能失去支持的相邻域。 +任务顺序、队列顺序、候选遍历及审计输出都确定；用集合去重待检查的弧。 +无禁止对的任务之间所有候选互相支持，不需要加入队列。 +典型 AC-3 最坏时间为 $O(e d^3)$（$e$ 为任务间有向弧数，$d$ 为最大候选域大小）， +本应用编译上限仍为 256 候选。它是经典预处理，计入求解器耗时。 +方法来自已有约束满足理论，参考 +[Mackworth, Consistency in Networks of Relations (1977)](https://www.cs.ubc.ca/~mack/Publications/b2hd-AI77.html)， +不将 AC-3 本身列为原创量子算法。 + +`quantum.reduction.removals` 每条新增删除含： + +- `variable`：原始编译模型中的候选索引。 +- `rule="no_support"`：与原单候选规则的 `forced_by` 区分。 +- `against_task`：没有任何支持选择的对方任务。 +- `blockers`：删除当时对方**全部**存活候选及每对的原始冲突原因。 + +例如工序 t2 的晚窗口与 t3 的三个候选都违反前序，先删 t2 的两个晚窗口； +t1 的晚窗口随后失去全部支持，再向 t0 传播。见归档中的逐条原因链。 +验证器按删除顺序重建域，拒绝缺失候选、虚构冲突原因和错误空域证明。 +原模式的 `forced_by` 字段保持不变；报告 schema 仍为 2，`method` 标明使用弧一致性。 + +Python 调用分别为 `reduce_model(model, pruning="arc")`、 +`solve_reduced(model, pruning="arc")`、`run_experiment(path, reduce=True, pruning="arc")`。 +CLI 单独传 `--pruning arc` 而没有 `--reduce` 会明确报错，不静默忽略参数。 + +## 已知语料上的结构收益 + +在既有 V2 的全部 36 个输入上核验两种模式的**全部原始语义可行集合和成本**。 +24 个可行、12 个不可行的结论保持一致；5 个可行实例的最大组件严格缩小， +其余 31 个没有最大组件收益，不隐藏这些结果。 + +| V2 实例 | 原组件比特数 | arc 组件比特数 | arc 固定任务 | +|---|---|---|---:| +| n3-s2-r1 | [7] | [5] | 1 | +| n4-s2-r1 | [9] | [] | 4 | +| n4-s2-r2 | [7] | [2] | 3 | +| n5-s2-r0 | [12] | [11] | 1 | +| n5-s2-r3 | [7] | [2] | 4 | + +`[]` 表示全部由经典传播解决，不计为量子成功。V2 本来就已满足组件上限； +新增 18→12 案例另列，不混入随机语料平均值。两种模式可能以不同顺序发现 +同一不可行问题，空域任务名称和删除链不必相同。 +此处只比较精确结构化简，没有新跑 360 次训练，也没有借用旧结果宣称 arc 的 +量子解质量更高。预处理重算组件 QUBO/罚系数，量子训练分布不要求保持一致。 + +## 复现与失败边界 + +[评价前范围记录](ARC_PROTOCOL.md)提交为 `086840d`。 +[完整证据](results/support/results.json)保存 37 个输入的指纹、两种模式及其证明记录， +还有三个种子的完整量子 counts、参数、历史和独立证书； +[二次重放](results/support/replay.json)确认除 `_seconds` 计时字段外一致。 +实现版本为 `67d07e7`；源码与环境指纹不同会使严格重放失败， +应使用该归档对应版本与锁定依赖。 + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 .venv/bin/python pyqpanda-algorithm/example/LabScheduling/support_pruning.py --output reports/support-pruning +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 .venv/bin/python pyqpanda-algorithm/example/LabScheduling/support_pruning.py --output reports/support-pruning-replay --verify-against reports/support-pruning/results.json +``` + +[回归测试](../../test/LabScheduling/support.test.py)额外核验种子 0–199 的 200 个小问题， +包括日历、清洗、小组、前序、空问题/空候选和固定成本；所有原始可行排程逐一对应。 +包含两任务各有多个候选但全部互斥的不可行证书，以及**三任务共用两个时隙**： +每个候选对任意另一任务都有支持，弧一致性不删除任何项，但整个问题不可行。 +因此局部一致性不是全局可行证书；仍必须检查真实采样结果并保留经典认证。 + +没有通用规模保证：过大的存活组件仍报资源限制。经典求解在这些规模更快； +本项贡献是保留可行集合的资源缩减、可检查的删除依据与完整端到端验证。 diff --git a/Tutorials/LabScheduling/BENCHMARK_PROTOCOL.md b/Tutorials/LabScheduling/BENCHMARK_PROTOCOL.md new file mode 100644 index 00000000..6354b414 --- /dev/null +++ b/Tutorials/LabScheduling/BENCHMARK_PROTOCOL.md @@ -0,0 +1,32 @@ +# 增强版实验协议 v1 + +冻结于 2026-09-21，首次运行评价集前制定。队伍 youshen。 +这是小规模合成探索评测，不是实际实验室数据，也不是正式统计功效研究。 +原来的 simple/medium 为已观察的开发样例；新增 development 仅用于检查脚本。 +评价集不用于选择默认参数；若运行发现实现错误，记录修复并全量重跑，不删除失败实例。 + +- 生成器 `example/LabScheduling/make_corpus.py`，NumPy PCG64。 +- 12 个评价实例：3/5/6/8 任务（6/10/12/16 存活候选）×3 个拥挤参数。 + 每格仅一个数据种子，因此不能推断整个问题分布的效果。 +- 一任务两种候选；t0 额外带一个被停机删除的原预约；所有实例保留。 +- 随机成本 0–4，变更成本 1–4；一个拥挤档含 t0→t1 前序。 + 拥挤参数只控制设备数和候选时间窗，实际冲突密度另外报告。 +- 参数固定 p=1、COBYLA 60 次/重启、2 次重启；优化种子 0–9。 + 不针对评价集调参，不用经典解初始化或修复量子解。 +- 正式量子训练使用精简 XY。原始 XY 在相同已训练参数比较概率和门数， + 不重复独立优化后混淆浮点误差带来的优化路径差异。 +- shots=8/16/32/64/128/512：对已训练分布计算解析最优命中率，另以独立种子 + 从完整 Born 分布实际采样，并记录所有预算的可行/最优命中。 +- 对照：均匀 one-hot、确定性最低成本顺序贪心（无回溯）、原始语义穷举、 + 从未经 QUBO 剪枝的原始选项独立建模的 SciPy/HiGHS MILP。 +- 每个实例报告：完整输入及 SHA-256、可行/最优组合数、基线状态、门数/深度、 + 所有训练参数/history/counts、训练评价次数和耗时、分布最优/可行概率、 + 抽样差距及失败；不可行实例的最优概率为 0，不计入可行实例胜率分母。 +- 先在实例内部汇总 10 个优化种子，再对实例等权汇总；不把 120 次训练当作 + 120 个独立调度问题。展示种子最差情况与失败，不以均值掩盖不稳定性。 +- 采样命中曲线排除训练成本，不能表示端到端量子加速。MILP/穷举耗时独立展示。 + 进程峰值 RSS 是整次评测累计峰值，不是单个线路内存，也不是硬件资源估计。 +- 安全化简独立用 100 个随机小问题枚举全部可行集合，验证一一回填和最优值。 + 分解大问题的上限是最大连通组件大小，不把互不相干组件总比特数称为模拟规模。 + +若评测量子收益不稳定，保留所有结果，将结论限定为可证明线路节省和可审计求解。 diff --git a/Tutorials/LabScheduling/BENCHMARK_V2_PROTOCOL.md b/Tutorials/LabScheduling/BENCHMARK_V2_PROTOCOL.md new file mode 100644 index 00000000..958debc4 --- /dev/null +++ b/Tutorials/LabScheduling/BENCHMARK_V2_PROTOCOL.md @@ -0,0 +1,31 @@ +# 第二批独立评测协议（评价前冻结） + +日期：2026-09-21。旧版 12 个实例及 120 次训练保留不变。本协议补充场景覆盖， +沿用原默认训练参数，不依据本批结果调参或筛选输入。 + +- 生成器 `make_corpus_v2.py`：3/4/5 任务 × 3 种故障场景 × 4 个数据种子， + 共 36 个合成实例。种子为 `2026092300 + 100*tasks + 10*scenario + replicate`。 +- 每任务原始候选数按 2/3/4 循环，时长 1–3、设备清洗时间 0–2；3 台设备、 + 2 个互斥小组。先生成无故障时合法的原预约，再增加单短故障、两段延长故障、 + 或带全链前序的单故障。原候选中包含原预约，其他设备/时间选择随机生成。 +- 无故障原预约必须由原始语义检查器验证。故障后的不可行、空域、纯经典传播、 + 有限采样失败均保留；不保证每个实例都仍需量子执行。 +- 每实例 10 个优化种子 0–9；p=1、COBYLA 两次重启、每次最多 60 评价、512 shots。 + 分别运行未分解 XY 与现有完整化简路径，不修改算法或超参数。 +- 分解模式每个组件各用 512 shots/相同训练上限，报告总 shots/评价数及耗时。 + 因总预算可能不同，这不是同预算胜率竞赛。分量重算罚系数和种子，分布不必相等。 +- 原始输入、日历剪枝、单候选传播、独立分量、XY 相位精简逐阶段报告资源。 + 原始输入未编码日历无效候选，只报告候选数，不虚构其量子门数或运行耗时。 + 门数采用固定 p=1 参数，CNOT/CRY 分列；组件深度同时报告串行和及最大值， + 最大值不是实际并行执行耗时。 +- 枚举原始输入全部候选组合、剪枝后全部可行位串及各分量回填集合，逐个验证 + 可行集合与成本保持；再核对独立 MILP。结构检查含全部 36 个实例。 +- 单次最优概率累加全部并列最优态；shots 8/16/32/64/128/512 曲线仅针对已训练 + 分布。先在每实例内平均优化种子，再等权平均可行实例；不把 360 个种子当作 + 360 个独立问题。报告每格 4 个实例的原始统计和范围，不作总体显著性承诺。 +- 全部原始记录、输入/源码哈希、环境、阶段耗时和失败状态归档；同环境重放时 + 只剔除时间字段后比较。训练、结构枚举和诊断分别计时，不宣称量子加速。 + +新增 `tradeoff.json` 是明确标注的手工业务说明案例，不并入随机评价集。三单可 +整体后移，或使用高成本备用设备保留后两单。量子求解必须处理故障关联的完整 +组件；精确结果仅在求解后认证，不能用来初始化或修复量子样本。 diff --git a/Tutorials/LabScheduling/COMPATIBILITY.md b/Tutorials/LabScheduling/COMPATIBILITY.md new file mode 100644 index 00000000..db6e16da --- /dev/null +++ b/Tutorials/LabScheduling/COMPATIBILITY.md @@ -0,0 +1,80 @@ +# Python 安装兼容性 + +推荐 Python 3.12。2026-09-21 在 Linux x86_64 上补充了 3.11 和 3.13 的 +独立 wheel 安装验证，核心代码和量子参数未修改。 +下表及机器记录保留该兼容性轮次的提交范围；后续报告预算修复的验证结果见 +[VALIDATION.md](VALIDATION.md)，不把历史安装指纹更新为未经重放的新指纹。 + +| Python | 依赖约束文件 | 应用测试 | 原有算法测试 | 核心覆盖率 | +|---|---|---:|---:|---:| +| 3.11.16 | `constraints-py311.txt` | 108 通过 | 18 通过 | 98.92% | +| 3.12.3 | `constraints-py312.txt` | 108 通过 | 18 通过 | 98.92% | +| 3.13.11 | `constraints-py312.txt` | 108 通过 | 18 通过 | 98.92% | + +3.12 的完整发布验证见 [VALIDATION.md](VALIDATION.md)。3.11 / 3.13 各使用干净 +worktree 内新建的 `.venv`，安装同一应用 wheel；确认导入来自 `site-packages`， +11 个核心模块与源码字节一致。依赖版本、模块及 wheel 指纹、命令、退出码和测试 +摘要见 [compatibility.json](results/compatibility.json)。两个新增环境均通过 +`pip check`，安装版本与对应约束文件逐项一致。 + +## 选择对应依赖 + +默认安装命令继续使用[主教程](README.md#1-三分钟演示)中的 Python 3.12 路线。 +Python 3.11 请在仓库根目录运行： + +```bash +python3.11 -m venv .venv +.venv/bin/python -m pip install \ + -c pyqpanda-algorithm/example/LabScheduling/constraints-py311.txt \ + -e ./pyqpanda-algorithm \ + -r pyqpanda-algorithm/example/LabScheduling/requirements-dev.txt +``` + +Python 3.13 使用 `python3.13 -m venv .venv`，随后执行主教程中的安装命令； +`constraints-py312.txt` 的文件名保留历史含义，当前固定版本也在 3.13 上验证通过。 +比较多个 Python 版本时，应使用不同 checkout 的 `.venv`，不要覆盖已有环境。 + +3.11 无法使用原 3.12 约束文件：实际安装预检以 `ResolutionImpossible` 失败。 +下列四项固定依赖的原版本要求 Python >=3.12；新文件仅调整这四项，其余锁定 +版本保持一致，未修改仓库全局依赖要求。 + +| 依赖 | 3.11 固定版本 | 3.12 / 3.13 固定版本 | +|---|---|---| +| NumPy | 2.4.6 | 2.5.3 | +| SciPy | 1.17.1 | 1.18.1 | +| ContourPy | 1.3.3 | 1.4.0 | +| Sphinx | 9.0.4 | 9.1.0 | + +PyQPanda3 均为 0.4.1；其发布文件含 CPython 3.11 / 3.12 / 3.13 的 Linux x86_64 +wheel，可在 [PyPI 发布文件](https://pypi.org/project/pyqpanda3/0.4.1/#files)核对。 +wheel 存在或包元数据允许安装本身不代表应用运行成功，上表记录的是实际运行结果。 + +## 验证范围与失败记录 + +两个新增环境均执行：medium（目标 4）、含 21 次权重敏感性运行的取舍演示、 +AC-3 资源边界案例（目标 3）、不启用 AC-3 的超限案例（退出码 5），以及不可行 +案例（退出码 3）。可行案例的实际量子结果经原始约束与经典证书验证。 + +源码可编辑安装使用主教程的测试命令。验证已安装 wheel 的覆盖率时，应指定 +安装目录，避免测到 checkout 中未被导入的副本。例如在 Python 3.11 中： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg \ + .venv/bin/python -m pytest -c test/LabScheduling/pytest.ini test/LabScheduling \ + --timeout=60 --cov=.venv/lib/python3.11/site-packages/pyqpanda_alg/LabScheduling \ + --cov-fail-under=95 +``` + +本次首次用 `--cov=pyqpanda_alg.LabScheduling` 在两个新增 wheel 环境均出现 +`ImportError: cannot load module more than once per process`，各有 9 个收集错误。 +改为上述实际安装目录后完整通过；归档保留该失败，不把首次运行算作通过。 +尚未确定该工具链行为的上游根因，不据此提交算法缺陷修复或修改业务代码。 + +CI 已配置 Ubuntu 的 3.11 / 3.12 / 3.13 矩阵，包含安装、质量检查、应用测试、 +18 项原有算法测试、成功演示与失败状态检查。远程实际结果单列于 VALIDATION.md； +本地通过不代表官方 PR CI 已获批准。 + +验证范围限于 Linux x86_64、标准 GIL 构建和上述依赖版本；未验证 Windows、 +macOS、ARM 或自由线程 Python。跨版本通过表示接口与业务性质成立，不表示不同 +依赖环境会产生完全相同的优化参数、counts 或耗时；严格历史重放仍应使用归档 +对应的提交与固定环境。 diff --git a/Tutorials/LabScheduling/ENHANCEMENTS.md b/Tutorials/LabScheduling/ENHANCEMENTS.md new file mode 100644 index 00000000..9f33d92b --- /dev/null +++ b/Tutorials/LabScheduling/ENHANCEMENTS.md @@ -0,0 +1,195 @@ +# 从小规模求解走向可审计的故障重排 + +队伍：youshen。数据均为合成；CPU 无噪声模拟。增强版保留原有显式候选接口， +新增可证明的线路精简、安全化简和原问题回填、原始数据 MILP、CSV 预约导入， +并以冻结语料记录失败边界。贡献是这些环节在应用中的实现、证明与验证， +不把 QAOA、XY 混合器、约束传播或 MILP 称为原创算法。 + +## 1. 三分钟业务演示 + +先按[主教程](README.md#1-三分钟演示)安装环境，在仓库根目录执行： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg .venv/bin/python pyqpanda-algorithm/example/LabScheduling/booking_demo.py +``` + +输出 `reports/booking-demo/`： + +- `outage-input.json` / `control-input.json`：CSV 展开后的完整候选，含尚未剪枝项。 +- `outage-report.json` / `control-report.json`：QUBO、传播原因、量子样本与双经典认证。 +- `changes.csv`：原预约、调整后预约、偏好成本、变更成本、原因。 +- `booking-changes.svg`：原预约与调整后的静态甘特图；红色阴影表示故障。 + +默认结果：停机后目标值 **6**、**3** 张预约改动、**0** 约束违反； +无故障对照目标值 **0**，全部保留。原问题 10 个候选，日历剪枝后 9 个； +传播固定三个烘箱任务，删除两个不兼容候选，剩余一个 **4 比特**量子组件。 +四比特组件实际运行 QAOA；传播解决的任务明确记作经典预处理。 +烘箱上的后两张预约原本没有碰到停机，但被迫连锁后移，不能提前冻结它们。 + +![原预约与重排](results/enhanced/booking-changes.svg) + +导入模板：[chain.csv](../../pyqpanda-algorithm/example/LabScheduling/data/bookings/chain.csv)， +日历：[outage.json](../../pyqpanda-algorithm/example/LabScheduling/data/bookings/outage.json)。 +CSV 必须使用模板列序，`allowed_resources` 以 `|` 分隔， +`earliest/latest` 是含端点的整数开始窗口，`step >= 1`。 +每个设备与窗口网格的笛卡尔积全部展开；`shift_cost * abs(start-original_start)` +是偏好成本，再按是否改约加 `change_cost`。设备兼容性由允许设备列显式表达。 +CSV 最多展开 256 个原始候选；超限报错，不能静默删候选。 +这是透明导入入口，尚无真实用户试用或隐私数据。 + +## 2. 删除 XY 相位里的恒零罚项 + +设任务域为 $D_t$，$N_t=\sum_{i\in D_t}x_i$，完整 QUBO +$Q=C+A\sum_t(N_t-1)^2+A\sum_{(i,j)\in E}x_ix_j$。 +W 初态在 $N_t=1$ 的子空间中；对角成本相位和域内 XY 交换都保持每个 $N_t$。 +因此每一步演化可达的子空间内，$\sum_t(N_t-1)^2=0$。 +将成本相位换为 $C+A\sum_E x_ix_j$，保留同一个缩放 $A$， +在无噪声、同参数、相同初态与混合器条件下得到相同的测量分布。 +Ising 实现略去的常数只影响全局相位。 + +`build_circuit(..., phase_mode="auto")` 默认仅对 XY 精简；`"full"` 保留旧相位。 +X 混合器会离开恰选一次子空间，必须使用完整相位，auto/full 对 X 相同。 +完整 `Model.energy`、罚系数、优化损失和审计表不变。 +优化器可能因浮点末位差异改变搜索路径，因此比较相同参数的线路分布， +不要求两个独立优化过程最终参数逐位相等。 + +每个被删除的同任务 ZZ 项节省本实现中的两个 CNOT， +节省数为 $2\sum_t\binom{|D_t|}{2}$。这是原生 PyQPanda3 操作计数， +CRY 单独保留，不等于面向某台硬件完整分解后的两比特门数。 +不承诺带噪硬件等价，深度也未必严格下降。 + +| 已有样例，p=1 | CNOT full→auto | 原生深度 full→auto | +|---|---:|---:| +| simple | 34→22 | 36→24 | +| medium | 78→54 | 45→35 | + +数学思想参考 [XY mixers, Wang 等](https://arxiv.org/abs/1904.09314)。 +该项创新价值在于将约束子空间性质用于可审计的应用线路编译，不是新的通用混合器。 + +## 3. 安全传播、分量与回填 + +调用 `reduce_model(model)` 或 CLI 加 `--reduce`： + +1. 某任务只有一个候选时，它在所有可行排程中必选；删除与它冲突的其他候选。 +2. 重复直到没有新单候选。若出现空域，返回 `infeasible_task` 及删除链，证明不可行。 +3. 将未固定任务按“仍存在不兼容候选对”连接成图，取连通分量。 +4. 每分量使用原问题的资源、候选与内部前序重建 QUBO。 + 删除的跨分量前序已对所有存活候选自动成立，否则会存在连边。 +5. 每分量独立运行真实 CPU QAOA，将实际采到的最佳位串映回原变量，插回固定选择， + 再按原问题约束检查。报告还在原始未剪枝输入上独立检查最终预约。 + +**等价性**：删除规则只排除与必选候选冲突的选择，所以所有原可行排程保留； +固定点后没有跨组件禁止对，组件可行排程的笛卡尔积加固定选择，恰好等于原可行集合。 +成本可加，$C_{original}=C_{fixed}+\sum_k C_k$，故组件精确最优值可相加。 +QAOA 没有最优保证，组件近似解拼接后也不获得最优保证。 +独立组件重算罚系数，因此不声称分解前后的量子分布相同。 + +`reduction.removals` 记录被删变量、`forced_by` 变量和冲突原因； +`component_to_original` 是回填映射，`fixed_cost` 单列固定成本。 +单次从每个组件独立抽一份的可行概率为各组件可行概率之积； +报告中的该乘积不是“各自挑完最佳样本后的最终成功率”。 +shots 是**每个组件**的预算，`total_shots=组件数×shots`。 +与未分解模型比较时必须计算总预算。 + +单组件最多 16 比特；总候选可以超过 16，但不能把组件总和称为模拟器处理的量子规模。 +大组件返回 `resource_limit`，CLI 退出码 **5**；不会截断或改用经典解假装量子输出。 +若传播已解决全部任务，状态为 `classical_propagation`，线路评价次数为 0。 +局部一致性不保证全局可行；默认路径只做单候选传播，没有支配剪枝。 +后续加入可选 `--reduce --pruning arc`，按完整对方域删除无支持候选； +证明、审计字段、超限边界演示与已知语料对照见[弧一致性说明](ARC_PRUNING.md)。 +本页历史评测仍对应默认单候选模式。 + +## 4. 独立 MILP 与证书边界 + +`solve_milp(problem, time_limit=30)` 从**原始未剪枝候选**独立建模： +每任务选择和为 1，日历无效选项的变量上界为 0，原始语义不兼容对满足 +$y_i+y_j\le1$，目标为原始偏好与改约成本。它不导入 QUBO 编译器、冲突图或能量。 +SciPy/HiGHS 求解后，以 `validate_assignment(problem, choices)` 再检查原始预约。 +MILP 实现与其检查器共享原始语义辅助函数，但与 QUBO 编译/评估独立； +小实例还与完全枚举交叉验证，避免以一份编码自证正确。 + +若某项任务的全部原始候选都违反设备日历，独立检查即可证明不可行，无需构造 +两两约束或调用 HiGHS。此时报告保留 `infeasible_task`、原因和原始变量数， +`constraints=0`，没有 `solver_status`；`best`、`dual_bound`、`gap` 均为 null。 +这项提前判定重新检查原始候选，不读取 QUBO 的剪枝结果。只要每项任务仍有 +日历可用选项，就继续完整建模；不会据此宣称资源冲突或先后关系一定可行。 + +对仍可求解的问题，保留所有原始变量、上界和选项编号，但只为日历可用选项 +构造两两约束：若上界已令 $y_i=0$，则 $y_i+y_j\le1$ 已由 $y_j\le1$ 保证。 +省去这类冗余行不改变可行集合、目标或回填索引，也不复用 QUBO 冲突图。 + +状态分为 `optimal`、`infeasible`、`limit`、`error`。 +达到时间限制时即便有可行 incumbent，也不标为最优；`dual_bound` 和 `gap` +分别返回，非有限/不可用值为 null。`time_limit` 仅限制求解器时间， +不包括模型构造。最多 2048 个原始 MILP 候选；配对构造 $O(n^2)$。 +SciPy 的状态定义见[官方 milp 文档](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.milp.html)。 + +报告的枚举上限仍为一百万组合，超过则 `exact.status="budget_exceeded"`； +若原始候选超过 2048，报告在构造 MILP 前记录 `milp.status="budget_exceeded"`， +同时列出原始候选数和上限，`best`、`dual_bound`、`gap` 均为 null。 +例如停机/时间范围剪枝可将 2049 个原始候选压到 2 比特，不能因为认证基线 +超预算就丢弃已完成的量子结果。直接 `solve_milp` API 的超限异常保持不变。 +只有精确枚举或 MILP 的最优证书可用于计算最优差距，未认证时 gap=null。 +不可行和有限抽样失败是不同状态。原生求解器有限精度证书不是形式化机器证明。 + +经典扩展检查： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 .venv/bin/python pyqpanda-algorithm/example/LabScheduling/classical_scaling.py +``` + +20/35/50 任务的三种合成烘箱链，MILP 目标值为 40/71/100，均返回 optimal。 +其最大连接组件分别为 59/104/149 候选，**没有执行量子求解**； +用于展示经典基线和资源边界，不是量子规模结果，也不代表一般调度性能。 + +## 5. 冻结语料与诚实比较 + +[实验协议](BENCHMARK_PROTOCOL.md)在首次评价前冻结生成规则、12 个实例和 10 个优化种子。 +[manifest](../../pyqpanda-algorithm/example/LabScheduling/data/corpus/manifest.json) +含输入及生成器 SHA-256；开发样例与评价实例分开。 +每个规模/拥挤格只有一个数据种子，因此该评价仍是探索性结果。 + +```bash +# 如需重建，生成器必须匹配冻结版本；不要根据求解结果筛选输入。 +.venv/bin/python pyqpanda-algorithm/example/LabScheduling/make_corpus.py +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 .venv/bin/python pyqpanda-algorithm/example/LabScheduling/stress_benchmark.py --output reports/lab-enhanced +.venv/bin/python pyqpanda-algorithm/example/LabScheduling/summarize_benchmark.py reports/lab-enhanced --output reports/lab-evidence +``` + +120 次训练全部保留，穷举与原始 MILP 一致：9 个实例可行，3 个不可行。 +可行实例中 86/90 个训练种子的单次最优概率高于均匀 one-hot，另 4 个退化； +9 个实例内部的种子平均值均较高，但不能把 90 个种子当作 90 个独立问题。 +最差的 16 比特稀疏实例种子单次最优概率约 0.00077，低于均匀的 0.00391。 +经典基线在这些规模更快，不宣称量子加速或普遍优势。 + +![全部实例原生 CNOT](results/enhanced/resources.svg) + +![可行实例低 shots 命中曲线](results/enhanced/hit-curves.svg) + +曲线计算 $P(hit\mid S)=1-(1-p_{opt})^S$，累计**全部并列最优态**的概率。 +先在实例内平均 10 个种子，再在 9 个可行实例间等权平均；不可行实例仍留在原始记录。 +交叉标记来自独立采样流的实际抽样；这些曲线都排除训练成本。 +`quantum.runtime_seconds` 含训练、最终采样及求解器内可行性诊断， +额外分布认证与对照线路计入 `diagnostic_seconds`。 +全程峰值 RSS 是 Linux 进程的累计高水位，不是某一线路或硬件内存需求。 + +查看[逐实例表](results/enhanced/SUMMARY.md)、 +[120 次原始记录](results/enhanced/runs.jsonl)、 +[代码与环境指纹](results/enhanced/provenance.json)。 +原来的三种子图表位于 `results/`，是 schema 1 / full 相位的历史基线； +增强版 schema 2 默认精简 XY，不要求优化历史与旧版逐位相同。 + +## 6. 测试与后续工作 + +除原有 QUBO/Ising 全状态验证外，新增：100 个随机小实例对所有可行排程做无损回填， +再与原始 MILP 比较；simple/medium 各 30 组随机角度的 p=1/2/3 实际 CPU 等价检查； +单候选混合域、连锁改约、多个组件、组件超限、独立证书状态、CSV 错误输入和成本展开。 +全部命令及安装证据见[验证记录](VALIDATION.md)。 + +下一步需要真实实验室的匿名数据与用户试用反馈，以及更多每格数据种子、 +更大连通组件的资源策略；目前没有用真实需求反馈或通用量子优势来包装合成实验。 +保留已有安全证明，比继续堆叠算法变体更有利于维护者理解和合并。 + +后续已补充[第二批异构评测](EVALUATION_V2.md)：36 个独立输入、每格 4 个数据种子、 +360 条配对运行记录，以及三项改约决策全部留在量子组件内的业务案例。 +上述 120 次归档仍保留原貌；真实用户验证和更大连通问题仍是限制。 diff --git a/Tutorials/LabScheduling/EVALUATION_V2.md b/Tutorials/LabScheduling/EVALUATION_V2.md new file mode 100644 index 00000000..f6e08270 --- /dev/null +++ b/Tutorials/LabScheduling/EVALUATION_V2.md @@ -0,0 +1,148 @@ +# 异构预约与逐阶段对照 + +本轮保持应用算法与默认参数不变。第一批 12 个实例/120 次训练继续保留， +第二批协议和生成数据在运行评价前以提交 `94efa84` 冻结。 +所有数据仍为合成，没有据此声称真实业务部署或量子加速。 + +## 覆盖了什么 + +3/4/5 任务 × 单短故障/双延长故障/前序链故障 × 4 个独立数据种子，共 36 个实例。 +每任务 2–4 个原始候选、时长 1–3、清洗时间 0–2，含跨设备选择和小组互斥。 +所有无故障原预约均独立验证为合法且成本为 0，再加入故障；故障后的实例不筛选。 +实际得到 24 个可行、12 个不可行问题，每个运行种子 0–9 的未分解和化简两条路径。 + +共 360 条配对种子记录，不等于 720 次实际量子训练：原始路径的空域以及化简后的 +不可行或完全固定情形不会启动量子线路；一个分解请求也可能训练多个组件。 +确切执行数、状态及逐实例预算见 [aggregate.json](results/v2/aggregate.json) +和 [summary.csv](results/v2/summary.csv)。 + +## 主要结果与退化 + +- 独立原始 MILP 与全部组合枚举对 36 个实例的可行性/最优值一致； + 日历剪枝与分量回填前后的**全部可行集合和成本**也逐个一致。 +- 可行问题的 240 个未分解训练种子中，206 个最优态概率高于均匀 one-hot， + 34 个更低。`n4-s0-r2` 连实例内 10 个种子的平均值也低于均匀基线，明确保留。 +- 512 shots 下，未分解路径 237/240 次命中最优；化简路径 240/240 次命中， + 其中 20 次是纯经典传播，且多个组件会增加总 shots/训练预算。 + **不能将这两个命中率作为同预算量子优势证据。** +- 12 个不可行问题全部被传播识别；未分解路径中 6 个因空域直接停止，另外 6 个 + 仍执行量子训练后没有可行样本。后者由 MILP/穷举确认不可行，不能仅靠抽样失败认证。 +- 未分解已训练线路 full/auto 同参数最大概率差为 `4.9961e-16` 以下。 + 概率比较基于无噪声 CPU，不延伸为真机等价结论。 +- 一次完整评价约 97.71 秒，含双路径训练、独立证书、组合证明和额外分布诊断； + 具体硬件/版本/代码指纹见 provenance。时间仅是本机观测，非性能承诺。 +- 第二次完整重放的 36 份实例记录、360 份配对运行记录在仅剔除 `_seconds` + 字段后完全一致，详见 [replay.json](results/v2/replay.json)。 + +![第二批实例资源与概率概览](results/v2/overview.svg) + +每格只含 4 个独立问题，故只提供逐实例结果及格内范围；一些故障格可行问题更少。 +低 shots 曲线和独立抽样记录保存于每次训练的 `direct_metrics`，均排除训练成本。 + +## 资源节省来自哪一步 + +以固定登记的 `n5-s0-r0` 为例： + +| 阶段 | 候选/量子资源 | 原生 CNOT（p=1） | +|---|---|---| +| 原始预约输入 | 14 个候选 | 不计：尚未编码日历无效候选 | +| 日历剪枝 | 13 比特 | 62 | +| 单候选传播 | 9 个剩余变量 | 见逐阶段记录 | +| 独立分量 | 2、5、2 比特；最大组件 5 比特 | 合计 29 | +| XY 精简相位 | 相同分量与变量 | 合计 17 | + +单候选传播和分解改变模型罚系数及随机种子，不要求与原模型有相同量子分布； +只证明原问题可行集合/成本保持。分量是顺序执行的，组件最大深度仅描述资源上限。 +某些问题分解后总训练次数或总运行时间上升；表格保留这些情况，不只展示成功节省。 + +## 业务案例：由量子组件完成实际取舍 + +[tradeoff.json](../../pyqpanda-algorithm/example/LabScheduling/data/tradeoff.json) +包含三个有前序关系、时长不同的实验，烘箱每次清洗 1 时隙。故障使 A 的原预约失效。 +候选允许三单连锁后移（成本 12、改动 3 单），或使用昂贵备用设备（成本 14、改动 1 单）。 +这也说明“加权成本最小”与“改约数量最少”不是同一目标。 + +剪枝后域大小为 2/3/3，没有单候选，三个任务仍在同一 8 比特组件。实际默认采样 +选择成本 12 的解；完整 QUBO/精简相位的 CNOT 为 37/23，原生深度为 42/23。 +无故障对照成本 0。所有 9 个可行方案见 [alternatives.csv](results/v2/tradeoff/alternatives.csv)， +采样证据、独立证书与图示见 [业务结果目录](results/v2/tradeoff/)。 + +## 复现 + +安装依赖后，在仓库根目录执行： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg .venv/bin/python pyqpanda-algorithm/example/LabScheduling/tradeoff_demo.py +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg .venv/bin/python pyqpanda-algorithm/example/LabScheduling/benchmark_v2.py --output reports/lab-v2 +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg .venv/bin/python pyqpanda-algorithm/example/LabScheduling/summarize_v2.py reports/lab-v2 +``` + +生成器和输入内容不匹配冻结哈希时，评价入口直接报错。原始输出分为 +`instances.jsonl`（每实例结构/经典证书）与 `runs.jsonl`（每种子两条求解路径）， +以避免在每个种子中复制不变的输入与证书。全部运行记录均提交，未删去退化或失败项。 + +## 改约成本敏感性：什么时候值得使用备用设备 + +使用已有三工序案例，保持全部约束与候选不变，只把每项任务的改约成本统一设为 +`w=0,1,2,3,4,5,6`。这是已知开发案例上的业务解释，不是新的留出评测或实际价格调查。 +[协议](SENSITIVITY_PROTOCOL.md)与[输入及参数网格](../../pyqpanda-algorithm/example/LabScheduling/data/sensitivity.json) +在首次运行前以 `5ebcdc3` 提交冻结。原来的默认权重仍为 2。 + +每项任务 i 的成本为偏好费用加改约费用， +`C(w) = sum(preference_i) + w * changed_tasks`。 +“整体后移”的偏好费用合计 6、改约三单，因此 `C_shift=6+3w`； +“备用设备”的偏好费用合计 12、改约一单，因此 `C_backup=12+w`。 +二者在 `w=3` 相交。我们还枚举了全部原始候选组合，确认七组输入都有相同的 +**9 个可行排程**；它们的完整成本曲线也纳入核验，不只比较这两个预选方案。 + +| 每单改约成本 w | 认证最优成本 | 认证最优决策 | 改约数量 | 三种子实际目标 | +|---:|---:|---|---|---| +| 0 | 6 | 整体后移 | 3 | 6 / 6 / 6 | +| 1 | 9 | 整体后移 | 3 | 9 / 9 / 9 | +| 2 | 12 | 整体后移 | 3 | 12 / 12 / 12 | +| 3 | 15 | 整体后移或备用设备，同优 | 3 或 1 | 15 / 15 / 15 | +| 4 | 16 | 备用设备 | 1 | 16 / 16 / 16 | +| 5 | 17 | 备用设备 | 1 | 17 / 17 / 17 | +| 6 | 18 | 备用设备 | 1 | 18 / 18 / 18 | + +![全部排程成本与实际量子差距](results/sensitivity/sensitivity.svg) + +量子设置统一为 seed 7/19/42、p=1、2 restarts、maxiter=60、512 shots、XY、 +默认单候选化简；三个任务始终留在一个 8 比特组件内，无固定任务。 +全部 **21 次**实际量子结果零违反、最优差距为 0，随后经独立穷举与原始 MILP 认证。 +图左灰线是全部可行排程的成本，星号是经典认证；图右是实际量子 gap， +重合点均保留。不能把图左解析曲线当作量子采样结果。 + +在 `w=3`，全枚举确认 `[1,1,1]` 与 `[2,0,0]` 两个原始候选索引组合都最优。 +本次三个种子的最终选中解均为备用设备方案；这不排除整体后移同样最优， +也不表示量子结果均匀覆盖所有同优方案，或把“少改约”作为了额外优化目标。 +评分接纳任意同优排程；报告重放则要求实际记录一致，二者是不同要求。 + +这解释了配置权重的业务含义：越重视避免改约，越可能接受更贵的备用设备。 +`w=3` 是这个合成案例的阈值，成本单位是偏好分；不把它推广为真实实验室价格。 +21 次命中也不改变[同预算实验](MATCHED_BUDGET.md)中经典随机持平或胜出的结论， +不声称量子加速或对任意问题有最优保证。 + +在原演示后加一个参数即可生成甘特图、成本曲线、七行表和全部记录： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg .venv/bin/python pyqpanda-algorithm/example/LabScheduling/tradeoff_demo.py --sensitivity +``` + +结果位于 `reports/tradeoff-demo/sensitivity/`：`summary.csv` 是七行决策表， +`inputs/` 是七个派生输入，`results.json` 包含全部实际参数、counts、状态、 +分项成本、认证、源码/输入指纹和环境；压缩排版以减少原始归档 diff。 +基础 `tradeoff.json` 不被修改。有限抽样失败或非最优结果会保留，业务字段在无 +可行样本时为 null，不以经典解替换；测试专门覆盖这些分支。 + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg .venv/bin/python pyqpanda-algorithm/example/LabScheduling/tradeoff_demo.py --sensitivity --output reports/tradeoff-replay --verify-sensitivity reports/tradeoff-demo/sensitivity/results.json +``` + +第二次完整执行中七个输入、21 次参数/counts、全部可行集合与成本、源码/环境 +指纹均复现；仅忽略 `_seconds` 字段，见[重放记录](results/sensitivity/replay.json)。 +上述命令比较当前环境内的两次运行；若要严格核对已提交的历史归档，请按 +[源码版本与环境说明](REVIEW_GUIDE.md#当前运行与历史归档如何核对)选择对应提交。 +[归档决策表](results/sensitivity/summary.csv)与[完整记录](results/sensitivity/results.json) +对应实现 `51a5067`，可按其源码与锁定环境复现。 +该增强仅涉及示例、测试、文档和聚焦 CI，核心包与默认量子参数不变。 diff --git a/Tutorials/LabScheduling/MATCHED_BUDGET.md b/Tutorials/LabScheduling/MATCHED_BUDGET.md new file mode 100644 index 00000000..b2d29feb --- /dev/null +++ b/Tutorials/LabScheduling/MATCHED_BUDGET.md @@ -0,0 +1,82 @@ +# 同总预算与结构化随机基线 + +本补充评价回答两个具体问题：分量量子求解的表现是否依赖额外预算？ +化简与分解的收益，是否也能由经典随机采样获得？ +[协议](MATCHED_BUDGET_PROTOCOL.md)在执行前以 `eb9c5c3` 提交冻结， +实现为 `b140dab`。沿用已经公开的第二批输入，属于补充分析，不是新盲测。 +核心应用与旧归档保持不变。 + +## 对照设计 + +每实例十个种子，两条量子路径总训练评价上限均为 120，总最终采样预算均为 +512。分量路径把每次重启的 60 次评价和 512 shots 按商余数均分，例如三分量 +各用 20 次评价 × 两次重启，采样数为 171/171/170。分配不参考精确解或训练结果。 +单分量沿用主种子，保证模型未变化时两路径完全一致；多分量使用固定子种子。 + +两条纯经典基线分别在原模型和相同精确化简后的分量中均匀采样 one-hot 排程， +使用同样的总 shots 和分配规则。所有路径均只选择实际样本，不用经典最优解修补。 +四条路径结束后才做精确认证及分布诊断。预算不足、不可行、纯经典传播均显式保留。 + +## 结果及边界 + +36 个实例中，22 个可行且仍需量子求解，2 个由经典传播完全解决，12 个不可行。 +下表只统计前 22 个实例 × 十种子，避免把纯经典成功算作量子成功： + +| 路径 | 实际采到最优 / 220 | 平均训练评价数 | 平均求解时间 / 秒 | +|---|---:|---:|---:| +| 直接 QAOA | 217 | 119.73 | 0.1981 | +| 分量 QAOA，同总预算 | 220 | 119.93 | 0.1125 | +| 直接均匀随机 | 220 | 0 | 0.0132 | +| 分量均匀随机，同总预算 | 220 | 0 | 0.0071 | + +**512 shots 下，经典随机基线也全部采到最优，不能据此宣称量子优于随机。** +QAOA 的经典训练与模拟成本明显存在。时间来自一次本机串行运行，包含各自采样和 +可行性诊断，不包括后来统一的精确认证/分布诊断；预先编译与化简是共用准备步骤， +不计入任一路径计时。这不是完整应用的端到端测速，更不是硬件加速结论。 +相等的是允许预算上限，COBYLA 实际评价数和不同宽度线路成本并不完全相同。 + +在每实例内平均十种子，再对这 22 个实例等权平均，可得到训练后理论命中曲线： + +| 总 shots | 直接 QAOA | 分量 QAOA | 直接随机 | 分量随机 | +|---:|---:|---:|---:|---:| +| 8 | 0.5657 | 0.7138 | 0.3455 | 0.4843 | +| 16 | 0.7282 | 0.8427 | 0.5246 | 0.6942 | +| 32 | 0.8438 | 0.9215 | 0.7099 | 0.8578 | +| 64 | 0.9098 | 0.9657 | 0.8571 | 0.9480 | +| 128 | 0.9461 | 0.9893 | 0.9502 | 0.9860 | +| 512 | 0.986578 | 0.999852 | 0.999787 | 0.999963 | + +这些是已训练无噪声分布上的条件概率，包含全部并列最优解，不是低 shots 的实际 +重复抽样成绩，也没有扣除训练成本。低采样预算下的平均分布收益不保证逐例改善： +16 shots 时 `n4-s0-r3` 的分量 QAOA 均值低于分量随机；32 shots 时还包括 +`n4-s0-r0`。512 shots 时分量 QAOA 的整体条件均值也略低于分量随机。 +完整逐例计数、失败、参数与 counts 见[结果表](results/matched/SUMMARY.md)。 + +### 为什么分量曲线使用乘积 + +固定训练后的第 j 个分量最优概率为 p_j，获得 B_j 次独立采样。该分量至少 +获得一个最优样本的概率为 `1-(1-p_j)^B_j`。精确分解后总成本是固定偏移加各分量 +成本，因此组装解全局最优当且仅当每个分量最优。独立采样给出 +`product_j[1-(1-p_j)^B_j]`，并满足 `sum B_j=B`。 +这与单次组装概率 `product_j p_j` 不同，也不能当作 B 次完整排程采样。 +分量随机基线采用同一公式，才能识别组合结构本身带来的收益。 + +## 复现 + +按主教程安装后，在仓库根目录运行；本机每次完整 360 记录约 91 秒，机器不同会变化： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 .venv/bin/python pyqpanda-algorithm/example/LabScheduling/matched_budget.py +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 .venv/bin/python pyqpanda-algorithm/example/LabScheduling/matched_budget.py --output reports/lab-matched-replay +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 .venv/bin/python pyqpanda-algorithm/example/LabScheduling/summarize_matched.py reports/lab-matched --replay reports/lab-matched-replay +``` + +比较器核对输入/源码/环境指纹，只忽略以 `_seconds` 结尾的计时字段；参数、 +counts、目标、状态及理论曲线必须全部一致。归档[重放记录](results/matched/replay.json) +确认历史 360 条记录通过。上述命令比较当前版本的两次运行；严格核对历史归档时， +应使用[匹配的源码提交与环境](REVIEW_GUIDE.md#当前运行与历史归档如何核对)。 +新增测试检查预算守恒、不足时停止、单分量公平性、真实 CPU +采样来源、原变量回填、不可行域及比较器拒绝被修改的样本。 + +本轮贡献是消除评价中的预算混淆和加强可核查性。尚未获得真实业务数据或硬件 +结果，不根据这批已知合成数据的补充分析扩大奖项、泛化或量子优势承诺。 diff --git a/Tutorials/LabScheduling/MATCHED_BUDGET_PROTOCOL.md b/Tutorials/LabScheduling/MATCHED_BUDGET_PROTOCOL.md new file mode 100644 index 00000000..038ce5f3 --- /dev/null +++ b/Tutorials/LabScheduling/MATCHED_BUDGET_PROTOCOL.md @@ -0,0 +1,31 @@ +# 同总预算补充对照协议 + +2026-09-21，运行前提交冻结。沿用第二批已公开的 36 个输入与种子 0–9； +这属于已知数据上的补充分析，不是新的盲测集，也不改变旧协议或旧结果。 + +1. 直接路径：XY、p=1、两次重启，每次最多 60 次目标函数评价，总计上限 120； + 最终采样总预算 512。重新运行，不从旧记录挑选最佳参数或种子。 +2. 分量路径：先使用原有精确化简。若有 k 个分量，按稳定分量顺序把 60 + 个每次重启评价额度、512 次采样分别商余数均分，前面的分量获得余数。 + 每分量两次重启，所以总训练评价上限仍为 120，总采样数严格为 512。 + 不按精确解、训练质量或问题难度自适应分配。单分量继承主种子，以使 + 无化简、无分解时与直接路径完全相同；多分量用 SeedSequence 子种子。 +3. 保留全部不可行、纯经典、超限和无可行样本状态。纯经典传播使用零量子 + 预算，单独统计。若预算不足以运行所有分量，报告 budget_insufficient， + 不偷偷增加预算。本批最多三分量，默认预算可覆盖。 +4. 增加两个纯经典随机对照：原模型均匀 one-hot，以及同样精确化简后每分量 + 均匀 one-hot。采用相同总采样预算及分配规则，固定独立随机流；均无量子 + 训练。各分量只选实际样本，再回填，不能调用最优解修复。 +5. 所有四条路径完成后才计算精确最优值与分布诊断。实际结果保留 counts、 + 采样最优值、可行性与绝对差距。按实例先平均十个种子，再汇总实例； + 22 个仍需量子的可行实例与两个纯经典可行实例分开报告。 +6. 对已训练分布计算总 shots 为 8/16/32/64/128/512 的理论命中曲线。 + 直接路径为 1-(1-p)^B；独立分量组装最优解的概率为 + product_j[1-(1-p_j)^B_j]，各 p_j 包含全部并列最优态，sum B_j=B。 + 均匀分量基线也采用该公式。零分量可行时成功率为 1，明确属于经典传播。 + 这些曲线不是重新训练或实际低 shots 试验，也不是量子优势证明。 +7. 报告实际目标函数评价数、采样数与串行墙钟时间。相等的是允许的预算上限； + COBYLA 可提前结束，且不同宽度线路的单次模拟成本不同，因此不宣称 + 实际计算量或端到端耗时相同。训练后诊断和经典认证耗时单列。 +8. 归档全部 360 条配对记录、源码/输入/协议指纹与环境。完整重跑一次， + 仅排除以 `_seconds` 结尾的计时字段，比较其余记录。任何退化结果照实保留。 diff --git a/Tutorials/LabScheduling/README.md b/Tutorials/LabScheduling/README.md new file mode 100644 index 00000000..66388af5 --- /dev/null +++ b/Tutorials/LabScheduling/README.md @@ -0,0 +1,354 @@ +# LabReschedule：基于 QUBO 的共享实验室最小扰动重排 + +一台仪器临时停机，哪些实验必须改约，哪些预约可以保留？本应用把设备预约、 +多时隙实验、清洗占用、小组冲突和工序先后关系编译为 QUBO，在 **PyQPanda3 +CPUQVM** 上运行 QAOA，并用独立穷举与原始输入 MILP 给出可核查的最优性和不可行性结论。 + +面向 2026 本源杯开源创新赛道。数据均为合成场景，没有学生个人信息。 +本项目交付的是可复用 Python 模块、命令行、输入样例、测试和实验记录。 +小规模经典算法更快；这里验证量子建模及约束保持线路，不宣称量子加速。 + +首次阅读建议从[五分钟审阅路线](REVIEW_GUIDE.md)开始。下面的业务案例中，烘箱 +故障后，三个实验可以整体后移，也可以租用备用设备来少改两单。成本权重决定 +哪种方案更合适；三个决策均保留在 8 比特量子组件中。 + +![合成业务案例：原预约与实际量子采样得到的重排](results/v2/tradeoff/tradeoff.svg) + +图为已归档的默认案例运行，成本 12、零违反；运行 `tradeoff_demo.py` 可生成 +当前版本的图与原始报告。完整比较、九种可行排程和权重切换见 +[业务取舍与成本分析](EVALUATION_V2.md)。 + +| 要核验什么 | 入口 | +|---|---| +| 安装、JSON 输入、QUBO 推导与测试 | 本页第 1–8 节 | +| CSV 连锁改约、XY 相位精简、分量回填证明 | [增强教程](ENHANCEMENTS.md) | +| 相同训练与采样预算下的量子/随机比较 | [同预算评价](MATCHED_BUDGET.md)，含随机持平或胜出的结果 | +| 构造案例的 18→12 比特化简及适用边界 | [可选弧一致性](ARC_PRUNING.md) | +| 当前版本重复运行与历史归档的对应提交 | [重放说明](REVIEW_GUIDE.md#当前运行与历史归档如何核对) | +| 测试、安装兼容性与 CI 的实际验证范围 | [验证记录](VALIDATION.md)、[安装兼容性](COMPATIBILITY.md) | +| 使用自己的预约数据并记录使用反馈 | [小范围试用流程](USER_TRIAL.md)，目前仅完成合成演练 | + +第一版的[历史开发基线图](results/overview.svg)保留供追溯。 + +## 1. 三分钟演示 + +在仓库根目录运行。推荐 **Python 3.12**（实际验证 3.12.3，WSL Linux x86_64）。 +Python 3.11、3.12、3.13 均已通过 Linux x86_64 完整应用测试及 wheel 验证。 +3.11 需要单独的约束文件，3.13 可沿用下方约束；具体版本与边界见 +[安装兼容性](COMPATIBILITY.md)。首次下载依赖所需时间取决于网络。 + +```bash +python3.12 -m venv .venv +.venv/bin/python -m pip install \ + -c pyqpanda-algorithm/example/LabScheduling/constraints-py312.txt \ + -e ./pyqpanda-algorithm \ + -r pyqpanda-algorithm/example/LabScheduling/requirements-dev.txt +``` + +安装后，一条命令完成读入、剪枝、QUBO、量子求解、经典认证与报告保存： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 .venv/bin/python -m pyqpanda_alg.LabScheduling pyqpanda-algorithm/example/LabScheduling/data/medium.json --output reports/medium.json +``` + +应看到 JSON 中 `status="feasible"`、`quantum.best.objective=4`、 +`quantum.best.violation_count=0`、`exact.best.objective=4`、`absolute_gap=0`。 +默认 seed=7、p=1、512 次最终采样、2 次参数初始化、每次至多 60 次优化评估。 +运行时间在本机为数秒以内（含解释器及依赖导入）；耗时不是跨机器承诺。 +不需要账户、API Key、GPU 或真实硬件。 + +若希望直接看到“原预约→故障→三单连锁改约”的 CSV 与甘特图，可运行： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg .venv/bin/python pyqpanda-algorithm/example/LabScheduling/booking_demo.py +``` + +输出成本 6、3 张改约、0 违反，以及无故障对照成本 0；详见[增强教程](ENHANCEMENTS.md)。 + +评委建议演示顺序： + +1. 运行上面的中等实例，查看四项实验日程及最优值 4。 +2. 查看 `model.pruned`：停机剪掉两个候选，14 个输入候选降为 12 比特。 +3. 查看 `model.variables`、`linear`、`quadratic`、`offset`，追溯比特到实际预约。 +4. 运行同命令加 `--mixer x`，对比 `one_hot_probability`、可行概率及最优差距。 +5. 把输入换为 `data/infeasible.json`，验证经典认证不可行、退出码为 3。 +6. 运行第 8 节测试，核查数学恒等式、位序和真实量子模拟。 + +完整接口帮助：`.venv/bin/python -m pyqpanda_alg.LabScheduling --help`。 +`--output` 会替换指定报告文件，请使用专用路径。若它与输入是同一路径，或通过 +符号链接/硬链接指向同一文件，CLI 会在求解前拒绝并退出 2。报告先写入同目录的 +临时文件，写完再替换；普通写入或替换失败会保留旧报告并清理临时文件。 +有效输出符号链接保留，更新其目标报告。这不提供断电持久性或多进程写入事务保证。 + +报告 `input_sha256` 对应本次读取并求解的原始 UTF-8 字节，包括原换行形式； +求解期间外部修改或删除输入不会改变该指纹。复现时应保留对应输入版本。 + +## 2. 问题定义与实际价值 + +校园实验室的仪器是单位容量资源。实验不能拆分，实验结束后设备还需要清洗； +同一小组不能同时在两台设备操作。部分实验必须等前一道工序完成才能开始。 +设备故障后，重新排一张可行表之外，还希望尽量保留原预约，降低协调成本。 + +应用关注有限时间窗口内的局部重排，而非整个学校的全年排课。调用者明确给出 +每项任务允许的 `(设备, 开始时刻)` 候选；不同任务可有不同候选数量。 +设备兼容性与人工确定的可用窗口通过候选集表达。一个任务只有一个小组标识； +不模拟可同时参加多个小组的个人。 + +- 仪器占用区间：`[start, start + duration + cleanup)`。 +- 小组工作区间：`[start, start + duration)`，清洗不继续占用小组。 +- 前序约束：`end(before) <= start(after)`，不等待前序设备的清洗结束。 +- 区间为左闭右开，相邻边界可接续；设备清洗结束也必须不超过 horizon。 +- 停机期间连清洗也不可发生。超时、停机候选在建模前删除，保留删除原因。 +- 冲突设备/小组各为单位容量，无容量松弛变量。 + +这些定义明确区分设备和小组时间，避免“实验结束了，但设备还不能用”的漏排。 + +## 3. 输入格式 + +完整示例：[simple.json](../../pyqpanda-algorithm/example/LabScheduling/data/simple.json)、 +[medium.json](../../pyqpanda-algorithm/example/LabScheduling/data/medium.json)、 +[infeasible.json](../../pyqpanda-algorithm/example/LabScheduling/data/infeasible.json)。 + +```json +{ + "name": "单实验示例", + "horizon": 6, + "resources": [{"id": "instrument", "cleanup": 1, "downtime": [[0, 1]]}], + "tasks": [{ + "id": "A", "group": "group-A", "duration": 2, + "original": {"resource": "instrument", "start": 0}, + "change_cost": 3, + "options": [ + {"resource": "instrument", "start": 0, "cost": 0}, + {"resource": "instrument", "start": 1, "cost": 1} + ] + }], + "precedence": [] +} +``` + +| 字段 | 语义 | +|---|---| +| `horizon` | 正整数，统一离散时隙的右边界 | +| `resources[].id` | 唯一设备名称 | +| `cleanup` / `downtime` | 默认 0 / []，非负整数清洗时间 / 停机区间 | +| `tasks[].id, group, duration` | 唯一任务名称、小组、正整数持续时间 | +| `options[].resource, start, cost` | 设备引用、非负开始时刻、非负整数偏好成本 | +| `original` | 可选原预约，可以因故障已不可用，不必仍在候选中 | +| `change_cost` | 默认 0；改变原设备或开始时间时一次性支付，有此成本须有 original | +| `precedence` | 默认 []，任务 ID 二元组 `[before, after]` | + +时间和成本整数范围为 0–1,000,000（horizon/duration 至少为 1）。 +拒绝重复 JSON 键、重复 ID/候选、未知字段和引用、负成本、浮点数、布尔整数和 NaN。 +`tasks=[]` 定义为空排程且目标为 0；任务的 `options=[]` 是有效但不可行的问题。 +前序成环也是有效但不可行的约束集合，交给精确基线认证。 +`Problem.from_dict`、`load_problem` 和 `parse_problem(text)` 是输入校验入口； +`parse_problem` 可从内存中的 JSON 文本构造相同的严格验证结果，仍拒绝重复键和 +非有限常量。直接构造内部数据类 +的调用者须满足同样的不变量。 + +## 4. QUBO 建模与惩罚证明 + +对任务 $t$ 的存活候选集合 $D_t$，定义 $x_i\in\{0,1\}$；$x_i=1$ +表示选择候选 $i$。索引按输入任务顺序、候选顺序生成，删去无效候选后连续编号。 +变量 $i$ 就是量子比特 $i$，不通过字符串名字排序。 + +候选线性成本为 + +$$c_i=\text{preference}_i+ +\text{change\_cost}_{t(i)}\, +\mathbf1[(r_i,s_i)\ne(r^0_{t(i)},s^0_{t(i)})].$$ + +没有原预约时第二项为零；这是加权扰动和偏好的联合目标，未声称按变更次数 +进行词典序优化。硬约束包括：每任务恰选一次、设备及小组互斥、前序完成时间。 +剪枝已满足所有候选自身的停机和时间边界约束。 + +令 $C$ 是不同任务之间不允许同时选择的候选对集合。即使同一对同时违反设备、 +小组和前序规则，也只加入一个冲突对；解释报告保留全部原因。完整 QUBO 为 + +$$E(x)=\sum_i c_i x_i+ + A\sum_t\left(\sum_{i\in D_t}x_i-1\right)^2+ + A\sum_{(i,j)\in C}x_ix_j.$$ + +使用 $x_i^2=x_i$ 展开：常数 $A|T|$，线性系数 $c_i-A$；同任务不同候选的 +二次系数 $2A$，不同任务冲突候选的二次系数 $A$。 +`quadratic` **只储存 $iU$。 + +违反数和罚分分开：选了三个候选的任务只算一个违反，但恰选一次罚项为 4； +有多个原因的冲突候选对只算一次违反。 +不可行输出的 `objective` 为 null，`raw_cost` 和 `qubo_energy` 仍保留供审计。 + +## 5. 量子算法与技术架构 + +```mermaid +flowchart LR + A[JSON 输入校验] --> B[剪枝与候选映射] + B --> C[QUBO 与 Ising] + C --> D[CPUQVM QAOA] + D --> E[有种子的 Born 分布采样] + E --> F[独立解码与约束检查] + B --> G[任务候选笛卡尔积穷举] + G --> H[最优性或不可行认证] + F --> I[JSON 报告] + H --> I +``` + +QUBO 使用 $x_i=(I-Z_i)/2$ 转换为对角 Ising 算符 $H_C$。 +相位线路用 $H_C/A$ 归一化控制角度尺度；报告中的能量仍是原始 QUBO 单位。 +常数偏移保留在能量计算中，相位线路省略相应全局相位不影响概率。 + +每层依次应用成本相位和混合器。增强版默认 XY 相位删除在可达子空间恒零的 +one-hot 罚项，仍使用相同 A 缩放，分布等价证明及 full/auto 审计见 +[增强教程第 2 节](ENHANCEMENTS.md#2-删除-xy-相位里的恒零罚项)。默认 XY 方法： + +- 对每项任务用上游 `linear_w_state` 准备等幅单激发态，初态是这些 W 态的直积。 +- 通过上游 `iswap(i,j,2*beta)` 依次交换域内相邻候选的激发，保持域内汉明重量。 +- 单候选域不施加混合门，保证其比特始终为 1。 +- 每个交换因子保持恰选一次；相邻因子的乘积不宣称等于整个 XY 和式的精确指数。 +- 此不变量只消除任务选择数错误，设备/小组/前序冲突仍依靠成本相位和罚分处理。 + +`--mixer x` 使用 Hadamard 初态及 RX 混合器作为标准惩罚 QAOA 对照。 +XY/X 的初态与混合器共同变化，属于完整策略消融；没有把效果全部归因于某个门。 +核心线路复用 `pyqpanda_alg.QAOA.qaoa.pauli_z_operator_to_circuit` 和 +`default_circuits`，实际执行只使用 `pyqpanda3.core.CPUQVM`。 +应用自管优化循环，以控制比特映射、随机流、预算和原始采样证据，不改上游接口。 + +COBYLA 最小化无噪声态矢量的期望 QUBO 能量。两个重启使用相同预算，记录每次 +能量及优化器终止原因；达到预算时不会声称数值收敛。最终采用所有评估中最低期望 +能量对应的参数。训练不会访问经典最优排程或预筛过的可行状态集合。 + +CPUQVM 的无测量线路给出确定的 Born 概率；最终用单独的 NumPy SeedSequence +子随机流执行 multinomial 采样。`shots` 是这个模拟采样预算，不是真机测量次数。 +只从实际被采到的比特串选择最优可行排程；没有可行样本时保留最低 QUBO 能量的 +不可行样本并返回 `no_feasible_sample`。不作经典修复、不以穷举结果替换量子输出。 +`counts` 使用常规高位在左的字符串，`bits` 列表则按 qubit 0,1,… 顺序。 + +文件职责：`problem.py` 输入；`model.py` 编译/独立约束检查；`classical.py` +穷举/贪心；`quantum.py` 线路/优化/采样；`report.py` 实验汇总；`__main__.py` CLI。 +增强模块：`reduction.py` 安全传播/组件/回填，`milp.py` 原始输入优化与检查， +`bookings.py` CSV 窗口展开，`metrics.py` 训练后最优命中率/资源审计。 +公共 API 的类型和文档字符串随代码提供；基本调用如下： + +```python +from pyqpanda_alg.LabScheduling import load_problem, compile_qubo, solve_qaoa, solve_exact +problem = load_problem("pyqpanda-algorithm/example/LabScheduling/data/simple.json") +model = compile_qubo(problem) +quantum = solve_qaoa(model, seed=7) +certificate = solve_exact(model) +``` + +## 6. 示例结果及经典对比 + +本节是增强前 schema 1 / full 相位的历史开发结果。在锁定环境中，XY、三个种子 7/19/42 在两种规模均从 512 次采样中 +找到了最优解；这是有限实验结果，不是算法保证。 + +| 场景 | 输入→存活候选 | 任务组合数 / 可行数 | 精确最优 | XY 三种子目标 | X 三种子目标 | +|---|---:|---:|---:|---|---| +| simple | 7→6 | 9 / 4 | 5 | 5 / 5 / 5 | 5 / 5 / 5 | +| medium | 14→12 | 81 / 10 | 4 | 4 / 4 / 4 | 8 / 18 / 13 | + +| 场景 | XY 平均可行概率 | X 平均可行概率 | 均匀 one-hot 可行概率 | +|---|---:|---:|---:| +| simple | 0.497681 | 0.013176 | 0.444444 | +| medium | 0.248389 | 0.002105 | 0.123457 | + +XY 的 one-hot 概率在浮点误差内为 1。简单实例的 seed 7/42 可行概率低于均匀 +one-hot，故不能声称全面优于随机方法。均匀 one-hot 的同预算抽样在这些小实例 +也找到了最优解。精确穷举只需 9/81 个任务组合，远快于 CPU 量子模拟。 +原始概率、随机样本、优化曲线、终止原因、耗时、环境版本和输入 SHA-256 均保留在 +[results](results/)；详细验证见 [VALIDATION.md](VALIDATION.md)。 + +中等实例 seed 7 的实际输出： + +| 实验 | 设备 | 实验区间 | 清洗结束 | 原预约变更 | +|---|---|---|---:|---| +| B-image | microscope | [0,2) | 3 | 否 | +| A-prepare | spectrometer | [2,4) | 5 | 是，成本 4 | +| A-image | microscope | [4,6) | 7 | 否 | +| B-measure | spectrometer | [5,7) | 8 | 否 | + +仅变更故障影响的 A-prepare；三条其他预约保留。spectrometer 停机 [0,2)， +microscope 停机 [7,8)，清洗与停机边界均满足。 + +退出码：0=返回可行排程（不必最优），2=输入/直接求解预算错误，3=经典证明不可行， +4=未采到可行解且未证明不可行，5=化简后仍有超过量子资源上限的组件。 +全由传播解出的排程明确标记 classical_propagation。 +`absolute_gap` 是可行目标减经典最优值；没有最优证书或未命中时为 null。优化器未收敛不等同于问题不可行。 + +## 7. 复现实验和图表 + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 .venv/bin/python pyqpanda-algorithm/example/LabScheduling/benchmark.py --output reports/lab-scheduling +.venv/bin/python pyqpanda-algorithm/example/LabScheduling/plot_results.py reports/lab-scheduling +``` + +当前脚本按增强版默认 auto 相位运行，不覆盖仓库内历史归档。 +该脚本运行两种输入 × 三个种子 × 两种混合器，生成 12 份紧凑 JSON、summary.csv +及后续生成的 overview.svg。默认预算完全相同，允许优化器提前收敛；记录实际 +线路评估次数。保存结果选用相同依赖版本和线程设置，概率/样本应可重复， +墙钟耗时会变化；不同操作系统或依赖版本可能改变优化轨迹。 +原始紧凑 JSON 可用 `.venv/bin/python -m json.tool 文件.json` 格式化查看。 +图中的点是各个种子的实际结果，不是统计置信区间。 + +## 8. 测试与代码质量 + +```bash +export OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg +.venv/bin/python -m pytest -c test/LabScheduling/pytest.ini test/LabScheduling --cov=pyqpanda-algorithm/pyqpanda_alg/LabScheduling --cov-report=term-missing +.venv/bin/python -m ruff check --config test/LabScheduling/ruff.toml pyqpanda-algorithm/pyqpanda_alg/LabScheduling test/LabScheduling pyqpanda-algorithm/example/LabScheduling +.venv/bin/python -m ruff format --check --config test/LabScheduling/ruff.toml pyqpanda-algorithm/pyqpanda_alg/LabScheduling test/LabScheduling pyqpanda-algorithm/example/LabScheduling +.venv/bin/python -m mypy --config-file test/LabScheduling/mypy.ini pyqpanda-algorithm/pyqpanda_alg/LabScheduling +``` + +测试遵循贡献指南的 `功能.test.py` 命名。上游实际测试采用 pytest 类/函数， +仓库未提供指南提到的 `Test(功能, 内容)` 辅助 API，因此采用可执行的 pytest +断言；`--import-mode=importlib` 处理含点文件名。使用应用独立 pytest 配置， +不改上游测试约定。测试中的 CPUQVM 是真实模拟器，不是 mock。 + +数学验证包括所有 6 比特/12 比特向量的约束能量恒等式、12 比特全部 Ising +对角值、与独立 NumPy 稠密 X-QAOA 演化的逐概率比较、XY 单激发不变量、 +位序、固定种子、单候选和空任务边界。另测试不可行、采样未命中、错误 JSON、 +非法参数和大小预算。新增 workflow 专门响应 develop 的相关 PR。 + +## 9. 限制和后续方向 + +- 无噪声 CPU 态矢量模拟；默认完整概率训练及诊断需要 $O(2^n)$ 内存/时间。 + 每个量子组件最多 16 个存活候选，编译器最多 256 个；两两建模 $O(n^2)$。 + `--reduce` 可分解严格独立的组件，但没有解决任意大连通问题。 +- 精确穷举 API 最多枚举 1,000,000 个组合，超限报错；报告中跳过超预算枚举， + 原始 MILP 最多 2048 个候选。报告对超预算 MILP 同样记录 `budget_exceeded`， + 保留实际求解结果；两种基线都无法认证时 `absolute_gap=null`，不冒充最优。 + 直接调用 `solve_milp` 超过上限仍报错。此应用面向小范围 + 冲突修复窗口；中等样例是本应用的 12 比特规模，并非工业级中等规模。 +- 限定非负整数成本、确定时长、单位设备容量、单小组归属；没有多容量资源、 + 跨天日历、随机故障预测或完整 IIS 最小不可行子集诊断。 +- 空候选是可解释的不可行原因；一般不可行性由穷举或 MILP 认证，不宣称是量子证明。 +- XY 的已知约束保持技术来自上游库，本项目创新在实验室重排建模、候选压缩、 + 扰动成本、可追溯约束审计和完整可复现应用，而非发明新 QAOA 理论。 +- 已实现单候选传播、严格分量分解、窗口候选导入和独立 MILP。 + 下一步是匿名实际数据验证、更多独立数据种子，以及有噪声/有限采样训练。 + +## 10. 来源与许可证 + +- [比赛主页](https://qcloud.originqc.com.cn/learning/zh/2026ccf)、 + [赛题 #13](https://github.com/OriginQ/pyqpanda-algorithm/issues/13)、 + [贡献指南](../../CONTRIBUTING.md)。 +- [PyQPanda3 AI 参考](https://github.com/OriginQ/pyqpanda3-skill)：CPUQVM、整数比特、 + PyQPanda3 API；实际接口均通过安装版本验证。 +- [上游 QAOA 组件](../../pyqpanda-algorithm/pyqpanda_alg/QAOA)：W 态、XY 交换及相位线路。 +- 去重记录及范围见根目录 [PLAN.md](../../PLAN.md)，相关 PR 比较见 + [调研快照](RESEARCH.md)。 + +新增内容沿用仓库 [Apache-2.0 许可证](../../LICENSE)。 diff --git a/Tutorials/LabScheduling/RESEARCH.md b/Tutorials/LabScheduling/RESEARCH.md new file mode 100644 index 00000000..3e70c398 --- /dev/null +++ b/Tutorials/LabScheduling/RESEARCH.md @@ -0,0 +1,34 @@ +# 选题调研快照（2026-09-21） + +基线 develop：`5f973efccb84bc193157d1ccebe32137e307293b`。 +阅读官方 README、main/develop CONTRIBUTING、QUBO/QAOA 模块、现有测试与 CI。 +检索源码中的 schedule / scheduling / 排程 / 排班 / 调度 / 实验室 / 考试， +没有发现已实现的同类应用。 + +GitHub 匿名 REST API 返回限流，网页工具的缓存计数与实时页面不同，故改为 +直接读 GitHub HTML：开放 PR 第 1–3 页，共 **70** 项；第 4 页为空。 +[完整标题与链接快照](research_pr_snapshot.json)保留可审阅清单。 +查阅关闭 PR 第 1 页（13 项），未发现同题。对相关 PR/Issues 进一步阅读正文。 + +| 对象 | 已有范围 | 对本应用的决策 | +|---|---|---| +| [#60](https://github.com/OriginQ/pyqpanda-algorithm/pull/60) | 小麦灌溉预测、土壤水量平衡、取水预算及 QUBO/QAOA | 同用调度技术但业务模型不同；选择设备故障、清洗、多时隙实验、前序和预约变更，避免农业/水预算方向 | +| [#45](https://github.com/OriginQ/pyqpanda-algorithm/pull/45) | QUBO_QAOA 默认 layer=None 导致崩溃 | 不重复修库，不依赖该封装默认参数 | +| [#69](https://github.com/OriginQ/pyqpanda-algorithm/pull/69) | QUBO 多项式规范化和高阶验证 | 直接编译二次系数、显式 Ising 索引，不依赖待合并修复 | +| [#40](https://github.com/OriginQ/pyqpanda-algorithm/pull/40) | QAOA 风险损失函数修复 | 应用使用标准期望能量，不修改 CVaR/Gibbs | +| [#38](https://github.com/OriginQ/pyqpanda-algorithm/issues/38) | QmRMR 导出、部分示例和文档问题 | 不扩大范围处理 | +| [#8](https://github.com/OriginQ/pyqpanda-algorithm/issues/8) | 旧硬件上的 QPanda3 应用案例 | 不涉及约束实验排程 | +| [#13](https://github.com/OriginQ/pyqpanda-algorithm/issues/13) | 比赛任务与提交流程 | 完整创新应用，PR 到 develop，规范标题 | + +网页主页初次读取只返回 JavaScript 提示。进一步读取该页面实际引用的 main +bundle 和 2026ccf 路由 chunk，核对可见文案：开源创新赛道不限制学生/社会人士， +采用 GitHub PR 提交，报名与 PR 窗口均为 4 月 10 日至 9 月 30 日； +[主页](https://qcloud.originqc.com.cn/learning/zh/2026ccf)指向 +[开源赛道详情](https://qcloud.originqc.com.cn/learning/zh/contest/list/60/contest:introduction)。 +没有登录或代填报名信息，不能确认用户的个人报名状态。 + +**结论：** LabReschedule 符合完整创新应用任务；在本次可见的开放 PR、Issue +及 develop 中，没有发现同题或高度相似的实验室重排应用。QUBO/QAOA 通用方法 +和 XY 技术本身不属于本项目原创。创新定位是场景约束组合、预约稳定性、候选 +压缩、可解释约束报告和端到端复现。不能凭检索证明全球新颖性。 +在用户批准发布之前还应刷新 PR 清单，以覆盖此快照之后的新投稿。 diff --git a/Tutorials/LabScheduling/REVIEW_GUIDE.md b/Tutorials/LabScheduling/REVIEW_GUIDE.md new file mode 100644 index 00000000..9f2659a3 --- /dev/null +++ b/Tutorials/LabScheduling/REVIEW_GUIDE.md @@ -0,0 +1,144 @@ +# 维护者与评委审阅路线 + +队伍 **youshen**。本应用处理设备故障后的实验预约重排：输入预约候选与约束， +在 CPU 上运行 QAOA，再独立核验排程。全部业务数据为合成，无真实用户试用声明。 + +## 五分钟主要演示路线（安装完成后） + +按[主教程](README.md#1-三分钟演示)安装后，在仓库根目录运行： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg .venv/bin/python pyqpanda-algorithm/example/LabScheduling/tradeoff_demo.py --sensitivity +``` + +三个有前序关系的实验共享带清洗时间的烘箱。故障后可将三单整体后移，成本 12； +也可支付备用设备费用，保留后两单，成本 14。三项决策全部留在 8 比特组件中， +没有预处理固定项。默认种子的实际量子样本得到成本 12，MILP/穷举随后认证。 +一般输入和其他参数不保证采到最优，脚本保留实际 gap，不能用经典解修补。 + +打开 `reports/tradeoff-demo/tradeoff.svg` 看原预约与采样结果； +`alternatives.csv` 列出全部 9 个可行排程；`outage-report.json` 含参数、counts、 +原始约束复核及证书。`ablation.json` 分别列出各阶段资源与验证耗时。 + +接着看 `sensitivity/summary.csv` 与 `sensitivity/sensitivity.svg`：每单改约成本 +从 0 增到 6，最优决策在 3 分处由整体后移切换为备用设备，阈值处两者同优。 +全部 21 次实际量子结果和七组完整可行排程都可核验；详见 +[权重与决策解释](EVALUATION_V2.md#改约成本敏感性什么时候值得使用备用设备)。 + +最后运行资源边界案例： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 .venv/bin/python -m pyqpanda_alg.LabScheduling pyqpanda-algorithm/example/LabScheduling/data/support-chain.json --reduce --pruning arc --output reports/support-chain.json +``` + +查看 `component_qubits=[12]`、成本 3、零违反和零 gap。去掉 `--pruning arc` +会留下 18 比特组件并返回资源限制，退出码 5。只运行这一受控边界例，便可说明 +精确化简带来的用途；不要将组件上限突破说成通用大规模量子调度能力。 +五分钟是演示目标，实测环境与计时见 VALIDATION.md，首次安装下载另计。 + +讲解顺序：约一分钟交代故障与两种方案，一分钟解释权重切换，一分钟展示 +18→12 的删除依据，再展示实际 counts、经典证书和失败状态。 +若现场未采到最优，展示实际 gap；不能以认证解替换量子样本。 + +## 核心代码阅读顺序（约 15 分钟） + +| 阅读顺序 | 文件 | 核查重点 | +|---|---|---| +| 1 | [problem.py](../../pyqpanda-algorithm/pyqpanda_alg/LabScheduling/problem.py) | 严格输入、整数成本、时间区间及单容量边界 | +| 2 | [model.py](../../pyqpanda-algorithm/pyqpanda_alg/LabScheduling/model.py) | 候选剪枝、禁止对只计一次、A 的充分界、独立解码 | +| 3 | [quantum.py](../../pyqpanda-algorithm/pyqpanda_alg/LabScheduling/quantum.py) | PyQPanda3 CPU、XY 单激发、相位简化、样本来源 | +| 4 | [reduction.py](../../pyqpanda-algorithm/pyqpanda_alg/LabScheduling/reduction.py) | 单候选传播、严格独立分量、原变量回填、超限状态 | +| 5 | [milp.py](../../pyqpanda-algorithm/pyqpanda_alg/LabScheduling/milp.py) | 从原始输入独立建模、限时不冒充最优 | +| 6 | [report.py](../../pyqpanda-algorithm/pyqpanda_alg/LabScheduling/report.py) | 求解后认证、独立语义检查、状态与 gap | + +数学证明集中在[主教程](README.md)和[增强教程第 2–4 节](ENHANCEMENTS.md)。 +可选[弧一致性化简](ARC_PRUNING.md)补强经典预处理；默认量子算法保持不变。 + +## 证据入口 + +- [无支持候选删除](ARC_PRUNING.md)：18→12 比特边界演示、200 个随机小问题与 + 全部 V2 输入的完整可行集合验证，以及局部有支持但全局不可行的反例。 +- [同总预算对照](MATCHED_BUDGET.md)：将总训练上限与 shots 对齐，新增化简后随机 + 基线。512 shots 下随机也全部采到最优；低 shots 条件曲线与退化实例同时公开。 +- [第二批协议](BENCHMARK_V2_PROTOCOL.md)：在评价前提交冻结；36 个问题，每格 4 个数据种子。 +- [第二批分析](EVALUATION_V2.md)：正常、不可行、纯经典和退化结果一并解释。 +- [逐实例表](results/v2/SUMMARY.md)：先看概览；再按实例/seed 查看 + [完整训练记录](results/v2/runs.jsonl)与[结构证明记录](results/v2/instances.jsonl)。 +- [关键测试](../../test/LabScheduling/enhancement.test.py)：100 个随机问题的可行集合等价、 + CPU 相位等价和 MILP 状态边界；[新增案例测试](../../test/LabScheduling/evidence.test.py) + 覆盖 36 个异构实例的无故障合法性和全部可行集合，以及完整业务取舍。 +- [验证记录](VALIDATION.md)：本地测试和 GitHub CI 分开标注。维护者批准前不能算上游 CI 通过。 +- [输入输出回归](../../test/LabScheduling/io.test.py)：拒绝输入别名覆盖，验证求解 + 数据与指纹一致，以及报告写入/替换失败时旧文件不被截断。 + +原始 JSONL 与生成数据占 diff 的大部分；可先审阅上表核心文件及测试，再抽查归档。 +归档保留输入和源码指纹、全部失败及计时，不需要逐行阅读优化历史。 + +新增的[安装兼容性说明](COMPATIBILITY.md)记录 3.11 / 3.12 / 3.13 的实际验证。 +兼容性轮次只调整安装约束、CI 和证据说明；后续预算修复见下方失败状态说明。 + +## 当前运行与历史归档如何核对 + +**验证当前代码是否可重复运行**：在同一 checkout、同一 `.venv` 中执行两次。 +若已运行上面的主演示，直接执行第二条即可： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg .venv/bin/python pyqpanda-algorithm/example/LabScheduling/tradeoff_demo.py --sensitivity +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg .venv/bin/python pyqpanda-algorithm/example/LabScheduling/tradeoff_demo.py --sensitivity --output reports/tradeoff-replay --verify-sensitivity reports/tradeoff-demo/sensitivity/results.json +``` + +第二条命令严格比较 21 条敏感性记录及其输入、源码和环境指纹，只排除 +`_seconds` 计时字段。成功退出表示这些记录一致；不表示不同 Python、依赖版本 +或机器上的优化参数和 counts 必然相同。比较对象是 `sensitivity/results.json`， +不是整份演示目录或 SVG。 + +**核验提交在仓库中的历史实验**：先选择归档对应的源码版本。后续修复会改变 +源码指纹，不能直接拿当前版本与旧归档严格比较，也不能删除指纹检查来“通过”。 +以下提交同时包含对应实现与归档；2026-09-22 已逐项核对归档记录的源码指纹： + +| 历史实验 | 包含匹配源码和归档的提交 | 匹配的源码/协议/配置指纹 | +|---|---|---:| +| 同总预算 360 条记录 | `a3716ed778ece61fcb7dfe13f18633f4c6164a62` | 17 | +| AC-3 结构证明与边界运行 | `4eb272a9f7c75167487a578752eb8f6fbda11c88` | 14 | +| 权重敏感性 21 条记录 | `384649588690fa3b100541b78a1ad82447df2388` | 17 | + +例如，已取得本 PR 的 Git 历史后，在仓库根目录创建一个独立检出： + +```bash +git worktree add --detach ../lab-sensitivity-archive 384649588690fa3b100541b78a1ad82447df2388 +cd ../lab-sensitivity-archive +python3.12 -m venv .venv +.venv/bin/python -m pip install -c pyqpanda-algorithm/example/LabScheduling/constraints-py312.txt -e ./pyqpanda-algorithm -r pyqpanda-algorithm/example/LabScheduling/requirements-dev.txt +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg .venv/bin/python pyqpanda-algorithm/example/LabScheduling/tradeoff_demo.py --sensitivity --output reports/archive-replay --verify-sensitivity Tutorials/LabScheduling/results/sensitivity/results.json +``` + +历史敏感性归档记录的解释器是 **Python 3.12.3**；这里的 `python3.12` 也须是该 +补丁版本，并使用归档约束。其他版本仍可做当前环境内的重复运行，但严格环境 +比较可能拒绝历史重放。指纹匹配是必要条件，不能代替实际执行比较器；历史完整 +重放的实测记录见 [VALIDATION.md](VALIDATION.md)。如果使用 ZIP 或浅克隆而没有 +这些提交，请先获取本 PR 的完整分支历史，再创建 worktree。 + +## 结论边界 + +可证明的是候选化简保持可行集合、固定成本回填、XY 恒零罚项删除的理想分布等价。 +经典基线在这些规模更快；分量预算相加，分量最大深度不代表实际并行耗时。 +360 个种子组合来自 36 个问题，不能当作 360 个独立业务实例，也不代表真实实验室部署。 + +## 答辩时需要回答的六个问题 + +| 问题 | 回答与可核验证据 | +|---|---| +| 为什么经典方法更快，还做量子调度？ | 本作品研究受限量子资源下如何形成可运行、可验证的调度子问题；没有商业性能或量子加速结论。同预算随机对照及失败记录均公开。 | +| 创新是不是只有把现成 QAOA 换个场景？ | 完整贡献包括最小扰动业务建模、可证明的候选化简和分量回填、可追溯成本/删除依据、独立原始输入认证与可复现流程。QAOA 和 AC-3 本身不是原创。 | +| 量子求解是否被经典最优解替代？ | 训练与采样先执行，最优证书随后计算；查看各组件 counts 与选中位串，再用原始输入检查器复核。纯经典传播明确单列。 | +| 18→12 比特说明什么？ | 在公开的构造案例中，可选 AC-3 将组件压到当前模拟预算内，四项决策仍在量子组件。V2 中只有 5/36 实例的最大组件缩小；不是普遍缩减比例。 | +| 业务成本有没有现实依据？ | 当前是可解释的合成偏好分。七档权重展示决策切换与并列最优；没有真实使用者反馈，不能称为经济收益测量。 | +| 没有最优证书、抽样失败或超过预算怎么办？ | 保留实际样本、违反数与明确状态；没有证书时 gap 为 null。MILP 超预算只跳过该认证路径，不丢弃已有结果，也不将结果标成最优。 | + +建议以“受限量子资源下的可审计故障重排工具”概括作品。评委无需阅读全部历史 +训练记录即可检查以上六点；原始证据保留供抽查。后续研究应由真实需求或实验 +结果驱动，不以不断增加功能代替验证。 + +已准备[小范围使用者试用流程](USER_TRIAL.md)及[空白反馈表](TRIAL_FEEDBACK_TEMPLATE.md)， +复用现有 CSV 导入和求解接口。真实参与者、匿名事件数据与反馈尚未取得；材料就绪 +不计为真实试用完成，也不改变以上证据边界。 diff --git a/Tutorials/LabScheduling/SENSITIVITY_PROTOCOL.md b/Tutorials/LabScheduling/SENSITIVITY_PROTOCOL.md new file mode 100644 index 00000000..a85774e4 --- /dev/null +++ b/Tutorials/LabScheduling/SENSITIVITY_PROTOCOL.md @@ -0,0 +1,27 @@ +# 改约成本敏感性分析：执行前协议 + +队伍 youshen，2026-09-21。沿用已知 `tradeoff.json` 开发案例，属于业务解释， +不是新留出评测，不用其结果选择新的默认量子参数。 + +1. 输入与参数网格冻结于 `example/LabScheduling/data/sensitivity.json`： + 基础输入 SHA-256、权重 0–6、seed 7/19/42，p=1、2 restarts、maxiter=60、 + 512 shots、XY、默认单候选化简，总计 21 次真实 CPU QAOA。 +2. 每次只把所有任务的 `change_cost` 改为同一非负整数 w；原始预约、候选、 + 偏好费用、设备、停机与前序全部保持不变。基础输入不原地修改。 +3. 先运行实际量子方案，再做独立认证和业务成本分析，不将经典方案传给量子路径。 + 所有种子保留，无可行样本时保留空业务指标和失败状态，不能用经典解填补。 +4. 对每个权重枚举全部原始候选组合，以独立原始语义检查器判断可行性和成本； + 跨权重核验完整可行排程集合一致，并与 QUBO 枚举和原始 MILP 最优值核对。 +5. 已知两个候选方案“整体后移”和“备用设备”的成本分别为 6+3w、12+w； + 交点为 w=3。全局最优值、阈值处全部并列最优仍须由完整枚举认证，不预设结论。 +6. 判定最优命中时接纳任意并列最优排程，不要求采样覆盖每个同优方案。 + 成本为业务偏好分，不是已调查的货币价格，不能把单个开发案例推论为一般优势。 +7. 输出七行决策表、全部九条可行排程的成本曲线（若实际数量不同，记录并排查）、 + 每次量子目标/改约数量/偏好与变更成本/gap/耗时、完整参数和 counts。 + 图表同时展示独立认证和实际量子结果，不把解析线当实际抽样成绩。 +8. 保存七个派生输入、源代码与协议/配置指纹、环境和 21 条原始记录。 + 再次执行并比较全部非计时字段；只忽略 `_seconds` 字段。 +9. 接入现有 tradeoff 演示的可选参数；正常调用保持原有行为。 + 测试覆盖 w=0、切换点、同优接受、故意非最优/不可行样本、输入变换和重放篡改。 + +完成这项限定增强后冻结新功能，继续收敛评审路线和发布候选验证。 diff --git a/Tutorials/LabScheduling/TRIAL_FEEDBACK_TEMPLATE.md b/Tutorials/LabScheduling/TRIAL_FEEDBACK_TEMPLATE.md new file mode 100644 index 00000000..f71e3e50 --- /dev/null +++ b/Tutorials/LabScheduling/TRIAL_FEEDBACK_TEMPLATE.md @@ -0,0 +1,54 @@ +# 实验室重排试用记录（未填写模板） + +> 空白项表示未知。复制到本地案例目录后填写；示例模板不能作为真实用户反馈。 +> 不填写姓名、联系方式、机构全称、课题名称或其他身份信息。 + +## A. 看求解结果前填写 + +- 案例代号： +- 日期： +- 数据来源：未确认（合成演练 / 真实事件去标识回放 / 其他并说明） +- 参与者代号与角色：未提供 +- 操作方式：未确认（参与者独立 / 队伍协助 / 仅工程师演练） +- 使用的代码提交号： +- 一时隙代表的分钟数、时隙 0 的含义： +- 停机情境、任务与设备数量： +- 必须满足的约束： +- 已知但当前模型无法表达的约束： +- 时间离散化是否准确、是否有取整或其他简化： +- 允许设备、候选窗口由谁确认： +- 改约成本 / 位移成本及选择理由（相对偏好分）： +- 当前人工处理方式与大致用时（无记录填“未测量”）： +- 看结果前的人工排法（无则填“未提供”；设备、时刻与任务对应）： +- 人工排法的改约数量；是否遵循相同候选与约束： +- 数据录入用时（未测量则保持未测量）： + +## B. 看结果后填写 + +- 首次命令退出码、报告 status 与 solver_status： +- 输入 SHA-256： +- 实际量子组件比特数 / 是否纯经典传播： +- 目标值 / 约束违反数 / absolute_gap（null 保持 null）： +- 程序运行时间（报告字段；不含录入、访谈）： +- 实际改约数量： +- 参与者逐项确认可执行：未确认（可执行 / 不可执行 / 无法判断） +- 不可执行或不确定的具体任务与原因： +- 原来未想到、现在发现的限制： +- 对比人工排法的意见；是否需要独立校验人工方案： +- 哪部分解释有帮助，哪部分难懂： +- 录入最费力或最容易出错的步骤： +- 是否愿意在类似事件中再次试用，理由：未询问 +- 修改了什么、为什么；后续案例目录（首次记录仍保留）： + +## C. 证据范围与后续行动 + +- 本记录填写者：未确认（参与者 / 队伍根据访谈整理） +- 参与者是否核对整理内容：未确认 +- 是否同意公开去标识摘要：未询问（默认不公开） +- 明确同意公开的内容范围与确认方式：未提供 +- 不同意见或负面结果： +- 建议的下一项改进及其原始依据： +- 工程师核查：未执行（模型适配、原始约束检查、人工方案可比性） +- 当前状态：材料准备 / 未开展真实试用 + +完成真实试用后仍应限定结论为该案例，不能由一次反馈推论一般优势。 diff --git a/Tutorials/LabScheduling/USER_TRIAL.md b/Tutorials/LabScheduling/USER_TRIAL.md new file mode 100644 index 00000000..71c36a81 --- /dev/null +++ b/Tutorials/LabScheduling/USER_TRIAL.md @@ -0,0 +1,149 @@ +# 一次小范围实验室试用 + +这份材料用于收集真实使用反馈。**目前仅完成合成数据演练，尚未开展真实试用。** +建议由队伍联系一位熟悉仪器预约的使用者或管理者，选择一次已经发生的设备停机， +离线回放当时的 3–5 项预约。参与者核验结果后再决定是否用于实际工作。 +一次试用只能说明该案例的适用性，不能证明一般性能或量子优势。 + +## 1. 先确定案例和评价方式 + +建议安装完成后预留约 20–30 分钟；这是安排建议，不是实测完成时间。 +开始前填写[反馈模板](TRIAL_FEEDBACK_TEMPLATE.md)，记录以下内容： + +- 用 T01、G01、R01 这类代号表示任务、小组和设备；保留真实约束，不填写姓名、 + 联系方式、课题名称或原始预约截图。身份对应表由参与者保留，无需交给项目。 +- 约定一个整数时隙代表多少分钟，以及时隙 0 的含义。时间不能精确落在网格上时， + 先记录模型不适配，不自行四舍五入后宣称排程可执行。 +- 记录停机、设备清洗、允许替代设备、小组冲突、工序先后及不可移动预约。 + 本模型只支持单位容量设备、单小组任务、不可拆分时长和给定候选。 + 技能匹配、多人交叉参与、耗材或容量大于 1 等未表达约束须在反馈中列出。 +- **看求解结果前**，记录参与者的人工排法、改约数量、考虑因素和大致用时， + 再填写允许窗口及改约/移动成本。若无法给出人工排法，填“未提供”，不补造基线。 +- 保留首次运行，包括不可行、未采到可行解和资源超限。修改窗口或权重时， + 建立 case-002 等新目录，写明原因，不覆盖首次记录，也不只保留成功案例。 + +改约成本是参与者给出的相对偏好分，不自动解释为货币或节省的工时。 +试用不以“必须优于人工”作为成功条件：发现一项真实遗漏约束也是有效反馈。 + +## 2. 创建本地工作副本 + +先按[安装说明](README.md#1-三分钟演示)准备项目 `.venv`，在仓库根目录运行： + +```bash +.venv/bin/python - <<'PY' +from pathlib import Path +from shutil import copyfile + +trial = Path("reports/user-trial/case-001") +trial.mkdir(parents=True, exist_ok=False) +source = Path("pyqpanda-algorithm/example/LabScheduling/data/bookings") +copyfile(source / "chain.csv", trial / "bookings.csv") +copyfile(source / "outage.json", trial / "calendar.json") +copyfile("Tutorials/LabScheduling/TRIAL_FEEDBACK_TEMPLATE.md", trial / "feedback.md") +print(f"已复制合成模板至 {trial}；请先确认数据来源，再填写反馈。") +PY +``` + +目录存在时命令停止，避免覆盖之前的数据。模板可以直接演练；原样运行始终属于 +**合成演练**。真实试用须用参与者确认的数据替换 CSV 和日历，修改案例名称，并在 +反馈中注明来源。`reports/` 已被 Git 忽略；它只防止普通误提交，不是访问控制。 + +CSV 保持首行与列序，使用 UTF-8 保存，数字填整数： + +| 字段 | 如何填写 | +|---|---| +| `id`, `group` | 唯一任务代号、小组代号；同组任务不能重叠 | +| `duration` | 实验持续时隙数，不包含设备清洗 | +| `original_resource`, `original_start` | 停机前的设备代号和开始时隙 | +| `allowed_resources` | 确实兼容的设备代号，以 `\|` 分隔 | +| `earliest`, `latest`, `step` | 允许开始时刻的含端点整数网格；固定时刻令两端相等 | +| `change_cost` | 改变设备或开始时刻时支付一次的偏好分 | +| `shift_cost` | 每移动一个时隙的偏好分，按绝对位移计费 | + +允许设备与时间网格的所有组合都会生成候选；若不同设备有不同时间窗口，或有 +设备专属费用，应改用[显式 JSON 候选](README.md#3-输入格式)，不能让 CSV 隐含 +不存在的限制。固定设备还须将 `allowed_resources` 限为该设备。 +`calendar.json` 填写 `horizon`、设备 `id/cleanup/downtime` 和 `precedence`。 +清洗占用设备但不占用小组，必须在 horizon 内；前序只等待实验完成，不等待清洗。 +CSV 最多展开 256 个原始候选，量子模拟上限是每个组件 16 比特。 + +## 3. 导入并运行 + +确认人工排法和权重已记录后，生成输入。文件以排他方式创建，重复转换须新建案例 +目录，避免悄悄覆盖输入： + +```bash +.venv/bin/python - <<'PY' +import json +from pathlib import Path +from pyqpanda_alg.LabScheduling.bookings import import_bookings + +trial = Path("reports/user-trial/case-001") +calendar = json.loads((trial / "calendar.json").read_text(encoding="utf-8")) +problem = import_bookings(trial / "bookings.csv", calendar) +with (trial / "input.json").open("x", encoding="utf-8") as stream: + stream.write(json.dumps(problem, ensure_ascii=False, indent=2, allow_nan=False) + "\n") +print("导入成功；候选数：", sum(len(task["options"]) for task in problem["tasks"])) +PY +``` + +一次 CPU 求解、原始约束复核及经典认证： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 .venv/bin/python -m pyqpanda_alg.LabScheduling reports/user-trial/case-001/input.json --reduce --seed 7 --output reports/user-trial/case-001/report.json +``` + +查看紧凑结果，让参与者逐项核验任务、设备和时间： + +```bash +.venv/bin/python - <<'PY' +import json +from pathlib import Path + +report = json.loads(Path("reports/user-trial/case-001/report.json").read_text(encoding="utf-8")) +quantum = report["quantum"] +best = quantum["best"] +print(json.dumps({ + "status": report["status"], + "solver_status": quantum["status"], + "objective": best["objective"] if best else None, + "violations": best["violation_count"] if best else None, + "schedule": best["schedule"] if best else None, + "absolute_gap": report["absolute_gap"], + "component_qubits": quantum["reduction"]["component_qubits"], + "runtime_seconds": report["runtime_seconds"], + "input_sha256": report["input_sha256"], +}, ensure_ascii=False, indent=2)) +PY +``` + +未修改的合成模板应得到目标值 6、零违反、gap 0，三个烘箱任务被传播固定，另有 +一个 4 比特量子组件。实际试用不预设目标值或最优命中。 + +| 状态 / CLI 退出码 | 如何记录和处理 | +|---|---| +| `feasible` / 0 | 模型内可行；仍须参与者确认未遗漏实际限制。gap=0 需有经典最优证书 | +| `infeasible` / 3 | 给定候选与约束不可行；保留原因，核对录入，不随意放宽硬约束 | +| `no_feasible_sample` / 4 | 有限量子样本没给出可行解，不等于问题不可行 | +| `resource_limit` / 5 | 组件超过 CPU 预算；记录规模，不截断候选后冒充同一问题 | +| 输入错误 / 2 | 按终端提示纠正格式；未生成有效报告时不记录为求解失败 | + +`classical_propagation` 表示全部由经典传播解决，不计为量子成功。 +gap 为 null 表示没有可用差距，不能填成零。程序运行时间不包含人工录入和访谈； +如果记录人工用时，须分别保留这些阶段，不能由一次演练得出效率优势。 + +## 4. 使用者复核与可交付证据 + +请参与者先独立判断能否执行，再阅读解释和经典证书，并填写反馈模板。人工排法 +只有在同一候选、约束和成本定义下才适合比较；否则写明不可直接比较，不填收益比例。 +若需定量核对人工排法，交由工程师使用现有 `validate_assignment` 对原始候选索引 +独立检查，检查前只报告参与者的定性意见。 + +一次案例保留 `bookings.csv`、`calendar.json`、`input.json`、`report.json`、 +`feedback.md` 及实际代码提交号（`git rev-parse HEAD`）。输入指纹已在报告中； +反馈标明是独立使用还是由队伍协助，记录失败、不同意见和缺失约束。 +默认不公开真实试用材料。参与者明确同意公开的范围与去标识摘要单独记录后， +才能将其作为参赛证据；对外联系与公开由队伍协调，不由本流程自动发送。 + +没有实际参与者的数据或反馈时，将状态保持为“材料准备完成 / 合成演练通过”。 +即使真实试用成功，也只描述这一案例和参与者意见，不称为部署、普遍收益或量子加速。 diff --git a/Tutorials/LabScheduling/VALIDATION.md b/Tutorials/LabScheduling/VALIDATION.md new file mode 100644 index 00000000..a66f9e2e --- /dev/null +++ b/Tutorials/LabScheduling/VALIDATION.md @@ -0,0 +1,473 @@ +# 验证与复现记录 + +本文前半保留初版历史记录；当前增强版状态与验证见文末。 + +日期：2026-09-21。基线：`upstream/develop@5f973ef`。 +环境：WSL2 Linux x86_64，Python 3.12.3，PyQPanda3 0.4.1， +pyqpanda_alg 2.0.0（本地源码），NumPy 2.5.3，SciPy 1.18.1。 +完整包版本见 [constraints-py312.txt](../../pyqpanda-algorithm/example/LabScheduling/constraints-py312.txt)。 + +## 上游基线 + +1. 官方 requirements 安装后，初始 `pytest test` 因未安装 Allure 插件拒绝 + `--alluredir` 参数；`import pyqpanda_alg` 也因未声明的 pandas 依赖失败。 +2. 依据实际导入和测试所需，在应用专属 requirements-dev 中补充 + pandas/scikit-learn/allure-pytest。没有修改全局 Python 或上游依赖文件。 +3. `test/pytest.ini` 没有 `[pytest]` 节，当前 pytest 9 仍能读取；使用独立配置 + 固定本次收集规则。有效测试是 **18 项**；其他多个文件主体为注释，不能算作测试通过。 +4. 独立运行原有测试得到 18 passed（35.99 s）。之后并发模拟压力下的重复检查 + 触发一个 20 秒超时且产生线程争用，该轮中止，不将它当成功结果。 + 固定 OMP/BLAS 单线程后完整复核 **18 passed，9.08 s**。 + +最终基线命令（在仓库根目录）： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg .venv/bin/python -m pytest \ + -c test/LabScheduling/pytest.ini \ + test/QAOA test/QAlgBase test/QPCA test/QSVM test/QARM \ + -o 'python_files=Test_*.py' --timeout=60 -q +``` + +## 新增应用测试 + +**56 passed，覆盖率 98.46%（455 条可执行语句，7 条未命中）**。 +`model.py` 和 `classical.py` 行覆盖为 100%。命令见主教程第 8 节； +聚焦 CI 设置 95% 最低覆盖率，保留真实量子模拟测试。 + +- 对 simple 的 64 态、medium 的 4096 态逐一验证 + `QUBO = 原始成本 + A*(恰选一次平方罚分 + 冲突对数)`。 +- 对 medium 全部 4096 态验证 Ising 对角值与 QUBO 能量，覆盖 qubit 10/11， + 避免 `x10` 字符串排序把映射改变。 +- 对真实 CPUQVM 做非对称位序测试，对 X 线路做独立 NumPy 态矢量交叉验证。 +- 对不同层数和角度验证 XY 域内单激发守恒，以及零角度均匀 one-hot 概率。 +- 同 seed 重复求解验证参数、counts、最佳样本一致；最佳结果必须出现在 counts。 +- 分别验证语义不可行、空域、零任务、单候选、同对多原因冲突、清洗与停机边界、 + 原预约变更成本、参数错误、重复键/未知字段、NaN 和规模保护。 +- 使用可行 simple + X + 1 shot 的真实模拟验证“未采到可行解”与“问题不可行” + 的区别，并验证 CLI 退出码 0/2/3/4。 + +覆盖工具注意：`--cov` 传**源码目录路径**。本机 coverage 对包含二进制导入链的 +包名预加载触发 NumPy 重复导入错误；路径模式正常工作。没有为通过覆盖率而 +mock 量子库。覆盖率未包含子进程执行的 CLI 分支，实际子进程命令另有断言验证。 + +## 代码质量与安装 + +- Ruff：E/F/I/UP/B 检查通过，格式检查通过，配置范围仅新增应用和测试。 +- Mypy：7 个模块检查通过；仅跳过 PyQPanda3 不完整的二进制导出类型定义。 +- `pip check`：无依赖冲突。 +- 构建 wheel 成功；用 `--no-deps --target .venv/wheel-check` 安装到项目内目录， + 从该安装目录导入运行 medium，得到目标值 4、0 违反、差距 0。 +- `git diff --check`、新增 Python 公开函数标注/文档字符串、相对文档链接、 + 缓存/密钥/旧 pyqpanda API 检查在最终提交前完成。 +- 新增 GitHub Actions workflow 的等价命令已本地运行；远程 Actions 尚未触发， + 不声称已经通过 GitHub CI。 + +## 多规模、多种子实验 + +统一预算：p=1，shots=512，restarts=2，COBYLA maxiter=60；种子 7、19、42。 +采用 `OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1`，CPU 无噪声模拟。 +按独立函数定义分别计时，`quantum.runtime_seconds` 包含量子训练、采样及诊断， +`exact.runtime_seconds` 包含全组合枚举和原始约束检查，不含公共建模和解释器导入。 +`report.runtime_seconds` 包含读入、建模和三种求解流程，也不含解释器导入。 +这些指标不是硬件量子执行耗时。 + +| 实例/方法 | seed 7 可行概率 | seed 19 | seed 42 | 最佳目标（对应三种子） | +|---|---:|---:|---:|---| +| simple / XY | 0.384406 | 0.688144 | 0.420494 | 5 / 5 / 5 | +| simple / X | 0.013176 | 0.013175 | 0.013176 | 5 / 5 / 5 | +| medium / XY | 0.393214 | 0.221515 | 0.130439 | 4 / 4 / 4 | +| medium / X | 0.002105 | 0.002105 | 0.002105 | 8 / 18 / 13 | + +全部上述最佳样本可行，违反数为 0；X 在 medium 的最优差距为 4/14/9。 +XY 在 simple 的模拟求解计时约 0.10–0.11 s，medium 约 0.30–0.34 s； +实际端到端还需解释器导入和建模。具体逐次耗时见 +[summary.csv](results/summary.csv)，不是稳定性能阈值。 + +同预算均匀 one-hot 抽样在所有这些小实例也命中经典最优值。 +XY simple 两个种子的可行概率低于均匀 one-hot，不能称为普遍优势。 +三个种子不足以推导统计显著性或可扩展性；精确基线在此规模明显更快。 +可行概率提升展示约束编码的作用，不能推出实用量子加速。 + +[每次完整 JSON](results/)包含输入 SHA-256、环境、QUBO 系数/映射、剪枝与 +冲突原因、counts、优化历史、终止状态、排程、精确认证和均匀基线。 +所有种子均纳入结果，没有以最佳种子替代均值。 + +默认一层线路资源也经 PyQPanda3 `QProg.depth()` / `count_ops()` 实测： + +| 场景/混合器 | 原生门深度 | CNOT 数 | +|---|---:|---:| +| simple / XY | 36 | 34 | +| simple / X | 23 | 22 | +| medium / XY | 45 | 78 | +| medium / X | 32 | 54 | + +这些数值含初态、相位与混合器，未做真实芯片映射，不代表硬件编译后资源。 + +最终完整重放：再次运行 12 组实验，将每份 JSON 递归删除 `runtime_seconds` +后与归档比较，全部完全一致（包括参数、概率、counts、排程和诊断）。 +最终再次 fetch 的 develop 仍为 `5f973ef`；刷新开放 PR 仍为 70 项，无新增标题。 + +## 增强版验证（2026-09-21，已更新 PR #92） + +以上 56 项和 12 份结果为增强前的历史基线。当前新增模块后实测： +**71 passed，98.44% 行覆盖率（771 条语句，12 条未命中），6.11 s**； +原有有效测试再次运行 **18 passed，8.22 s**。Ruff lint/format 全部通过， +Mypy **11 个模块**通过。`bookings.py`、`reduction.py`、`milp.py` 在本轮覆盖为 100%。 +覆盖率是辅助指标；核心证据是独立建模、可行集合等价和真实 CPU 线路测试。 + +- 100 个随机小问题：对全部可行排程集合进行化简/回填一一比对，验证固定成本， + 原始输入 MILP 的可行性及最优值与穷举一致。 +- simple/medium 每例 30 组随机参数，p=1/2/3，full 与 auto 相位概率误差 <1e-10； + 另测单候选混合域，X 分支保持完整相位。未声称两个优化轨迹必然一致。 +- 业务 CSV：故障重排成本 6、3 张改约、0 违反，量子组件 4 比特；无故障对照成本 0。 +- `--reduce` 最大组件资源上限实测退出 5，量子评价次数 0；没有静默经典替换。 +- MILP 限时状态、带 incumbent 的限时、错误/非整数 incumbent 等防御逻辑单独测试； + 正常求解使用真实 HiGHS。故障返回模拟仅用于不易稳定触发的状态分支测试。 +- 12 个新合成评价实例 × 10 个优化种子全部保存；9 可行、3 不可行，两经典基线一致。 + 120 个已训练参数上的 full/auto 最大概率差 2.220446049250313e-16。 + 整次评测约 49.06 秒；峰值进程 RSS 269832 KiB，包含 Python、依赖、MILP 和模拟器。 + 这不是单个线路内存或硬件耗时承诺。 +- 两次完整 120 训练的参数、counts、分布指标及独立抽样结果在排除耗时后完全相同。 + 所有输入/核心源码哈希与归档 provenance 一致，最佳位串均真实出现在 counts。 +- 公开 Python API 的参数/返回类型及 docstring 经 AST 扫描无遗漏。 + +原始实验记录与源代码指纹：[增强结果目录](results/enhanced/)。 +评测协议在首次评价前冻结，详见 [BENCHMARK_PROTOCOL.md](BENCHMARK_PROTOCOL.md)。 + +远程状态说明：用户批准后，增强提交 `9c23ddb` 已推送至 PR #92，完整 PR 描述已更新。 +对应 [Actions 运行](https://github.com/OriginQ/pyqpanda-algorithm/actions/runs/35576581365) +状态为 `action_required`，jobs 数量为 0，等待上游维护者批准。 +此前“尚未触发”表述仅适用于首次本地验证时；没有远程 CI 通过的证据， +本地测试不可替代上游审核。后续发布记录提交不改变应用代码及本地验证结论。 + +### 增强版全新 wheel 安装复核 + +使用 `.venv/bin/python -m pip wheel --no-deps --no-build-isolation ./pyqpanda-algorithm` +构建 wheel，然后在项目内新建 `.venv/enhanced-clean`，按相同 constraints 和 +requirements-dev 安装 wheel 及依赖。确认导入来自该新环境的 site-packages， +而非 editable 源码；`pip check` 无依赖冲突。 +新环境中 **71 项测试通过（4.70 s）**，CSV 连锁改约/无故障对照与 medium CLI +均成功，分别保持成本 6/0/4，0 约束违反。 +最初尝试 `python -m build` 时环境未安装 build，改用现有 pip 的 wheel 构建流程， +没有全局安装包或跳过构建验证。 + +## 第二批评测与审阅优化（2026-09-21） + +冻结提交 `94efa84` 先于评价执行。新增 36 个异构实例/360 条配对种子记录， +以及三项预约决策全部留在 8 比特组件的业务演示；核心应用源码未修改。 + +- 当前完整应用测试 **76 passed，6.95 s**，覆盖率 **98.44%**； + 包含全部 36 个输入的控制排程、可行集合/成本证明和独立 MILP 一致性。 +- Ruff lint/format（29 文件）、Mypy（11 个核心模块）、pip check 全部通过。 +- 已有纯 wheel 隔离环境再次运行当前 76 项测试：**76 passed，5.45 s**； + 新业务演示实际得到成本 12、零违反，全部决策仍由量子组件求解。 + 因本轮核心包未修改，复用已验证 wheel，不将示例修改称作新算法版本。 +- 第二次完整重放只剔除键名以 `_seconds` 结尾的时间字段后，36 份实例结构记录、 + 360 份配对训练记录完全一致；源码/输入哈希均匹配，实际最佳位串来自 counts。 +- 业务案例全部 9 个可行选择公开；高价备用设备方案成本 14，三单后移成本 12。 +- 新增独立 fork 的分支 push CI 入口，运行与 PR 相同的 Python 3.12 安装、质量检查、 + CPU 测试和三类演示；其结果与上游维护者批准状态分开记录。 + +结果入口：[异构评测分析](EVALUATION_V2.md)、[审阅路线](REVIEW_GUIDE.md)。 +个人 fork 的 [Actions 35583744393](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35583744393) +已在提交 `198ddfb` 上返回 **success**：Ubuntu / Python 3.12 新安装、Ruff/Mypy、 +76 项真实 CPU 测试、medium CLI、CSV 连锁改约及新增取舍演示均通过。 +官方 PR 对应运行 `35583752436` 仍为 `action_required`，等待上游维护者批准。 +fork CI 成功不等于上游 CI 成功或 PR 合并；后续补录记录仅修改文档， +不改变已验证代码、数据、测试和工作流。 + +## 同总预算补充评价（2026-09-21） + +- 协议先以 `eb9c5c3` 冻结；运行实现 `b140dab`。核心包与旧归档均未修改。 +- **82 passed，7.86 s，核心行覆盖率 98.44%**；Ruff lint/format（32 文件）、 + Mypy（11 个核心模块）、pip check 均通过。 +- 重新运行全部 36 实例 × 十种子，四条路径保留原始 counts 与状态；总训练评价 + 上限 120、采样上限 512。首轮 90.81 s，第二轮 90.55 s，非计时字段完全一致。 +- 第二轮 360 条直接 QAOA 记录还与旧 V2 归档一致（仅忽略计时），没有修改旧基准。 +- 两次运行源码/输入/环境指纹一致，归档前源码哈希匹配，采样来源与预算上限复核通过。 +- 22 个仍需量子的可行实例中：直接/分量 QAOA 最优 217/220、220/220； + 两个随机对照均为 220/220。低 shots 条件曲线包含分量 QAOA 低于随机的实例。 +- 新增测试涵盖预算守恒、不足时停止、单分量与直接路径完全一致、分量回填、 + 实际 CPU 样本来源、不可行域和重放比较器拒绝篡改 counts。 + +完整解释与复现命令见 [MATCHED_BUDGET.md](MATCHED_BUDGET.md)，原始记录见 +[results/matched](results/matched/)。此处本地验证不代替当前提交的远程 CI。 + +本轮发布提交 `a3716ed` 的独立 fork +[Actions 35586823475](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35586823475) +实际返回 **success**：Ubuntu / Python 3.12.14 全新安装，Ruff/Mypy， +**82 passed，16.32 s，覆盖率 98.44%**，三类演示全部通过，取舍示例成本 12。 +上游运行 `35586829560` 仍为 `action_required`。最终补录仅修改文档， +保留该成功运行验证过的代码、测试与数据不变。 + +## 输入快照与报告完整性修复（2026-09-21） + +本轮在临时目录先复现两处实际问题：`--output` 同输入时覆盖原文件；输入在 +求解后被修改时，报告哈希对应新文件而非求解数据。改为读取一次原始字节快照， +同一快照解析与哈希；`parse_problem(text)` 复用严格 JSON 校验。CLI 在求解前 +检查同路径/符号链接/硬链接，报告写完同目录临时文件后才替换目标。 + +- 本地 **92 passed，9.39 s，核心覆盖率 98.87%**；新增十项 I/O 回归。 +- Ruff lint/format（33 文件）、Mypy（11 模块）、pip check 全部通过。 +- 原有 **18 passed，7.75 s**。首次误用应用 `*.test.py` 规则未收集到上游测试； + 显式采用上游 `Test_*.py` 规则后执行，未把空收集计为通过： + + ```bash + OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 .venv/bin/python -m pytest -c test/LabScheduling/pytest.ini -o 'python_files=Test_*.py' test --ignore=test/LabScheduling --timeout=60 + ``` + +- 重新构建 wheel 并强制安装到已有 `.venv/enhanced-clean` 隔离环境，确认实际 + 导入 site-packages。**92 passed，5.60 s**，取舍演示成本 12、最优差距 0。 +- 写入/替换故障用 I/O 层故障注入验证；数据修改/删除测试仍运行真实 CPU 求解。 + CRLF 原字节哈希、有效输出符号链接保留、临时文件清理也已检查。 + +量子线路、优化器、QUBO 与冻结实验数据未改。旧归档中的源码指纹保留为对应 +历史提交的记录（同预算归档为 `a3716ed`），不重写成修复后的源码指纹。 +普通写入失败保护不等于断电持久性或多写入进程事务保证，接口说明见主教程。 + +发布提交 `36adc9f` 的个人 fork +[CI 35588841891](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35588841891) +全部通过：全新安装、质量检查、**92 passed，17.03 s，覆盖率 98.87%**、 +三类 CPU 演示成功，取舍样本与证书仍为成本 12。上游 `35588846744` 仍待 +维护者批准；最后补录 CI 的提交仅改变文档。 + +## 可选无支持候选化简（2026-09-21） + +范围与边界数据先在 `086840d` 提交；默认模式保留，增加 `--reduce --pruning arc`。 +实现采用确定性 AC-3，记录完整对方域及逐对冲突原因，不增加依赖或新量子变体。 + +- 本地 **102 passed，13.91 s，核心覆盖率 98.92%**（831 行，9 行未覆盖）； + reduction.py 的 128 条可执行语句全部覆盖。 +- 200 个固定种子小问题、全部既有 36 个 V2 输入和一个专门构造的边界案例， + 逐一核对原始语义全部可行集合、成本、回填映射与删除依据。 +- 40 个输入的默认化简审计与上一发布 `f64a7ad` 完全相同；旧归档未改。 +- 18 比特单组件降至 12 比特，无预先固定项；seed 7/19/42 均实际采到最优成本 3。 + V2 中仅 5/36 最大组件缩小，全部结果与无收益/不可行实例保留。 +- 第二次完整评价与第一次所有非 `_seconds` 字段完全一致；14 项源码/协议指纹 + 和全部 37 项输入指纹核对通过。没有用旧归档源码指纹替换当前指纹。 +- Ruff lint/format（35 文件）、Mypy（11 核心模块）、pip check 通过。 +- 原有 **18 passed，8.75 s**，使用此前记录的 `Test_*.py` 收集方式。 +- 最新 wheel 构建并重新安装至项目内 `.venv/enhanced-clean`，确认来自 site-packages； + 最终完整 **102 passed，9.00 s**，包含新增模式真实 CPU 演示与全记录重放。 + wheel SHA-256：`d4cd80c87c604e0cedaf48b93e5200564e601d7e579b3658a5f044be7d2237aa`。 +- 增加端到端 CLI 的聚焦 CI 步骤；源码/数据路径、相对文档链接、公开函数注解与 + 文档字符串、Git diff 均已检查。 + +新评价脚本在初次运行中暴露输出目录未创建、内存 tuple 与 JSON list 比较问题， +均在归档前修复；新增命令级真实 CPU 重放测试，最终记录来自修复后的两次完整运行。 +数学证明、经典算法出处、局部一致性不保证全局可行的反例及复现命令见 +[ARC_PRUNING.md](ARC_PRUNING.md)。新模式的远程 CI 状态在发布后另行记录。 + +弧一致性版本 `4eb272a` 的个人 fork +[CI 35597416071](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35597416071) +全部成功：Ubuntu / Python 3.12.14 全新安装、Ruff/Mypy，**102 passed，19.82 s， +覆盖率 98.92%**，中等实例、连锁改约、成本取舍及新增资源边界四类 CPU 示例均通过。 +官方运行 `35597421975` 仍待维护者批准。随后补录 CI 的提交只修改文档， +保留被远程验证的核心代码、测试、数据与工作流。 + +## 决策权重敏感性与发布候选（2026-09-21） + +参数协议 `5ebcdc3` 在评价前提交；实现 `51a5067`。原核心模块、默认权重和 +量子参数均未修改，只在现有 tradeoff 演示增加可选 `--sensitivity`。 + +- 权重 0–6、seed 7/19/42，共 21 次真实 CPU 求解，均采到认证最优、零违反； + 各权重完整原始可行集合均为相同的九个排程。w=3 处两个方案同优，代价 15。 +- 全部输入、参数、counts、成本、状态及源码/环境指纹二次重放一致，仅忽略耗时。 + 17 项源码/协议/配置指纹与七个派生输入哈希核对通过。 +- 本地 **108 passed，31.90 s，核心行覆盖率 98.92%**。 + 新增测试包含真实 21 次运行与重放、全部同优接受、非最优/无可行样本保留、 + 权重零边界、成本变换不修改原输入，以及拒绝被改变的样本和源码指纹。 +- Ruff lint/format（37 文件）、Mypy（11 核心模块）、pip check 通过。 +- 创建 `51a5067` 的干净 detached worktree，在其中新建 `.venv`，按锁定依赖 + 安装 wheel 和开发依赖；确认从 site-packages 导入，11 个安装核心文件与 + 源码逐个 SHA-256 相同。**108 passed，18.78 s**，pip check 通过。 + 同一干净环境下原有 **18 passed，9.35 s**。 +- wheel SHA-256：`4bc41141574a11b949ca01256d707429317f5122f5da167d86a89247c61bfbac`。 +- 安装完成后的命令级演示计时（含解释器启动）：tradeoff + 21 次敏感性 **6.98 s**， + arc 资源边界 **2.40 s**，原模式资源限制 **1.82 s**，不可行案例 **2.25 s**； + 退出码依次为 0/0/5/3。总计约 13.45 s，满足本机五分钟演示目标。 + 首次依赖下载另计，不将本机时间推广为跨机器性能承诺。 + +发布候选的命令、提交、导入位置与计时见 +[release-check.json](results/sensitivity/release-check.json)。审阅路线已收敛到 +REVIEW_GUIDE.md；权重说明和决策表并入原 EVALUATION_V2.md，没有新增平行教程。 +完整实验保留为约 221 KB 的紧凑 JSON，便于减少生成数据的 diff 行数。 + +发布提交 `3846495` 的个人 fork +[CI 35600289671](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35600289671) +全部成功：Ubuntu / Python 3.12.14 全新安装、Ruff/Mypy、**108 passed，43.86 s， +覆盖率 98.92%**，全部 CPU 演示通过，包含新增的 21 次敏感性运行。 +官方 `35600294948` 仍为 `action_required`，等待维护者批准。随后仅补录文档， +不改变该成功运行验证过的代码、数据、测试和工作流。 + +## 第七轮：Python 3.11 / 3.13 安装兼容性（2026-09-21） + +在 `a27d784` 的两个干净 detached worktree 中各建项目 `.venv`，安装上一轮同一 +wheel，SHA-256 仍为 `4bc41141574a11b949ca01256d707429317f5122f5da167d86a89247c61bfbac`。 +实际导入来自 site-packages，11 个核心模块与 checkout 逐文件字节相同。 + +- Python 3.11.16：**108 passed，20.46 s，核心覆盖率 98.92%**；上游 **18 passed，10.86 s**。 +- Python 3.13.11：**108 passed，22.79 s，核心覆盖率 98.92%**；上游 **18 passed，11.76 s**。 +- 两个环境 `pip check` 通过，依赖逐项符合各自约束；medium 实际样本目标 4、 + AC-3 边界目标 3，零违反、零 gap；取舍与 21 次敏感性演示通过，超限/不可行 + 命令分别返回 5/3。计时为本机记录，部分检查并行进行，不作性能比较。 +- 原 3.12 约束在 3.11 安装预检失败；新增 `constraints-py311.txt` 仅更换 NumPy、 + SciPy、ContourPy、Sphinx 四个不支持 3.11 的固定版本。3.13 沿用原文件。 +- 首次按包名统计覆盖率在两个环境均发生 9 项收集错误；改用 wheel 安装目录 + 作为覆盖率来源后完整通过。错误、处理方法和环境边界保留于 + [兼容性说明](COMPATIBILITY.md)及[机器记录](results/compatibility.json)。 + +CI 扩展为三个 Python 版本，Mypy 按各目标版本检查，增加上游测试及两个失败 +状态的显式核验。核心算法、示例、旧实验归档与 3.12 约束文件均未修改。 +本地 Ruff 检查和 37 文件格式检查通过；Mypy 在新增两个目标版本均通过。 + +发布提交 `5678e41` 的个人 fork +[三版本 CI 35604218576](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35604218576) +已全部成功： + +| Ubuntu Python | 应用测试 | 原有算法测试 | 核心覆盖率 | +|---|---|---|---| +| 3.11.16 | 108 passed，30.62 s | 18 passed，12.44 s | 98.92% | +| 3.12.14 | 108 passed，24.22 s | 18 passed，8.97 s | 98.92% | +| 3.13.15 | 108 passed，38.30 s | 18 passed，14.86 s | 98.92% | + +三项任务的安装、pip check、Ruff/Mypy、全部成功演示和两个失败状态检查均通过。 +3.11 另报告 15 项上游文档字符串转义的弃用警告；没有测试失败，未扩展为无关修复。 +官方 [35604226057](https://github.com/OriginQ/pyqpanda-algorithm/actions/runs/35604226057) +仍为 `action_required`，待维护者批准。随后提交仅记录这些结果，未改变矩阵验证过的 +代码、约束文件、测试或工作流。 + +## 第八轮：报告预算边界与最终审查（2026-09-21） + +复现输入含 2050 个原始候选、日历剪枝后仅 2 比特，原报告在量子求解后因 +MILP 的 2048 原始候选上限抛错，导致可用结果无法保存。实现提交 `1a54465` +在报告层预检独立基线预算，超过则记录 `budget_exceeded`、变量数/上限以及空证书。 +直接 `solve_milp` API 的超限异常保持不变；量子算法与默认参数未修改。 + +- 新增五项回归：2048/2049 分界、双认证路径超预算、日历空域不可行及 CLI + 保存结果。修复前 **4 failed、1 passed**，修复后 **5 passed**。 +- 完整应用 **113 passed，30.79 s，核心覆盖率 98.92%**。 +- simple、medium、infeasible、tradeoff、support-chain 五条既有路径重放， + 全部非计时报告字段与修复前一致；未仅比较目标值，也未替换实际采样结果。 +- Ruff lint/format（38 文件）、Mypy（11 模块）通过。 + +历史实验文件保持原样。此次 report.py / milp.py 源码指纹发生变化；严格对照 +历史归档需使用其记录对应的提交和环境，不能忽略指纹差异假称同一构建重放。 +当前版本同环境的独立重复运行仍由完整测试中的重放检查覆盖。 + +从 `1a54465` 建立干净 worktree、新建项目 `.venv`、安装新 wheel 与固定依赖， +确认 site-packages 导入和 11 个核心模块字节一致。完整 **113 passed，41.26 s， +覆盖率 98.92%**，原有 **18 passed，10.47 s**；pip check、medium、取舍及 21 次 +敏感性运行、AC-3 和两个失败退出状态全部通过。wheel 指纹、各命令与结果见 +[final-review.json](results/final-review.json)。 + +发布提交 `e89c78b` 的个人 fork +[CI 35606032579](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35606032579) +三版本全部成功，安装、质量检查、全部成功/失败演示均通过： + +| Ubuntu Python | 应用测试 | 原有算法测试 | 核心覆盖率 | +|---|---|---|---| +| 3.11.16 | 113 passed，37.00 s | 18 passed，14.15 s | 98.92% | +| 3.12.14 | 113 passed，30.63 s | 18 passed，9.24 s | 98.92% | +| 3.13.15 | 113 passed，20.77 s | 18 passed，8.04 s | 98.92% | + +3.11 仍有相同的 15 项上游文档字符串弃用警告。官方 +[35606039667](https://github.com/OriginQ/pyqpanda-algorithm/actions/runs/35606039667) +仍待维护者批准运行；PR 为 Open、可合并，无评论或评审。后续仅补录验证记录， +不更改被验证代码、数据、测试与工作流。 + +## 评委入口与重放说明核查（2026-09-22） + +本轮仅调整文档，核心代码仍与 `e89c78b` 的三版本 CI 相同，不新增测试数量或 +覆盖率声明。以 `0a524ce` 的代码、现有 Python 3.12.3 项目环境运行两次 +`tradeoff_demo.py --sensitivity`，分别输出至本地忽略目录 +`reports/reviewer-audit/run-1` 和 `run-2`；第二次通过 `--verify-sensitivity` +比较第一次记录。两命令退出码均为 0，各包含 21 次敏感性求解，所有非计时字段 +严格一致；主案例均为成本 12、gap 0、8 比特、无固定变量、9 个可行备选。 + +对审阅指南列出的三个历史提交，通过 `git show COMMIT:PATH` 获取文件字节并 +重新计算 SHA-256，与各自原始归档中的源码指纹逐项比较：同预算 17 项、AC-3 +14 项、敏感性 17 项全部一致。本轮未重新执行历史环境的完整量子实验，历史 +重放结果仍以各原轮次记录为准。没有覆写旧归档，也没有忽略源码或环境差异。 + +文档本地链接及 Git diff 检查通过。PR #92 仍开放、可合并，无评论或评审； +官方运行 `35606039667` 仍为 `action_required`。平台作品回执尚未核验。 + +## 试用材料的合成演练（2026-09-22） + +按 USER_TRIAL.md 原样执行四个 bash 命令块：创建本地案例、导入 CSV、运行 +已有 CPU CLI、读取报告摘要，退出码全部为 0。沿用未修改的合成 chain.csv +和 outage.json，展开 10 个候选；报告目标值 6、零违反、gap 0，三项任务由 +传播固定、一个 4 比特组件实际执行量子求解，输入字节与报告 SHA-256 一致。 +命令日志保留在本地忽略目录 reports/trial-material-validation。 + +重复执行初始化和导入命令均因文件已存在而停止，案例目录所有文件字节保持不变。 +模板反馈仍为“未开展真实试用”，未填入虚构参与者或意见；案例与反馈目录经 +git check-ignore 确认不进入普通提交。本轮仅新增文档，未更改算法或现有测试， +不扩大既有 113 项测试和三版本 CI 的验证范围。 + + +## 原始日历空域的构造预算检查（2026-09-22） + +独立 MILP 现在先判断每项任务是否有日历可用的原始选项。若没有，报告明确 +标出任务和原因、constraints=0，不调用 HiGHS；这与一般约束不可行的求解器 +认证路径区分开。6 个回归覆盖停机、清洗、半开区间边界、保留可用候选，以及 +512 任务 CLI 报告（退出 3，gap=null，量子线路调用 0）。 + +新 wheel 在项目内独立 `.venv` 安装，确认 site-packages 导入与全部 11 个核心 +模块字节一致；**137 passed，32.30 s**（119 应用 + 18 原有测试），核心覆盖率 +**98.93%**（838 行、9 行未覆盖）。Ruff/格式/Mypy/pip check 通过。中等实例、 +主取舍案例及 21 次敏感性、AC-3 资源边界与显式失败状态均在安装包环境通过。 + +此处是本地结果；本轮三版本远程 CI 尚待推送后核对。历史源码指纹与实验记录 +没有修改，严格重放仍应按 REVIEW_GUIDE 检出相应固定提交。 + + +`b0a92d0` 的个人 fork [CI 35696834868](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35696834868) +三版本全部通过：3.11 / 3.12 / 3.13 各 119 项应用测试、18 项上游测试、98.93% +覆盖率及全部 CPU 演示。应用测试用时分别 37.59 / 45.03 / 36.12 s，上游测试 +14.46 / 15.16 / 14.51 s。官方 [35696840057](https://github.com/OriginQ/pyqpanda-algorithm/actions/runs/35696840057) +仍为 action_required。该记录覆盖日历空域提前判定，尚未包含后续冗余配对行优化。 + + +## 日历固定零变量的冗余配对行（2026-09-22） + +新增两项真实 MILP 回归验证：省去上界固定为零变量的冗余配对行后，仍保留 +原始变量数、原始选项编号和最优目标。修改前两项均失败，修改后完整应用 +**121 passed，19.87 s，98.93%**。固定零变量不影响可行集合的证明见 ENHANCEMENTS。 + +更新后的 wheel 在另一个全新项目 `.venv` 安装，确认 site-packages 导入、11 个 +模块字节相同；**139 passed，26.86 s**（121 应用 + 18 原有），核心覆盖率 +**98.93%**（839 行、9 行未覆盖）。主取舍及 21 次敏感性通过；Ruff/format/Mypy/ +pip check 通过。对应远程结果见下方最终核对。 + +## 最终交付核对（2026-09-22） + +个人 fork [CI 35697552146](https://github.com/youshen2025/pyqpanda-algorithm/actions/runs/35697552146) +验证代码提交 `6c43e4059c07516f5cd1df5d912f417faa37bdeb`，最终 conclusion=success。 +收尾仅更新文档，不修改被测代码。 + +| Python | 应用测试 | 原有测试 | 应用覆盖率 | 通过轮次 | +| --- | --- | --- | --- | --- | +| 3.11 | 121，27.42 s | 18，11.00 s | 98.93% | 失败任务重跑一次 | +| 3.12 | 121，44.37 s | 18，15.34 s | 98.93% | 首次 | +| 3.13 | 121，38.23 s | 18，14.83 s | 98.93% | 首次 | + +3.11 首次应用测试已通过（121 项，36.47 s），原有测试为 17 通过、1 失败： +`test/QAlgBase/Test_QUBO_GAS_origin_run.py:31` 的有限次随机 GAS 搜索返回 +`[1,1,0]`，与断言的 `[[0,1,0]]` 不同。应用分支没有修改该测试、QUBO.py 或 +Grover_core.py。保留失败日志后仅重跑失败矩阵任务一次，未修改测试断言或代码。 +因此不能将本次记录描述为首次全绿，也不能由重跑通过断言已修复该随机失败。 + +原始日志保留于本地忽略目录 `reports/calendar-final-release/ci-attempt1.log`、 +`ci-failed.log`、`ci-attempt2.log`；公开运行链接也可查看两轮结果。 +官方 [35697558446](https://github.com/OriginQ/pyqpanda-algorithm/actions/runs/35697558446) +仍为 action_required，等待维护者批准。 + +当前应用与四个独立库修复的临时集成 `faee903` 在 Python 3.12 上通过 +**250 项测试，27.68 s，应用覆盖率 98.93%**。它没有混入任何参赛 PR；此前 +Python 3.11–3.13 各 242 项的记录仍对应应用旧提交，不扩大其验证范围。 + +截至本次核对，应用 #92 与库修复 #95/#96/#97/#98 均开放且可合并。这表示 Git +未检测到合并冲突，不表示维护者已经批准。停止主动扩展功能；平台提交回执、 +正式审核及真实试用仍属外部待办，没有虚构完成状态。 diff --git a/Tutorials/LabScheduling/research_pr_snapshot.json b/Tutorials/LabScheduling/research_pr_snapshot.json new file mode 100644 index 00000000..dc42e0ce --- /dev/null +++ b/Tutorials/LabScheduling/research_pr_snapshot.json @@ -0,0 +1,358 @@ +{ + "checked_on": "2026-09-21", + "source": "https://github.com/OriginQ/pyqpanda-algorithm/pulls", + "method": "GitHub HTML pages 1, 2, 3; page 4 empty", + "open_pr_count": 70, + "pull_requests": [ + { + "number": 62, + "title": "【代码贡献】新增 Simon 隐藏异或算法与 GF(2) 恢复工具", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/62" + }, + { + "number": 61, + "title": "【代码贡献】局部期望、量子核分量抽样、自适应归属与弱测量恢复", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/61" + }, + { + "number": 60, + "title": "【创新应用】新增小麦灌溉预测与 QUBO/QAOA 调度应用（含本源真机验证）", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/60" + }, + { + "number": 59, + "title": "【代码贡献】新增 Deutsch-Jozsa 与 Bernstein-Vazirani oracle-learning 模块", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/59" + }, + { + "number": 58, + "title": " 新增 QECClosedForm：量子纠错码闭式参数预测（与 QECNoise 闭环）", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/58" + }, + { + "number": 57, + "title": "【创新应用】量子纠错相干噪声 θ⁴ 损失标度演示", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/57" + }, + { + "number": 56, + "title": "【代码贡献】优化量子核矩阵与量子距离计算：去除采样噪声、复用模拟器并修复多处缺陷", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/56" + }, + { + "number": 55, + "title": "【代码贡献】完善 QARM 高阶关联规则枚举", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/55" + }, + { + "number": 54, + "title": "【代码贡献】Reuse QSVR training-kernel symmetry to reduce simulator calls", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/54" + }, + { + "number": 53, + "title": "【创新应用】QSEncode-Insight：面向误差预算的资源感知量子态制备与审计工具", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/53" + }, + { + "number": 52, + "title": "【代码贡献】优化 QSEncode Walsh 变换与 Top-K 筛选性能", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/52" + }, + { + "number": 51, + "title": "【代码贡献】补充 QSEncode 稀疏编码模块单元测试与文档示例", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/51" + }, + { + "number": 50, + "title": "【代码贡献】修复 QARM 支持度与关联规则结果错误", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/50" + }, + { + "number": 47, + "title": "【代码贡献】Add quantum counting (BHT) built on the existing Grover operator", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/47" + }, + { + "number": 46, + "title": "【代码贡献】QSVR: build the show_res test grid from each column's own min/max", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/46" + }, + { + "number": 45, + "title": "【代码贡献】Fix QUBO_QAOA.run crashing with default arguments", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/45" + }, + { + "number": 44, + "title": "【代码贡献】QSVD: skip the unused state-vector simulation in the loss inner loop", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/44" + }, + { + "number": 43, + "title": "【代码贡献】Fix QPCA crash on 4-feature input and k=2 returning 0", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/43" + }, + { + "number": 42, + "title": "【代码贡献】Fix qft_qubit_comparator geq and s modes returning wrong results", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/42" + }, + { + "number": 41, + "title": "【代码贡献】Fix SPSA optimizer ignoring parameter bounds", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/41" + }, + { + "number": 40, + "title": "【代码贡献】修复并加速 QAOA 的 CVaR/Gibbs 风险目标函数", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/40" + }, + { + "number": 39, + "title": "【代码贡献】修复 QmRMR 星号导入报错、8 个无法运行的示例及文档中的过时接口", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/39" + }, + { + "number": 37, + "title": "【创新应用】基于振幅估计的量子风险分析：VaR 与预期损失计算", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/37" + }, + { + "number": 36, + "title": "【代码贡献】修正 Grover 最优迭代次数计算，补充输入校验与依赖声明", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/36" + }, + { + "number": 35, + "title": "【创新应用】量子增强A股选股系统——量子核SVM市场择时", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/35" + }, + { + "number": 91, + "title": "【代码贡献】新增 QMitigation 读出误差缓解与零噪声外推模块", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/91" + }, + { + "number": 90, + "title": "【代码贡献】新增 qDRIFT 随机哈密顿量模拟算法", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/90" + }, + { + "number": 89, + "title": "【代码贡献】新增 Quantum Lanczos / Krylov 谱估计算法", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/89" + }, + { + "number": 88, + "title": "【代码贡献】新增 Quantum Natural Gradient 与 Fubini-Study 度量模块", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/88" + }, + { + "number": 87, + "title": "【代码贡献】新增 Parameter-Shift 变分量子梯度模块", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/87" + }, + { + "number": 85, + "title": "【代码贡献】新增 Superdense Coding 量子超密编码模块", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/85" + }, + { + "number": 83, + "title": "【代码贡献】新增 QuantumReservoir 量子储备池计算模块", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/83" + }, + { + "number": 82, + "title": "【代码贡献】新增 Bernstein–Vazirani 隐藏比特串算法", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/82" + }, + { + "number": 81, + "title": "【代码贡献】新增量子相位估计（QPE）算法模块", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/81" + }, + { + "number": 80, + "title": "【代码贡献】新增最大似然振幅估计算法（MLAE）", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/80" + }, + { + "number": 79, + "title": "【代码贡献】新增 VQLS 变分量子线性求解器", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/79" + }, + { + "number": 78, + "title": "【代码贡献】新增多量子比特 SWAP Test 状态重叠估计算法", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/78" + }, + { + "number": 77, + "title": "【代码贡献】新增量子态层析 QST 模块", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/77" + }, + { + "number": 76, + "title": "【代码贡献】新增 Hadamard Test 复期望值估计模块", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/76" + }, + { + "number": 75, + "title": "【代码贡献】新增可复用量子傅里叶变换（QFT）模块", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/75" + }, + { + "number": 74, + "title": "【代码贡献】新增 Grover 量子计数算法", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/74" + }, + { + "number": 73, + "title": "【代码贡献】新增 Quantum Counting 标记态计数模块", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/73" + }, + { + "number": 72, + "title": "【代码贡献】新增 QTeleport 相干量子隐形传态模块", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/72" + }, + { + "number": 69, + "title": "【代码贡献】完善 QUBO 二进制多项式规范化与高阶项校验", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/69" + }, + { + "number": 68, + "title": "【代码贡献】Fix SPSA termination when tolerance is set", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/68" + }, + { + "number": 67, + "title": "【代码贡献】新增BBHT未知解数量随机Grover搜索", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/67" + }, + { + "number": 66, + "title": "【代码贡献】QKMeans 提前拒绝不可能的聚类数量", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/66" + }, + { + "number": 65, + "title": "【代码贡献】修复 QSVR 绘图网格的特征范围", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/65" + }, + { + "number": 64, + "title": "【代码贡献】新增三量子比特重复码 QEC 算法模块", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/64" + }, + { + "number": 63, + "title": "【代码贡献】新增离散时间量子行走 QWalk 模块", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/63" + }, + { + "number": 34, + "title": "【代码贡献】新增QPE量子相位估计算法", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/34" + }, + { + "number": 32, + "title": "【创新应用】新增 QShadow：可观测量感知的经典阴影估计与置信预算控制", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/32" + }, + { + "number": 31, + "title": "【创新应用】新增量子格密码攻击算法集（QLattice）", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/31" + }, + { + "number": 30, + "title": "【创新应用】量子密钥分发QKD协议", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/30" + }, + { + "number": 29, + "title": "【创新应用】QJPEG 量子图像压缩算法", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/29" + }, + { + "number": 28, + "title": "【代码贡献】Add VQLS variational solver for linear systems", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/28" + }, + { + "number": 27, + "title": "【代码贡献】修复 QAE / IQAE 目标量子比特索引错误", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/27" + }, + { + "number": 26, + "title": "【创新应用】新增 Lindblad 动力学的随机 Magnus 变分模拟算法", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/26" + }, + { + "number": 24, + "title": "【创新应用】新增 QReupload：数据重上传变分量子学习模块（可训练 input scaling + Fourier/贫瘠高原诊断）", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/24" + }, + { + "number": 23, + "title": "【代码贡献】新增 VQE 变分量子本征求解器算法模块", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/23" + }, + { + "number": 22, + "title": "【代码贡献】Add missing pandas and scikit-learn to requirements.txt", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/22" + }, + { + "number": 21, + "title": "【代码贡献】Add HHL solver for linear systems (Ax=b)", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/21" + }, + { + "number": 20, + "title": "feat: 新增 VQE（变分量子本征求解器）模块", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/20" + }, + { + "number": 19, + "title": "[Innovative Application] NISQ Hardware Benchmarking Suite", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/19" + }, + { + "number": 18, + "title": "[Innovative Application] Add CCF2026 professional MNIST submission", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/18" + }, + { + "number": 17, + "title": "【创新应用】SU(2) color-chain quantum simulation tutorial", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/17" + }, + { + "number": 15, + "title": "【代码贡献】优化 QSVM 算法实现效率", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/15" + }, + { + "number": 9, + "title": "Fix typos, indentation, and add missing docstrings (QKmeans + Grover)", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/9" + }, + { + "number": 7, + "title": "docs(QAOA): Comprehensive bilingual documentation", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/7" + }, + { + "number": 6, + "title": "docs: Add comprehensive QAOA algorithm documentation with examples", + "url": "https://github.com/OriginQ/pyqpanda-algorithm/pull/6" + } + ] +} diff --git a/Tutorials/LabScheduling/results/compatibility.json b/Tutorials/LabScheduling/results/compatibility.json new file mode 100644 index 00000000..f4b0ca25 --- /dev/null +++ b/Tutorials/LabScheduling/results/compatibility.json @@ -0,0 +1,448 @@ +{ + "date": "2026-09-21", + "environments": [ + { + "application_tests": { + "command": "OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg .venv/bin/python -m pytest -c test/LabScheduling/pytest.ini test/LabScheduling --timeout=60 --cov=.venv/lib/python3.11/site-packages/pyqpanda_alg/LabScheduling --cov-fail-under=95", + "coverage_percent": 98.92, + "passed": 108, + "summary": "============================= 108 passed in 20.46s =============================" + }, + "arc_objective": 3, + "commands": [ + { + "command": [ + ".venv/bin/python", + "-m", + "pip", + "check" + ], + "exit_code": 0, + "expected_exit_code": 0, + "name": "pip-check", + "seconds": 0.7555378020042554 + }, + { + "command": [ + ".venv/bin/python", + 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"4bc41141574a11b949ca01256d707429317f5122f5da167d86a89247c61bfbac" +} diff --git a/Tutorials/LabScheduling/results/enhanced/SUMMARY.md b/Tutorials/LabScheduling/results/enhanced/SUMMARY.md new file mode 100644 index 00000000..1ff3ea39 --- /dev/null +++ b/Tutorials/LabScheduling/results/enhanced/SUMMARY.md @@ -0,0 +1,18 @@ +# 冻结语料逐实例汇总 + +完整训练记录见 runs.jsonl，每行一次训练；包含全部不可行实例。 + +| 实例 | 状态 | 平均/最差 p_opt | 均匀 p_opt | 胜出种子/10 | CNOT 原始→精简 | +|---|---|---|---|---|---| +| evaluation-n3-d0.json | optimal | 0.265351 / 0.261244 | 0.125000 | 10 | 19 → 13 | +| evaluation-n3-d1.json | optimal | 0.546045 / 0.531842 | 0.125000 | 10 | 21 → 15 | +| evaluation-n3-d2.json | optimal | 0.246273 / 0.054195 | 0.125000 | 9 | 23 → 17 | +| evaluation-n5-d0.json | optimal | 0.140080 / 0.071053 | 0.031250 | 10 | 33 → 23 | +| evaluation-n5-d1.json | optimal | 0.183046 / 0.044921 | 0.031250 | 10 | 39 → 29 | +| evaluation-n5-d2.json | infeasible | 0.000000 / 0.000000 | 0.000000 | 0 | 57 → 47 | +| evaluation-n6-d0.json | optimal | 0.033681 / 0.018933 | 0.015625 | 10 | 34 → 22 | +| evaluation-n6-d1.json | optimal | 0.184659 / 0.181925 | 0.031250 | 10 | 44 → 32 | +| evaluation-n6-d2.json | infeasible | 0.000000 / 0.000000 | 0.000000 | 0 | 60 → 48 | +| evaluation-n8-d0.json | optimal | 0.008020 / 0.000766 | 0.003906 | 7 | 42 → 26 | +| evaluation-n8-d1.json | optimal | 0.025118 / 0.006623 | 0.003906 | 10 | 56 → 40 | +| evaluation-n8-d2.json | infeasible | 0.000000 / 0.000000 | 0.000000 | 0 | 78 → 62 | diff --git a/Tutorials/LabScheduling/results/enhanced/aggregate.json b/Tutorials/LabScheduling/results/enhanced/aggregate.json new file mode 100644 index 00000000..f55f8ccc --- /dev/null +++ b/Tutorials/LabScheduling/results/enhanced/aggregate.json @@ -0,0 +1,206 @@ +[ + { + "instance": "evaluation-n3-d0.json", + "status": "optimal", + "qubits": 6, + "candidate_conflict_density": 0.16666666666666666, + "mean_p_opt": 0.2653513376450117, + "min_p_opt": 0.2612440470070083, + "max_p_opt": 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mode 100644 index 00000000..f6bca338 --- /dev/null +++ b/Tutorials/LabScheduling/results/matched/SUMMARY.md @@ -0,0 +1,62 @@ +# 同总预算对照结果 + +全部 36 个实例、每实例 10 种子；Q=直接量子，R=分量量子，U=直接均匀随机，S=分量均匀随机。 + +下表为实际 512 总 shots 下采到精确最优的种子数；不可行行无最优值，记 0。 + +| 实例 | 类别 | 最优值 | Q | R | U | S | +|---|---|---:|---:|---:|---:|---:| +| n3-s0-r0.json | quantum_required | 10 | 10 | 10 | 10 | 10 | +| n3-s0-r1.json | quantum_required | 19 | 10 | 10 | 10 | 10 | +| n3-s0-r2.json | quantum_required | 10 | 10 | 10 | 10 | 10 | +| n3-s0-r3.json | quantum_required | 14 | 10 | 10 | 10 | 10 | +| n3-s1-r0.json | classical_only | 25 | 10 | 10 | 10 | 10 | +| n3-s1-r1.json | quantum_required | 10 | 10 | 10 | 10 | 10 | +| n3-s1-r2.json | infeasible | — | 0 | 0 | 0 | 0 | +| n3-s1-r3.json | infeasible | — | 0 | 0 | 0 | 0 | +| n3-s2-r0.json | infeasible | — | 0 | 0 | 0 | 0 | +| n3-s2-r1.json | quantum_required | 7 | 10 | 10 | 10 | 10 | +| n3-s2-r2.json | classical_only | 27 | 10 | 10 | 10 | 10 | +| n3-s2-r3.json | infeasible | — | 0 | 0 | 0 | 0 | +| n4-s0-r0.json | quantum_required | 22 | 10 | 10 | 10 | 10 | +| n4-s0-r1.json | quantum_required | 18 | 10 | 10 | 10 | 10 | +| n4-s0-r2.json | quantum_required | 4 | 9 | 10 | 10 | 10 | +| n4-s0-r3.json | quantum_required | 10 | 9 | 10 | 10 | 10 | +| n4-s1-r0.json | infeasible | — | 0 | 0 | 0 | 0 | +| n4-s1-r1.json | infeasible | — | 0 | 0 | 0 | 0 | +| n4-s1-r2.json | quantum_required | 18 | 10 | 10 | 10 | 10 | +| n4-s1-r3.json | quantum_required | 15 | 10 | 10 | 10 | 10 | +| n4-s2-r0.json | quantum_required | 7 | 10 | 10 | 10 | 10 | +| n4-s2-r1.json | quantum_required | 3 | 10 | 10 | 10 | 10 | +| n4-s2-r2.json | quantum_required | 32 | 10 | 10 | 10 | 10 | +| n4-s2-r3.json | infeasible | — | 0 | 0 | 0 | 0 | +| n5-s0-r0.json | quantum_required | 17 | 9 | 10 | 10 | 10 | +| n5-s0-r1.json | quantum_required | 11 | 10 | 10 | 10 | 10 | +| n5-s0-r2.json | quantum_required | 13 | 10 | 10 | 10 | 10 | +| n5-s0-r3.json | quantum_required | 3 | 10 | 10 | 10 | 10 | +| n5-s1-r0.json | infeasible | — | 0 | 0 | 0 | 0 | +| n5-s1-r1.json | infeasible | — | 0 | 0 | 0 | 0 | +| n5-s1-r2.json | quantum_required | 20 | 10 | 10 | 10 | 10 | +| n5-s1-r3.json | infeasible | — | 0 | 0 | 0 | 0 | +| n5-s2-r0.json | quantum_required | 4 | 10 | 10 | 10 | 10 | +| n5-s2-r1.json | infeasible | — | 0 | 0 | 0 | 0 | +| n5-s2-r2.json | infeasible | — | 0 | 0 | 0 | 0 | +| n5-s2-r3.json | quantum_required | 44 | 10 | 10 | 10 | 10 | + +## 同总 shots 的条件命中概率 + +仅 22 个仍需量子的可行实例，先平均每实例的十种子，再等权平均实例。 +这是训练后理论曲线，低 shots 未重新训练或实际抽样，不包括训练成本。 + +| 总 shots | Q | R | U | S | R>S 实例 | R + + + + + + + image/svg+xml + + + Matplotlib v3.11.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Simple (6 qubits) + + + + + + + + + + Medium (12 qubits) + + + + + + + + + + + + + + + + + + 0.0 + + + + + + + + + + + + + 0.2 + + + + + + + + + + + + + 0.4 + + + + + + + + + + + + + 0.6 + + + + + + + + + + + + + 0.8 + + + + + + + + + + + + + 1.0 + + + + Feasible Born probability + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Seeds: 7, 19, 42; p=1, 512 shots + + + + + + + Uniform one-hot + + + + + + XY + + + + + + X + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 0 + + + + + + + + + + + + + 1 + + + + + + + + + + + + + 2 + + + + + + + + + + + + + 3 + + + + + + + + + + + + + 4 + + + + + + + + + + + + + 5 + + + + + + + + + + + + + 6 + + + + + + + + + + + + + 7 + + + + + + + + + + + + + 8 + + + + + + + + + + + + + 9 + + + + + + + + + + + + + 10 + + + + + + + + + + + + + 11 + + + + + + + + + + + + + 12 + + + + Time slot (hatched tail = cleanup) + + + + + + + + + + + Microscope + + + + + + + + + + Spectrometer + + + + + + + + + + + + + + + + + B-image + + + A-prepare + + + A-image + + + B-measure + + + Medium / XY / seed 7: cost 4, violations 0 + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git 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"oven", + "start": 6, + "end": 7 + } + ] + }, + "dual_bound": 3.0, + "gap": 0.0, + "status": "optimal", + "solver_status": 0, + "message": "Optimization terminated successfully. (HiGHS Status 7: Optimal)", + "constraints": 35, + "runtime_seconds": 0.0016573409957345575 + }, + "uniform_one_hot": { + "best": { + "feasible": true, + "objective": 3, + "raw_cost": 3, + "violation_count": 0, + "violations": [], + "schedule": [ + { + "variable": 0, + "task": "t0", + "group": "g0", + "resource": "oven", + "start": 1, + "end": 2, + "reserved_end": 2, + "changed": true + }, + { + "variable": 5, + "task": "t1", + "group": "g1", + "resource": "oven", + "start": 2, + "end": 3, + "reserved_end": 3, + "changed": false + }, + { + "variable": 11, + "task": "t2", + "group": "g2", + "resource": "oven", + "start": 4, + "end": 5, + "reserved_end": 5, + "changed": false + }, + { + "variable": 17, + "task": "t3", + "group": "g3", + "resource": "oven", + "start": 6, + "end": 7, + "reserved_end": 7, + "changed": false + } + ], + "bits": [ + 1, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 1 + ], + "qubo_energy": 3 + }, + "shots": 512, + "seed": 42, + "feasible_probability": 0.04, + "sampled_feasible_fraction": 0.0390625, + "runtime_seconds": 0.013554268007283099 + }, + "absolute_gap": 0, + "status": "feasible", + "runtime_seconds": 0.35457481400226243 + } + ] +} diff --git a/Tutorials/LabScheduling/results/v2/SUMMARY.md b/Tutorials/LabScheduling/results/v2/SUMMARY.md new file mode 100644 index 00000000..9fe56270 --- /dev/null +++ b/Tutorials/LabScheduling/results/v2/SUMMARY.md @@ -0,0 +1,44 @@ +# 第二批逐实例结果 + +36 个实例 × 10 个种子，两条执行路径；不可行和纯经典传播均保留。 + +原始模型只有候选计数，不虚构含无效候选的线路。分解前后预算不同，不能用命中数宣称公平胜率优势。 + +| 实例 | 最优值 | 原始→日历→传播→最大组件候选 | CNOT 日历完整→组件完整→精简 | 直接/化简最优命中（各10） | +|---|---|---|---|---| +| n3-s0-r0.json | 10 | 9→8→7→7 | 35→35→17 | 10/10 | +| n3-s0-r1.json | 19 | 9→8→5→3 | 39→17→9 | 10/10 | +| n3-s0-r2.json | 10 | 9→8→7→7 | 39→39→21 | 10/10 | +| n3-s0-r3.json | 14 | 9→8→5→3 | 37→17→9 | 10/10 | +| n3-s1-r0.json | 25 | 9→7→0→0 | 36→0→0 | 10/10 | +| n3-s1-r1.json | 10 | 9→6→3→3 | 23→12→6 | 10/10 | +| n3-s1-r2.json | None | 9→5→None→None | None→None→None | 0/0 | +| n3-s1-r3.json | None | 9→4→None→None | None→None→None | 0/0 | +| n3-s2-r0.json | None | 9→8→None→None | 53→None→None | 0/0 | +| n3-s2-r1.json | 7 | 9→8→7→7 | 49→49→31 | 10/10 | +| n3-s2-r2.json | 27 | 9→8→0→0 | 49→0→0 | 10/10 | +| n3-s2-r3.json | None | 9→8→None→None | 45→None→None | 0/0 | +| n4-s0-r0.json | 22 | 11→10→7→7 | 50→30→20 | 10/10 | +| n4-s0-r1.json | 18 | 11→10→4→2 | 52→10→6 | 10/10 | +| n4-s0-r2.json | 4 | 11→10→7→5 | 46→24→14 | 9/10 | +| n4-s0-r3.json | 10 | 11→10→8→5 | 44→33→19 | 9/10 | +| n4-s1-r0.json | None | 11→7→None→None | None→None→None | 0/0 | +| n4-s1-r1.json | None | 11→7→None→None | None→None→None | 0/0 | +| n4-s1-r2.json | 18 | 11→9→8→8 | 43→43→27 | 10/10 | +| n4-s1-r3.json | 15 | 11→7→4→2 | 19→10→6 | 10/10 | +| n4-s2-r0.json | 7 | 11→10→8→8 | 52→43→27 | 10/10 | +| n4-s2-r1.json | 3 | 11→10→9→9 | 62→62→42 | 10/10 | +| n4-s2-r2.json | 32 | 11→10→7→7 | 60→34→24 | 10/10 | +| n4-s2-r3.json | None | 11→10→None→None | 64→None→None | 0/0 | +| n5-s0-r0.json | 17 | 14→13→9→5 | 62→29→17 | 9/10 | +| n5-s0-r1.json | 11 | 14→13→12→9 | 54→54→28 | 10/10 | +| n5-s0-r2.json | 13 | 14→13→11→6 | 58→49→27 | 10/10 | +| n5-s0-r3.json | 3 | 14→13→12→9 | 64→64→38 | 10/10 | +| n5-s1-r0.json | None | 14→12→None→None | 53→None→None | 0/0 | +| n5-s1-r1.json | None | 14→10→None→None | None→None→None | 0/0 | +| n5-s1-r2.json | 20 | 14→12→8→8 | 55→37→23 | 10/10 | +| n5-s1-r3.json | None | 14→10→None→None | None→None→None | 0/0 | +| n5-s2-r0.json | 4 | 14→13→12→12 | 72→72→46 | 10/10 | +| n5-s2-r1.json | None | 14→13→None→None | 72→None→None | 0/0 | +| n5-s2-r2.json | None | 14→13→None→None | 82→None→None | 0/0 | +| n5-s2-r3.json | 44 | 14→13→7→7 | 74→34→24 | 10/10 | diff --git a/Tutorials/LabScheduling/results/v2/aggregate.json b/Tutorials/LabScheduling/results/v2/aggregate.json new file mode 100644 index 00000000..675a912a --- /dev/null +++ b/Tutorials/LabScheduling/results/v2/aggregate.json @@ -0,0 +1,133 @@ +{ + "instances": 36, + "feasible_instances": 24, + "paired_seed_records": 360, + "direct_statuses": { + "feasible": 240, + "infeasible_domain": 60, + "no_feasible_sample": 60 + }, + "reduced_statuses": { + "feasible": 220, + "classical_propagation": 20, + "infeasible_propagation": 120 + }, + "direct_quantum_executions": 300, + "reduced_quantum_executions": 220, + "reduced_component_trainings": 340, + "feasible_training_seeds": 240, + "seeds_better_than_uniform": 206, + "seeds_worse_than_uniform": 34, + "phase_max_error": 4.996003610813204e-16, + "cells": [ + { + "tasks": 3, + "scenario": 0, + "instances": 4, + "feasible": 4, + "instance_mean_p_opt": 0.18786986403449024, + "instance_mean_uniform_p_opt": 0.08333333333333333, + "instance_mean_delta_range": [ + 0.06413173407907789, + 0.18323327262836497 + ] + }, + { + "tasks": 3, + "scenario": 1, + "instances": 4, + "feasible": 2, + "instance_mean_p_opt": 0.3448381601987412, + "instance_mean_uniform_p_opt": 0.1875, + "instance_mean_delta_range": [ + 0.09887757618912868, + 0.2157987442083537 + ] + }, + { + "tasks": 3, + "scenario": 2, + "instances": 4, + "feasible": 2, + "instance_mean_p_opt": 0.17274474138797985, + "instance_mean_uniform_p_opt": 0.08333333333333333, + "instance_mean_delta_range": [ + 0.05719447681395938, + 0.12162833929533369 + ] + }, + { + "tasks": 4, + "scenario": 0, + "instances": 4, + "feasible": 4, + "instance_mean_p_opt": 0.06493803014908012, + "instance_mean_uniform_p_opt": 0.041666666666666664, + "instance_mean_delta_range": [ + -0.007502533321192738, + 0.06361773930346004 + ] + }, + { + "tasks": 4, + "scenario": 1, + "instances": 4, + "feasible": 2, + "instance_mean_p_opt": 0.20110470241254325, + "instance_mean_uniform_p_opt": 0.11458333333333333, + "instance_mean_delta_range": [ + 0.05347529302208902, + 0.1195674451363308 + ] + }, + { + "tasks": 4, + "scenario": 2, + "instances": 4, + "feasible": 3, + "instance_mean_p_opt": 0.15728182023404488, + "instance_mean_uniform_p_opt": 0.05555555555555555, + "instance_mean_delta_range": [ + 0.06131367761263367, + 0.12301993361808973 + ] + }, + { + "tasks": 5, + "scenario": 0, + "instances": 4, + "feasible": 4, + "instance_mean_p_opt": 0.03643322626910944, + "instance_mean_uniform_p_opt": 0.013888888888888888, + "instance_mean_delta_range": [ + 0.0015993116588795837, + 0.04438090186202739 + ] + }, + { + "tasks": 5, + "scenario": 1, + "instances": 4, + "feasible": 1, + "instance_mean_p_opt": 0.08914420177005611, + "instance_mean_uniform_p_opt": 0.020833333333333332, + "instance_mean_delta_range": [ + 0.06831086843672278, + 0.06831086843672278 + ] + }, + { + "tasks": 5, + "scenario": 2, + "instances": 4, + "feasible": 2, + "instance_mean_p_opt": 0.09263376497535755, + "instance_mean_uniform_p_opt": 0.013888888888888888, + "instance_mean_delta_range": [ + 0.03388222584343676, + 0.12360752632950056 + ] + } + ], + "scope": "synthetic instances; seed means within instance; unequal component budgets; no speedup claim" +} diff --git a/Tutorials/LabScheduling/results/v2/execution.json b/Tutorials/LabScheduling/results/v2/execution.json new file mode 100644 index 00000000..96a11598 --- /dev/null +++ b/Tutorials/LabScheduling/results/v2/execution.json @@ -0,0 +1,5 @@ +{ + "instances": 36, + "paired_seed_records": 360, + "runtime_seconds": 97.70505226500973 +} diff --git a/Tutorials/LabScheduling/results/v2/instances.jsonl b/Tutorials/LabScheduling/results/v2/instances.jsonl new file mode 100644 index 00000000..271fb351 --- /dev/null +++ b/Tutorials/LabScheduling/results/v2/instances.jsonl @@ -0,0 +1,36 @@ 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[100%] +================================ tests coverage ================================ +_______________ coverage: platform linux, python 3.12.3-final-0 ________________ + +Name Stmts Miss Cover Missing +------------------------------------------------------------------------------------------ +pyqpanda-algorithm/pyqpanda_alg/LabScheduling/__init__.py 5 0 100% +pyqpanda-algorithm/pyqpanda_alg/LabScheduling/__main__.py 27 3 89% 34-35, 43 +pyqpanda-algorithm/pyqpanda_alg/LabScheduling/classical.py 24 0 100% +pyqpanda-algorithm/pyqpanda_alg/LabScheduling/model.py 119 0 100% +pyqpanda-algorithm/pyqpanda_alg/LabScheduling/problem.py 121 1 99% 150 +pyqpanda-algorithm/pyqpanda_alg/LabScheduling/quantum.py 121 2 98% 82, 231 +pyqpanda-algorithm/pyqpanda_alg/LabScheduling/report.py 38 1 97% 71 +------------------------------------------------------------------------------------------ +TOTAL 455 7 98% +Required test coverage of 95% reached. Total coverage: 98.46% +56 passed in 5.74s + +UPSTREAM EFFECTIVE TESTS (OMP/BLAS threads=1) +.................. [100%] +18 passed in 9.08s + +QUALITY +Ruff check: passed +Ruff format --check: 13 files formatted +Mypy: no issues in 7 source files +pip check: no broken requirements +Built wheel import + medium CPU example: cost=4, violations=0, gap=0 +Full 12-run benchmark replay: exact JSON equality excluding runtime_seconds. +Final upstream refresh: develop unchanged; 70 open PRs, no new entries. +Final diff, artifact, secret-pattern, legacy-import and relative-link audits: passed. diff --git a/Tutorials/README.md b/Tutorials/README.md index bb77d7f7..b7e95fe2 100644 --- a/Tutorials/README.md +++ b/Tutorials/README.md @@ -1,2 +1,7 @@ # QPanda-Toturial # pyQPanda-Toturial + + +## 应用案例 + +- [LabReschedule 中文教程与复现实验](LabScheduling/README.md) diff --git a/Tutorials/source/index.rst b/Tutorials/source/index.rst index 08668542..a70934fb 100644 --- a/Tutorials/source/index.rst +++ b/Tutorials/source/index.rst @@ -42,3 +42,4 @@ Overall, it provides a standardized set of tools for developers, allowing them t autoapi/pyqpanda_alg/QSVD/index autoapi/pyqpanda_alg/QSVR/index autoapi/pyqpanda_alg/QUBO/index + autoapi/pyqpanda_alg/LabScheduling/index diff --git a/pyqpanda-algorithm/example/LabScheduling/README.md b/pyqpanda-algorithm/example/LabScheduling/README.md new file mode 100644 index 00000000..a6834123 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/README.md @@ -0,0 +1,31 @@ +# 共享实验室最小扰动重排 + +完整中文教程、输入规范、QUBO 推导、结果和演示流程： +[LabReschedule](../../../Tutorials/LabScheduling/README.md)。 + +在**仓库根目录**安装本地包和应用依赖后运行： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 .venv/bin/python -m pyqpanda_alg.LabScheduling pyqpanda-algorithm/example/LabScheduling/data/medium.json --output reports/medium.json +``` + +默认 XY / seed 7：中等样例目标值 4、违反数 0、经典最优差距 0。 +数据为合成数据；不需要 API Key、GPU 或真实量子硬件。 + +增强演示（CSV 预约→连锁改约→4 比特 CPU 求解→双经典认证→变更表/甘特图）： + +```bash +OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MPLBACKEND=Agg .venv/bin/python pyqpanda-algorithm/example/LabScheduling/booking_demo.py +``` + +[增强教程与全部实验](../../../Tutorials/LabScheduling/ENHANCEMENTS.md)解释安全传播、 +组件回填、XY 相位精简、MILP、12 实例 × 10 种子评测及已知失败。 +## 最新审阅与异构案例 + +安装后可运行 `tradeoff_demo.py`，查看三个实验整体后移与使用备用设备的完整取舍。 +`benchmark_v2.py` 执行冻结的 36 个异构问题及两条求解路径； +`summarize_v2.py reports/lab-v2` 汇总所有结果，旧版评价入口继续保留。 +详见[审阅指南](../../../Tutorials/LabScheduling/REVIEW_GUIDE.md)。 + +`matched_budget.py` 与 `summarize_matched.py` 提供同总预算及化简后随机基线对照。 +运行、结论边界和全量重放见[补充评价](../../../Tutorials/LabScheduling/MATCHED_BUDGET.md)。 diff --git a/pyqpanda-algorithm/example/LabScheduling/ablation.py b/pyqpanda-algorithm/example/LabScheduling/ablation.py new file mode 100644 index 00000000..c7c9d33c --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/ablation.py @@ -0,0 +1,140 @@ +"""Audit each preprocessing stage against every raw feasible assignment.""" + +from dataclasses import replace +from itertools import product +from time import perf_counter +from typing import Any + +from pyqpanda_alg.LabScheduling import ( + Model, + Problem, + compile_qubo, + evaluate, + reduce_model, + validate_assignment, +) +from pyqpanda_alg.LabScheduling.metrics import circuit_resources + + +def feasible_vectors(model: Model) -> dict[tuple[int, ...], int]: + """Enumerate feasible one-hot vectors and their original objective costs.""" + result = {} + for choices in product(*model.domains): + bits = tuple(int(i in choices) for i in range(len(model.variables))) + row = evaluate(model, bits) + if row["feasible"]: + result[bits] = row["objective"] + return result + + +def resources(models: list[Model], mode: str) -> dict[str, Any]: + """Count native p=1 gates; distinguish serial depth from component maximum.""" + valid = all(all(m.domains) for m in models) + counts = [circuit_resources(m, [0.37, 0.61])[mode] for m in models] if valid else [] + return { + "component_qubits": [len(m.variables) for m in models], + "total_qubits": sum(len(m.variables) for m in models), + "max_component_qubits": max((len(m.variables) for m in models), default=0), + "cnot": sum(c["cnot"] for c in counts) if valid else None, + "cry": sum(c["cry"] for c in counts) if valid else None, + "serial_depth_sum": sum(c["depth"] for c in counts) if valid else None, + "max_component_depth": max((c["depth"] for c in counts), default=0) + if valid + else None, + "circuit_status": "constructed" if valid and models else "no_circuit", + } + + +def audit_stages(problem: Problem) -> dict[str, Any]: + """Verify pruning and restoration bijections before reporting saved resources. + + Intended for the bounded example corpus, not unrestricted production inputs. + Raw input is enumerated with the independent raw semantic checker. A raw + invalid option has no circuit representation in this app, so its gate count + is deliberately absent instead of comparing different constraint problems. + """ + started = perf_counter() + model = compile_qubo(problem) + compiled = perf_counter() + reduction = reduce_model(model) + reduced = perf_counter() + raw_feasible = {} + for choices in product(*(range(len(t.options)) for t in problem.tasks)): + row = validate_assignment(problem, choices) + if row["feasible"]: + raw_feasible[choices] = row["objective"] + vectors = feasible_vectors(model) + mapped = {} + for bits, cost in vectors.items(): + choices = tuple( + problem.tasks[ti].options.index( + model.variables[next(i for i in d if bits[i])].option + ) + for ti, d in enumerate(model.domains) + ) + mapped[choices] = cost + if mapped != raw_feasible: + raise RuntimeError("calendar pruning changed raw feasible assignments") + restored = {} + if reduction.infeasible_task is None: + local = [feasible_vectors(m) for m in reduction.components] + for choices in product(*(list(values) for values in local)): + row = reduction.restore(choices) + expected = reduction.audit()["fixed_cost"] + sum( + values[bits] for values, bits in zip(local, choices, strict=True) + ) + if not row["feasible"] or row["objective"] != expected: + raise RuntimeError("component restoration changed feasibility or cost") + restored[tuple(row["bits"])] = row["objective"] + if restored != vectors: + raise RuntimeError("reduction changed the complete feasible set") + proven = perf_counter() + stage_rows = { + "raw_input": { + "candidate_count": sum(len(t.options) for t in problem.tasks), + "cnot": None, + "circuit_status": "not_encoded; unary-invalid candidates still present", + }, + "calendar_full_phase": resources([model], "full"), + } + if reduction.infeasible_task is None: + active = {i for mapping in reduction.mappings for i in mapping} + tasks = tuple( + replace( + task, + options=tuple(model.variables[i].option for i in domain if i in active), + ) + for task, domain in zip(problem.tasks, model.domains, strict=True) + if any(i in active for i in domain) + ) + ids = {t.id for t in tasks} + joint = compile_qubo( + replace( + problem, + tasks=tasks, + precedence=tuple( + (a, b) for a, b in problem.precedence if a in ids and b in ids + ), + ) + ) + stage_rows.update( + { + "propagation_full_phase": resources([joint] if tasks else [], "full"), + "components_full_phase": resources(list(reduction.components), "full"), + "components_auto_phase": resources(list(reduction.components), "auto"), + } + ) + return { + "stages": stage_rows, + "reduction": reduction.audit(), + "feasible_count": len(vectors), + "exact_objective": min(vectors.values(), default=None), + "proof": ( + "all raw feasible choices = pruned choices = restored component choices; " + "costs equal" + ), + "calendar_seconds": compiled - started, + "reduction_seconds": reduced - compiled, + "proof_seconds": proven - reduced, + "resource_audit_seconds": perf_counter() - proven, + } diff --git a/pyqpanda-algorithm/example/LabScheduling/benchmark.py b/pyqpanda-algorithm/example/LabScheduling/benchmark.py new file mode 100644 index 00000000..e84d014b --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/benchmark.py @@ -0,0 +1,63 @@ +"""Reproduce seed/mixer ablations and write raw reports plus CSV metrics.""" + +import argparse +import csv +import json +from pathlib import Path + +from pyqpanda_alg.LabScheduling.report import run_experiment + + +def main() -> None: + """Benchmark both feasible datasets with three seeds and two mixers.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output", type=Path, default=Path("reports/lab-scheduling")) + parser.add_argument("--seeds", type=int, nargs="+", default=[7, 19, 42]) + args = parser.parse_args() + args.output.mkdir(parents=True, exist_ok=True) + rows = [] + for dataset in ("simple", "medium"): + for seed in args.seeds: + for mixer in ("xy", "x"): + report = run_experiment( + Path(__file__).parent / "data" / f"{dataset}.json", + seed=seed, + mixer=mixer, + ) + name = f"{dataset}-{mixer}-{seed}" + (args.output / f"{name}.json").write_text( + json.dumps(report, ensure_ascii=False, allow_nan=False) + "\n", + encoding="utf-8", + ) + q = report["quantum"] + rows.append( + { + "dataset": dataset, + "seed": seed, + "mixer": mixer, + "qubits": report["model"]["qubits"], + "objective": q["best"]["objective"], + "violations": q["best"]["violation_count"], + "exact_objective": report["exact"]["best"]["objective"], + "gap": report["absolute_gap"], + "feasible_probability": q["feasible_probability"], + "one_hot_probability": q["one_hot_probability"], + "uniform_feasible_probability": report["uniform_one_hot"][ + "feasible_probability" + ], + "quantum_seconds": q["runtime_seconds"], + "exact_seconds": report["exact"]["runtime_seconds"], + "circuit_evaluations": q["circuit_evaluations"], + } + ) + print(name, rows[-1], flush=True) + with (args.output / "summary.csv").open( + "w", newline="", encoding="utf-8" + ) as stream: + writer = csv.DictWriter(stream, fieldnames=list(rows[0]), lineterminator="\n") + writer.writeheader() + writer.writerows(rows) + + +if __name__ == "__main__": + main() diff --git a/pyqpanda-algorithm/example/LabScheduling/benchmark_v2.py b/pyqpanda-algorithm/example/LabScheduling/benchmark_v2.py new file mode 100644 index 00000000..67a6b35f --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/benchmark_v2.py @@ -0,0 +1,234 @@ +"""Run every frozen heterogeneous instance and both quantum execution paths.""" + +import argparse +import hashlib +import json +import platform +from dataclasses import replace +from importlib.metadata import version +from math import prod +from pathlib import Path +from time import perf_counter +from typing import Any + +import numpy as np +from ablation import audit_stages +from pyqpanda_alg.LabScheduling import ( + circuit_probabilities, + compile_qubo, + evaluate, + load_problem, + reduce_model, + solve_milp, + solve_qaoa, + solve_reduced, + validate_assignment, +) +from pyqpanda_alg.LabScheduling.metrics import distribution_metrics +from stress_benchmark import write_json + + +def check_sample(model: Any, run: dict[str, Any]) -> None: + """Reject a selected result absent from counts or invalid in raw input.""" + if "counts" in run: + key = "".join(map(str, run["best"]["bits"][::-1])) + if run["counts"].get(key, 0) <= 0: + raise RuntimeError("best result is not an observed quantum sample") + if run["best"] and run["best"]["feasible"]: + choices = [ + task.options.index( + next( + v.option + for v, bit in zip(model.variables, run["best"]["bits"], strict=True) + if bit and v.task == ti + ) + ) + for ti, task in enumerate(model.problem.tasks) + ] + raw = validate_assignment(model.problem, choices) + if not raw["feasible"] or raw["objective"] != run["best"]["objective"]: + raise RuntimeError("sample failed raw semantic check") + + +def diagnostics(model: Any, run: dict[str, Any]) -> dict[str, Any] | None: + """Certify a trained distribution without feeding the optimum into training.""" + if "parameters" not in run: + return None + probabilities = circuit_probabilities(model, run["parameters"]) + full = circuit_probabilities(model, run["parameters"], phase_mode="full") + error = float(np.max(np.abs(probabilities - full))) + if error > 1e-10: + raise RuntimeError("full and simplified phases disagree") + metrics = distribution_metrics(model, probabilities) + samples = [] + for shots in (8, 16, 32, 64, 128, 512): + rng = np.random.default_rng( + np.random.SeedSequence([run["seed"], shots, 20260929]) + ) + objectives = [] + for value in set(rng.choice(len(probabilities), size=shots, p=probabilities)): + row = evaluate( + model, [(int(value) >> j) & 1 for j in range(len(model.variables))] + ) + if row["feasible"]: + objectives.append(row["objective"]) + samples.append( + {"shots": shots, "best_objective": min(objectives, default=None)} + ) + return {**metrics, "phase_max_error": error, "independent_samples": samples} + + +def run(corpus: Path, output: Path) -> None: + """Save all instances and seed runs; failed/fully classical cases remain visible.""" + manifest_path = corpus / "manifest.json" + manifest = json.loads(manifest_path.read_text()) + here = Path(__file__).parent + if ( + hashlib.sha256((here / "make_corpus_v2.py").read_bytes()).hexdigest() + != manifest["generator_sha256"] + ): + raise ValueError("frozen generator changed") + for spec in manifest["instances"]: + if ( + hashlib.sha256((corpus / spec["file"]).read_bytes()).hexdigest() + != spec["sha256"] + ): + raise ValueError(f"frozen input changed: {spec['file']}") + output.mkdir(parents=True, exist_ok=True) + import pyqpanda_alg.LabScheduling as package + + write_json( + output / "provenance.json", + { + "manifest": manifest, + "manifest_sha256": hashlib.sha256(manifest_path.read_bytes()).hexdigest(), + "python": platform.python_version(), + "platform": platform.platform(), + "packages": { + n: version(n) for n in ("numpy", "scipy", "pyqpanda3", "pyqpanda_alg") + }, + "sources": { + str(p.relative_to(here)) + if p.is_relative_to(here) + else f"core/{p.name}": hashlib.sha256(p.read_bytes()).hexdigest() + for p in [ + Path(__file__), + here / "ablation.py", + here / "stress_benchmark.py", + *Path(package.__file__).parent.glob("*.py"), + ] + }, + }, + ) + started = perf_counter() + instances = [] + count = 0 + with (output / "runs.jsonl").open("w", encoding="utf-8") as stream: + for spec in manifest["instances"]: + problem = load_problem(corpus / spec["file"]) + control = replace( + problem, + resources=tuple(replace(r, downtime=()) for r in problem.resources), + ) + original = validate_assignment(control, [0] * len(control.tasks)) + if not original["feasible"] or original["objective"] != 0: + raise RuntimeError( + "original bookings are not a valid zero-cost control" + ) + model = compile_qubo(problem) + reduction = reduce_model(model) + stages = audit_stages(problem) + milp = solve_milp(problem) + if (milp["status"] == "optimal") != bool(stages["feasible_count"]) or milp[ + "status" + ] not in ("optimal", "infeasible"): + raise RuntimeError("MILP and exhaustive proof disagree") + if milp["best"] and milp["best"]["objective"] != stages["exact_objective"]: + raise RuntimeError("MILP optimum disagrees") + instances.append( + { + "input": spec, + "model": model.audit(), + "ablation": stages, + "milp": milp, + "control": original, + } + ) + for seed in manifest["optimization_seeds"]: + # Both paths execute before any distribution diagnostics. + direct = solve_qaoa(model, seed=seed, **manifest["training"]) + reduced = solve_reduced(model, seed=seed, **manifest["training"]) + check_sample(model, direct) + check_sample(model, reduced) + diagnostic_started = perf_counter() + profile = diagnostics(model, direct) + local = [] + for component, result in zip( + reduction.components, reduced["components"], strict=True + ): + check_sample(component, result) + local.append(diagnostics(component, result)) + record = { + "instance": spec["file"], + "seed": seed, + "direct": direct, + "reduced": reduced, + "direct_metrics": profile, + "component_metrics": local, + "reduced_single_joint_draw_p_opt": prod( + m["optimal_probability"] for m in local + ) + if reduced["status"] + in ("feasible", "no_feasible_sample", "classical_propagation") + else None, + "diagnostic_seconds": perf_counter() - diagnostic_started, + } + stream.write( + json.dumps( + record, + ensure_ascii=False, + allow_nan=False, + separators=(",", ":"), + ) + + "\n" + ) + stream.flush() + count += 1 + print( + spec["file"], + milp["status"], + "components", + stages["reduction"]["component_qubits"], + flush=True, + ) + with (output / "instances.jsonl").open("w", encoding="utf-8") as stream: + for record in instances: + stream.write( + json.dumps( + record, ensure_ascii=False, allow_nan=False, separators=(",", ":") + ) + + "\n" + ) + write_json( + output / "execution.json", + { + "instances": len(instances), + "paired_seed_records": count, + "runtime_seconds": perf_counter() - started, + }, + ) + + +def main() -> None: + """Run the frozen second corpus independently of the first archive.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--corpus", type=Path, default=Path(__file__).parent / "data/corpus-v2" + ) + parser.add_argument("--output", type=Path, default=Path("reports/lab-v2")) + args = parser.parse_args() + run(args.corpus, args.output) + + +if __name__ == "__main__": + main() diff --git a/pyqpanda-algorithm/example/LabScheduling/booking_demo.py b/pyqpanda-algorithm/example/LabScheduling/booking_demo.py new file mode 100644 index 00000000..edac3c95 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/booking_demo.py @@ -0,0 +1,138 @@ +"""One-command booking CSV -> safe reduction -> CPU QAOA -> verified change ledger.""" + +import argparse +import copy +import csv +import json +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from pyqpanda_alg.LabScheduling.bookings import import_bookings +from pyqpanda_alg.LabScheduling.report import run_experiment + + +def run_demo(output: Path) -> None: + """Run outage and unchanged control, save raw reports, CSV changes and SVG.""" + source = Path(__file__).parent / "data/bookings" + calendar = json.loads((source / "outage.json").read_text(encoding="utf-8")) + problem = import_bookings(source / "chain.csv", calendar) + output.mkdir(parents=True, exist_ok=True) + reports = {} + for name in ("outage", "control"): + raw = copy.deepcopy(problem) + if name == "control": + for resource in raw["resources"]: + resource["downtime"] = [] + raw["name"] = "合成无停机对照：原预约全部保留" + path = output / f"{name}-input.json" + path.write_text(json.dumps(raw, ensure_ascii=False, indent=2) + "\n") + report = run_experiment(path, reduce=True) + (output / f"{name}-report.json").write_text( + json.dumps(report, ensure_ascii=False, indent=2, allow_nan=False) + "\n" + ) + if report["status"] != "feasible": + raise RuntimeError(f"demo failed: {name}: {report['status']}") + reports[name] = report + best = reports["outage"]["quantum"]["best"] + reduction = reports["outage"]["quantum"]["reduction"] + changes = [] + for row in best["schedule"]: + task = next(t for t in problem["tasks"] if t["id"] == row["task"]) + original = task["original"] + option = next( + o + for o in task["options"] + if (o["resource"], o["start"]) == (row["resource"], row["start"]) + ) + pruned = any( + p["task"] == task["id"] + and p["resource"] == original["resource"] + and p["start"] == original["start"] + for p in reports["outage"]["model"]["pruned"] + ) + changes.append( + { + "task": row["task"], + "original_resource": original["resource"], + "original_start": original["start"], + "new_resource": row["resource"], + "new_start": row["start"], + "changed": row["changed"], + "preference_cost": option["cost"], + "change_cost": task["change_cost"] if row["changed"] else 0, + "explanation": "原预约被停机排除" + if pruned + else "约束传播要求连锁改约，见 reduction.removals" + if row["changed"] + else "保留原预约", + } + ) + with (output / "changes.csv").open("w", newline="", encoding="utf-8") as stream: + writer = csv.DictWriter( + stream, fieldnames=list(changes[0]), lineterminator="\n" + ) + writer.writeheader() + writer.writerows(changes) + fig, axes = plt.subplots(2, 1, figsize=(9, 4.8), layout="constrained", sharex=True) + for ax, title, after in zip( + axes, + ("Original bookings + outage", "Verified reschedule"), + (False, True), + strict=True, + ): + for i, task in enumerate(problem["tasks"]): + original = task["original"] + row = next(r for r in best["schedule"] if r["task"] == task["id"]) + start = row["start"] if after else original["start"] + rid = row["resource"] if after else original["resource"] + y = {"oven": 0, "scope": 1}[rid] + ax.barh( + y, + task["duration"], + left=start, + height=0.55, + color=plt.get_cmap("tab10")(i), + ) + ax.text( + start + task["duration"] / 2, y, task["id"], ha="center", va="center" + ) + ax.barh( + 0, 1, left=0, height=0.75, facecolor="none", edgecolor="red", hatch="xx" + ) + ax.set( + title=title, yticks=[0, 1], yticklabels=["Oven", "Microscope"], xlim=(0, 6) + ) + ax.grid(axis="x", alpha=0.2) + axes[-1].set_xlabel("Time slot; red hatch = outage; synthetic booking data") + matplotlib.rcParams["svg.hashsalt"] = "lab-booking-demo" + matplotlib.rcParams["svg.fonttype"] = "none" + fig.savefig(output / "booking-changes.svg", metadata={"Date": None}) + plt.close(fig) + print( + json.dumps( + { + "outage_cost": best["objective"], + "control_cost": reports["control"]["quantum"]["best"]["objective"], + "changed_tasks": sum(row["changed"] for row in changes), + "original_qubits": reduction["original_qubits"], + "component_qubits": reduction["component_qubits"], + "outputs": str(output), + }, + ensure_ascii=False, + indent=2, + ) + ) + + +def main() -> None: + """Generate the complete offline demonstration in one command.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output", type=Path, default=Path("reports/booking-demo")) + run_demo(parser.parse_args().output) + + +if __name__ == "__main__": + main() diff --git a/pyqpanda-algorithm/example/LabScheduling/classical_scaling.py b/pyqpanda-algorithm/example/LabScheduling/classical_scaling.py new file mode 100644 index 00000000..22c20505 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/classical_scaling.py @@ -0,0 +1,70 @@ +"""Classical-only 20/35/50-task cascade stress cases; no quantum scaling claim.""" + +import argparse +import hashlib +import json +from pathlib import Path +from typing import Any + +from pyqpanda_alg.LabScheduling import Problem, compile_qubo, reduce_model, solve_milp + + +def generate_chain(tasks: int) -> dict[str, Any]: + """Create a connected shared-oven cascade with three choices per booking.""" + return { + "name": f"synthetic-classical-chain-{tasks}", + "horizon": 2 * tasks + 4, + "resources": [{"id": "oven", "cleanup": 1, "downtime": [[0, 2]]}], + "tasks": [ + { + "id": f"t{i}", + "group": f"g{i}", + "duration": 1, + "original": {"resource": "oven", "start": 2 * i}, + "change_cost": 2, + "options": [ + {"resource": "oven", "start": 2 * (i + j), "cost": j} + for j in range(3) + ], + } + for i in range(tasks) + ], + } + + +def main() -> None: + """Save full synthetic inputs and independent MILP certificates.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--output", type=Path, default=Path("reports/classical-scaling") + ) + output = parser.parse_args().output + output.mkdir(parents=True, exist_ok=True) + for count in (20, 35, 50): + data = generate_chain(count) + content = json.dumps(data, indent=2) + "\n" + (output / f"chain-{count}.json").write_text(content) + problem = Problem.from_dict(data) + certificate = solve_milp(problem) + reduction = reduce_model(compile_qubo(problem)) + report = { + "tasks": count, + "input_sha256": hashlib.sha256(content.encode()).hexdigest(), + "milp": certificate, + "reduction": reduction.audit(), + "quantum_executed": False, + "scope": "classical-only; connected component exceeds CPU quantum cap", + } + (output / f"chain-{count}-result.json").write_text( + json.dumps(report, indent=2, allow_nan=False) + "\n" + ) + print( + count, + certificate["status"], + certificate["best"]["objective"] if certificate["best"] else None, + reduction.audit()["max_component_qubits"], + ) + + +if __name__ == "__main__": + main() diff --git a/pyqpanda-algorithm/example/LabScheduling/constraints-py311.txt b/pyqpanda-algorithm/example/LabScheduling/constraints-py311.txt new file mode 100644 index 00000000..22971ea1 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/constraints-py311.txt @@ -0,0 +1,68 @@ +# Tested Linux x86_64 / Python 3.11 environment; see Tutorials/LabScheduling/COMPATIBILITY.md. +alabaster==1.0.0 +allure-pytest==2.16.1 +allure-python-commons==2.16.1 +ast_serialize==0.11.2 +astroid==4.3.1 +attrs==26.1.0 +babel==2.18.0 +certifi==2026.7.22 +charset-normalizer==3.5.1 +cloudpickle==3.1.2 +contourpy==1.3.3 +coverage==7.16.1 +cycler==0.12.1 +Cython==3.3.0 +docutils==0.22.4 +fonttools==4.65.0 +graphviz==0.21 +idna==3.20 +imagesize==2.0.1 +iniconfig==2.3.0 +Jinja2==3.1.6 +joblib==1.6.0 +kiwisolver==1.5.1 +librt==0.15.0 +MarkupSafe==3.0.3 +matplotlib==3.11.2 +mpmath==1.3.0 +mypy==2.3.1 +mypy_extensions==1.1.0 +narwhals==2.26.0 +networkx==3.6.1 +numpy==2.4.6 +packaging==26.3 +pandas==3.0.6 +pathspec==1.1.1 +pillow==12.3.0 +pluggy==1.6.0 +pycryptodome==3.23.0 +pydot==4.0.1 +Pygments==2.21.0 +pyparsing==3.3.3 +pyqpanda3==0.4.1 +pytest==9.1.1 +pytest-cov==7.1.0 +pytest-timeout==2.4.0 +python-dateutil==2.9.0.post0 +PyYAML==6.0.3 +requests==2.34.2 +roman-numerals==4.1.0 +ruff==0.16.8 +scikit-learn==1.9.1 +scipy==1.17.1 +setuptools==84.0.0 +six==1.17.0 +snowballstemmer==3.1.1 +Sphinx==9.0.4 +sphinx-autoapi==3.8.1 +sphinxcontrib-applehelp==2.0.0 +sphinxcontrib-devhelp==2.0.0 +sphinxcontrib-htmlhelp==2.1.0 +sphinxcontrib-jsmath==1.0.1 +sphinxcontrib-qthelp==2.0.0 +sphinxcontrib-serializinghtml==2.0.0 +sympy==1.14.0 +threadpoolctl==3.7.0 +typing_extensions==4.16.0 +urllib3==2.8.0 diff --git a/pyqpanda-algorithm/example/LabScheduling/constraints-py312.txt b/pyqpanda-algorithm/example/LabScheduling/constraints-py312.txt new file mode 100644 index 00000000..8b27615d --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/constraints-py312.txt @@ -0,0 +1,68 @@ +# Tested WSL x86_64 / Python 3.12.3 environment, 2026-09-21. +alabaster==1.0.0 +allure-pytest==2.16.1 +allure-python-commons==2.16.1 +ast_serialize==0.11.2 +astroid==4.3.1 +attrs==26.1.0 +babel==2.18.0 +certifi==2026.7.22 +charset-normalizer==3.5.1 +cloudpickle==3.1.2 +contourpy==1.4.0 +coverage==7.16.1 +cycler==0.12.1 +Cython==3.3.0 +docutils==0.22.4 +fonttools==4.65.0 +graphviz==0.21 +idna==3.20 +imagesize==2.0.1 +iniconfig==2.3.0 +Jinja2==3.1.6 +joblib==1.6.0 +kiwisolver==1.5.1 +librt==0.15.0 +MarkupSafe==3.0.3 +matplotlib==3.11.2 +mpmath==1.3.0 +mypy==2.3.1 +mypy_extensions==1.1.0 +narwhals==2.26.0 +networkx==3.6.1 +numpy==2.5.3 +packaging==26.3 +pandas==3.0.6 +pathspec==1.1.1 +pillow==12.3.0 +pluggy==1.6.0 +pycryptodome==3.23.0 +pydot==4.0.1 +Pygments==2.21.0 +pyparsing==3.3.3 +pyqpanda3==0.4.1 +pytest==9.1.1 +pytest-cov==7.1.0 +pytest-timeout==2.4.0 +python-dateutil==2.9.0.post0 +PyYAML==6.0.3 +requests==2.34.2 +roman-numerals==4.1.0 +ruff==0.16.8 +scikit-learn==1.9.1 +scipy==1.18.1 +setuptools==84.0.0 +six==1.17.0 +snowballstemmer==3.1.1 +Sphinx==9.1.0 +sphinx-autoapi==3.8.1 +sphinxcontrib-applehelp==2.0.0 +sphinxcontrib-devhelp==2.0.0 +sphinxcontrib-htmlhelp==2.1.0 +sphinxcontrib-jsmath==1.0.1 +sphinxcontrib-qthelp==2.0.0 +sphinxcontrib-serializinghtml==2.0.0 +sympy==1.14.0 +threadpoolctl==3.7.0 +typing_extensions==4.16.0 +urllib3==2.8.0 diff --git a/pyqpanda-algorithm/example/LabScheduling/data/bookings/chain.csv b/pyqpanda-algorithm/example/LabScheduling/data/bookings/chain.csv new file mode 100644 index 00000000..75e86dbe --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/data/bookings/chain.csv @@ -0,0 +1,6 @@ +id,group,duration,original_resource,original_start,allowed_resources,earliest,latest,step,change_cost,shift_cost +A-heat,A,1,oven,0,oven,0,1,1,1,0 +B-heat,B,1,oven,1,oven,1,2,1,2,0 +C-heat,C,1,oven,2,oven,2,3,1,3,0 +D-image,D,1,scope,1,scope,1,2,1,2,0 +E-image,E,1,scope,2,scope,1,2,1,2,0 diff --git a/pyqpanda-algorithm/example/LabScheduling/data/bookings/outage.json b/pyqpanda-algorithm/example/LabScheduling/data/bookings/outage.json new file mode 100644 index 00000000..4bda6c6e --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/data/bookings/outage.json @@ -0,0 +1,9 @@ +{ + "name": "合成业务案例：烘箱停机引发三单连锁改约，显微预约保持不动", + 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b/pyqpanda-algorithm/example/LabScheduling/data/medium.json @@ -0,0 +1,37 @@ +{ + "name": "双设备四实验：停机、清洗、小组冲突与前序工艺", + "horizon": 12, + "resources": [ + {"id": "spectrometer", "cleanup": 1, "downtime": [[0, 2]]}, + {"id": "microscope", "cleanup": 1, "downtime": [[7, 8]]} + ], + "tasks": [ + {"id": "A-prepare", "group": "group-A", "duration": 2, + "original": {"resource": "spectrometer", "start": 0}, "change_cost": 4, + "options": [ + {"resource": "spectrometer", "start": 0, "cost": 0}, + {"resource": "spectrometer", "start": 2, "cost": 0}, + {"resource": "spectrometer", "start": 5, "cost": 1}, + {"resource": "spectrometer", "start": 8, "cost": 2}]}, + {"id": "A-image", "group": "group-A", "duration": 2, + "original": {"resource": "microscope", "start": 4}, "change_cost": 4, + "options": [ + {"resource": "microscope", "start": 2, "cost": 0}, + {"resource": "microscope", "start": 4, "cost": 0}, + {"resource": "microscope", "start": 8, "cost": 1}, + {"resource": "microscope", "start": 6, "cost": 0}]}, + {"id": "B-measure", "group": "group-B", "duration": 2, + "original": {"resource": "spectrometer", "start": 5}, "change_cost": 3, + "options": [ + {"resource": "spectrometer", "start": 2, "cost": 1}, + {"resource": "spectrometer", "start": 5, "cost": 0}, + {"resource": "spectrometer", "start": 8, "cost": 1}]}, + {"id": "B-image", "group": "group-B", "duration": 2, + "original": {"resource": "microscope", "start": 0}, "change_cost": 3, + "options": [ + {"resource": "microscope", "start": 0, "cost": 0}, + {"resource": "microscope", "start": 4, "cost": 1}, + {"resource": "microscope", "start": 8, "cost": 2}]} + ], + "precedence": [["A-prepare", "A-image"]] +} diff --git a/pyqpanda-algorithm/example/LabScheduling/data/sensitivity.json b/pyqpanda-algorithm/example/LabScheduling/data/sensitivity.json new file mode 100644 index 00000000..d1fd2b69 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/data/sensitivity.json @@ -0,0 +1,26 @@ +{ + "base_input": "tradeoff.json", + "base_sha256": "fb186f6154a157be65af2f6b9e65c295c016791fd60e21b1e257ab35b2f63b9f", + "weights": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6 + ], + "seeds": [ + 7, + 19, + 42 + ], + "solver": { + "reduce": true, + "layers": 1, + "shots": 512, + "maxiter": 60, + "restarts": 2, + "mixer": "xy" + } +} diff --git a/pyqpanda-algorithm/example/LabScheduling/data/simple.json b/pyqpanda-algorithm/example/LabScheduling/data/simple.json new file mode 100644 index 00000000..8d5ee457 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/data/simple.json @@ -0,0 +1,20 @@ +{ + "name": "共享光谱仪故障后的两组重排", + "horizon": 9, + "resources": [{"id": "spectrometer", "cleanup": 1, "downtime": [[0, 1]]}], + "tasks": [ + {"id": "sample-A", "group": "group-A", "duration": 2, + "original": {"resource": "spectrometer", "start": 0}, "change_cost": 3, + "options": [ + {"resource": "spectrometer", "start": 0, "cost": 0}, + {"resource": "spectrometer", "start": 1, "cost": 0}, + {"resource": "spectrometer", "start": 3, "cost": 1}, + {"resource": "spectrometer", "start": 6, "cost": 2}]}, + {"id": "sample-B", "group": "group-B", "duration": 2, + "original": {"resource": "spectrometer", "start": 3}, "change_cost": 3, + "options": [ + {"resource": "spectrometer", "start": 1, "cost": 1}, + {"resource": "spectrometer", "start": 3, "cost": 0}, + {"resource": "spectrometer", "start": 6, "cost": 1}]} + ] +} diff --git a/pyqpanda-algorithm/example/LabScheduling/data/support-chain.json b/pyqpanda-algorithm/example/LabScheduling/data/support-chain.json new file mode 100644 index 00000000..447fd395 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/data/support-chain.json @@ -0,0 +1,174 @@ +{ + "name": "Synthetic outage: unsupported late processing windows", + "horizon": 13, + "resources": [ + { + "id": "oven", + "downtime": [ + [ + 0, + 1 + ] + ] + } + ], + "tasks": [ + { + "id": "t0", + "group": "g0", + "duration": 1, + "original": { + "resource": "oven", + "start": 0 + }, + "change_cost": 2, + "options": [ + { + "resource": "oven", + "start": 0, + "cost": 0 + }, + { + "resource": "oven", + "start": 1, + "cost": 1 + }, + { + "resource": "oven", + "start": 2, + "cost": 2 + }, + { + "resource": "oven", + "start": 3, + "cost": 3 + }, + { + "resource": "oven", + "start": 8, + "cost": 8 + }, + { + "resource": "oven", + "start": 9, + "cost": 9 + } + ] + }, + { + "id": "t1", + "group": "g1", + "duration": 1, + "original": { + "resource": "oven", + "start": 2 + }, + "change_cost": 2, + "options": [ + { + "resource": "oven", + "start": 2, + "cost": 0 + }, + { + "resource": "oven", + "start": 3, + "cost": 1 + }, + { + "resource": "oven", + "start": 4, + "cost": 2 + }, + { + "resource": "oven", + "start": 9, + "cost": 7 + }, + { + "resource": "oven", + "start": 10, + "cost": 8 + } + ] + }, + { + "id": "t2", + "group": "g2", + "duration": 1, + "original": { + "resource": "oven", + "start": 4 + }, + "change_cost": 2, + "options": [ + { + "resource": "oven", + "start": 3, + "cost": 1 + }, + { + "resource": "oven", + "start": 4, + "cost": 0 + }, + { + "resource": "oven", + "start": 5, + "cost": 1 + }, + { + "resource": "oven", + "start": 10, + "cost": 6 + }, + { + "resource": "oven", + "start": 11, + "cost": 7 + } + ] + }, + { + "id": "t3", + "group": "g3", + "duration": 1, + "original": { + "resource": "oven", + "start": 6 + }, + "change_cost": 2, + "options": [ + { + "resource": "oven", + "start": 4, + "cost": 2 + }, + { + "resource": "oven", + "start": 5, + "cost": 1 + }, + { + "resource": "oven", + "start": 6, + "cost": 0 + } + ] + } + ], + "precedence": [ + [ + "t0", + "t1" + ], + [ + "t1", + "t2" + ], + [ + "t2", + "t3" + ] + ] +} diff --git a/pyqpanda-algorithm/example/LabScheduling/data/tradeoff.json b/pyqpanda-algorithm/example/LabScheduling/data/tradeoff.json new file mode 100644 index 00000000..fa7f6a26 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/data/tradeoff.json @@ -0,0 +1,38 @@ +{ + "name": "合成连锁改约：低成本整体后移或高成本备用设备", + "horizon": 14, + "resources": [ + {"id": "oven", "cleanup": 1, "downtime": [[0, 1]]}, + {"id": "backup", "cleanup": 1} + ], + "tasks": [ + { + "id": "A", "group": "gA", "duration": 2, + "original": {"resource": "oven", "start": 0}, "change_cost": 2, + "options": [ + {"resource": "oven", "start": 0, "cost": 0}, + {"resource": "oven", "start": 2, "cost": 2}, + {"resource": "backup", "start": 0, "cost": 12} + ] + }, + { + "id": "B", "group": "gB", "duration": 1, + "original": {"resource": "oven", "start": 3}, "change_cost": 2, + "options": [ + {"resource": "oven", "start": 3, "cost": 0}, + {"resource": "oven", "start": 5, "cost": 2}, + {"resource": "oven", "start": 8, "cost": 5} + ] + }, + { + "id": "C", "group": "gC", "duration": 2, + "original": {"resource": "oven", "start": 5}, "change_cost": 2, + "options": [ + {"resource": "oven", "start": 5, "cost": 0}, + {"resource": "oven", "start": 7, "cost": 2}, + {"resource": "oven", "start": 10, "cost": 5} + ] + } + ], + "precedence": [["A", "B"], ["B", "C"]] +} diff --git a/pyqpanda-algorithm/example/LabScheduling/make_corpus.py b/pyqpanda-algorithm/example/LabScheduling/make_corpus.py new file mode 100644 index 00000000..7e4cfe69 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/make_corpus.py @@ -0,0 +1,108 @@ +"""Deterministic synthetic corpus; no filtering by feasibility or solver outcome.""" + +import argparse +import hashlib +import json +from pathlib import Path +from typing import Any + +import numpy as np + + +def generate(tasks: int, density: int, seed: int) -> dict[str, Any]: + """Generate two surviving choices per task plus one outage-pruned booking. + + Density labels control resource count and slot range, not measured density. + All generated instances are retained, including infeasible instances. + """ + rng = np.random.default_rng(seed) + resource_count = (3, 2, 1)[density] + span = (2 * tasks, tasks + 1, max(3, tasks // 2 + 1))[density] + resources = [ + {"id": f"r{i}", "cleanup": 0, "downtime": [[0, 1]] if i == 0 else []} + for i in range(resource_count) + ] + rows = [] + for ti in range(tasks): + universe = [ + (f"r{r}", s) for r in range(resource_count) for s in range(1, span + 1) + ] + indices = rng.choice(len(universe), size=2, replace=False) + options = [ + { + "resource": universe[i][0], + "start": universe[i][1], + "cost": int(rng.integers(0, 5)), + } + for i in indices + ] + original = {"resource": options[0]["resource"], "start": options[0]["start"]} + if ti == 0: + options.insert(0, {"resource": "r0", "start": 0, "cost": 0}) + original = {"resource": "r0", "start": 0} + rows.append( + { + "id": f"t{ti}", + "group": f"g{ti // 2}", + "duration": 1, + "options": options, + "original": original, + "change_cost": int(rng.integers(1, 5)), + } + ) + return { + "name": f"synthetic-n{tasks}-d{density}-s{seed}", + "horizon": span + 1, + "resources": resources, + "tasks": rows, + "precedence": [["t0", "t1"]] if density == 1 else [], + } + + +def write_corpus(output: Path) -> None: + """Freeze 12 evaluation inputs, one development input and content hashes.""" + output.mkdir(parents=True, exist_ok=True) + records = [] + specs = [("development", 3, 0, 20260921)] + [ + ("evaluation", n, density, 20260922 + 10 * n + density) + for n in (3, 5, 6, 8) + for density in range(3) + ] + for split, tasks, density, seed in specs: + raw = generate(tasks, density, seed) + filename = f"{split}-n{tasks}-d{density}.json" + content = json.dumps(raw, ensure_ascii=False, indent=2) + "\n" + (output / filename).write_text(content, encoding="utf-8") + records.append( + { + "file": filename, + "split": split, + "tasks": tasks, + "density_parameter": density, + "data_seed": seed, + "sha256": hashlib.sha256(content.encode()).hexdigest(), + } + ) + manifest = { + "schema_version": 1, + "synthetic": True, + "generator_sha256": hashlib.sha256(Path(__file__).read_bytes()).hexdigest(), + "optimization_seeds": list(range(10)), + "training": {"layers": 1, "maxiter": 60, "restarts": 2}, + "shots": [8, 16, 32, 64, 128, 512], + "instances": records, + } + (output / "manifest.json").write_text(json.dumps(manifest, indent=2) + "\n") + + +def main() -> None: + """Write or identically regenerate the fixed corpus.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--output", type=Path, default=Path(__file__).parent / "data/corpus" + ) + write_corpus(parser.parse_args().output) + + +if __name__ == "__main__": + main() diff --git a/pyqpanda-algorithm/example/LabScheduling/make_corpus_v2.py b/pyqpanda-algorithm/example/LabScheduling/make_corpus_v2.py new file mode 100644 index 00000000..a184eb69 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/make_corpus_v2.py @@ -0,0 +1,121 @@ +"""Freeze heterogeneous synthetic bookings before evaluating any solver.""" + +import argparse +import hashlib +import json +from pathlib import Path +from typing import Any + +import numpy as np + + +def generate(tasks: int, scenario: int, seed: int) -> dict[str, Any]: + """Create valid original bookings, then introduce an unfiltered outage. + + Original reservations are sequential even across resources. Alternatives + vary in start, resource and cost; no feasibility or optimizer filtering is + used. Scenarios select one short outage, two extended outages, or a chain. + """ + rng = np.random.default_rng(seed) + resources = [ + {"id": f"r{i}", "cleanup": int(rng.integers(0, 3)), "downtime": []} + for i in range(3) + ] + rows = [] + start = 0 + for ti in range(tasks): + resource = resources[ti % 3] + duration = int(rng.integers(1, 4)) + count = (2, 3, 4)[ti % 3] + original = {"resource": resource["id"], "start": start} + universe = [ + (r["id"], s) + for r in resources + for s in range(max(0, start - 3), start + 7) + if (r["id"], s) != (resource["id"], start) + ] + choices = rng.choice(len(universe), count - 1, replace=False) + options = [{**original, "cost": 0}] + [ + { + "resource": universe[i][0], + "start": universe[i][1], + "cost": abs(universe[i][1] - start) + int(rng.integers(0, 4)), + } + for i in choices + ] + rows.append( + { + "id": f"t{ti}", + "group": f"g{ti % 2}", + "duration": duration, + "original": original, + "change_cost": int(rng.integers(1, 5)), + "options": options, + } + ) + start += duration + resource["cleanup"] + int(rng.integers(0, 2)) + for ti in (0, 1) if scenario == 1 else (0,): + task = rows[ti] + left = task["original"]["start"] + length = task["duration"] + resources[ti % 3]["cleanup"] + resources[ti % 3]["downtime"] = [ + [left, left + (length + 2 if scenario == 1 else 1)] + ] + return { + "name": f"heterogeneous-n{tasks}-s{scenario}-seed{seed}", + "horizon": start + 10, + "resources": resources, + "tasks": rows, + "precedence": [[f"t{i}", f"t{i + 1}"] for i in range(tasks - 1)] + if scenario == 2 + else [], + } + + +def write_corpus(output: Path) -> None: + """Write all 36 registered instances, hashes and unchanged training budgets.""" + output.mkdir(parents=True, exist_ok=True) + records = [] + for tasks in (3, 4, 5): + for scenario in range(3): + for replicate in range(4): + seed = 2026092300 + tasks * 100 + scenario * 10 + replicate + raw = generate(tasks, scenario, seed) + name = f"n{tasks}-s{scenario}-r{replicate}.json" + content = json.dumps(raw, ensure_ascii=False, indent=2) + "\n" + (output / name).write_text(content, encoding="utf-8") + records.append( + { + "file": name, + "tasks": tasks, + "scenario": scenario, + "replicate": replicate, + "data_seed": seed, + "sha256": hashlib.sha256(content.encode()).hexdigest(), + } + ) + manifest = { + "version": 2, + "generator_sha256": hashlib.sha256(Path(__file__).read_bytes()).hexdigest(), + "instances": records, + "optimization_seeds": list(range(10)), + "training": {"layers": 1, "shots": 512, "maxiter": 60, "restarts": 2}, + "shots": [8, 16, 32, 64, 128, 512], + "selection": "all instances retained; no solver-based selection or tuning", + } + (output / "manifest.json").write_text( + json.dumps(manifest, indent=2) + "\n", encoding="utf-8" + ) + + +def main() -> None: + """Generate the separately versioned corpus without changing v1.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--output", type=Path, default=Path(__file__).parent / "data/corpus-v2" + ) + write_corpus(parser.parse_args().output) + + +if __name__ == "__main__": + main() diff --git a/pyqpanda-algorithm/example/LabScheduling/matched_budget.py b/pyqpanda-algorithm/example/LabScheduling/matched_budget.py new file mode 100644 index 00000000..7f7693ba --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/matched_budget.py @@ -0,0 +1,326 @@ +"""Compare four scheduling paths under equal total sampling/training caps.""" + +import argparse +import hashlib +import json +import platform +from collections import Counter +from importlib.metadata import version +from math import prod +from pathlib import Path +from time import perf_counter +from typing import Any + +import numpy as np +from benchmark_v2 import check_sample +from pyqpanda_alg.LabScheduling import ( + Model, + Reduction, + circuit_probabilities, + compile_qubo, + evaluate, + load_problem, + reduce_model, + solve_exact, + solve_qaoa, +) +from pyqpanda_alg.LabScheduling.metrics import distribution_metrics, hit_probability +from pyqpanda_alg.LabScheduling.quantum import MAX_QUBITS +from stress_benchmark import write_json + +HERE = Path(__file__).resolve().parent +ROOT = HERE.parents[2] + + +def allocate(total: int, count: int, minimum: int = 1) -> list[int]: + """Split a positive budget deterministically; never exceed the total cap.""" + if any(type(v) is not int for v in (total, count, minimum)): + raise ValueError("budgets and counts must be integers") + if total < 1 or count < 0 or minimum < 1 or total < count * minimum: + raise ValueError("insufficient or invalid budget") + if not count: + return [] + quotient, remainder = divmod(total, count) + return [quotient + int(i < remainder) for i in range(count)] + + +def uniform(model: Model, seed: int, shots: int) -> dict[str, Any]: + """Sample one-hot schedules without repair or consulting an exact optimum.""" + started = perf_counter() + rng = np.random.default_rng(np.random.SeedSequence([seed, 20260930])) + counts: Counter[str] = Counter() + best = None + if all(model.domains): + for _ in range(shots): + bits = [0] * len(model.variables) + for domain in model.domains: + bits[int(rng.choice(domain))] = 1 + counts["".join(map(str, bits[::-1]))] += 1 + row = evaluate(model, bits) + if best is None or (not row["feasible"], row["qubo_energy"], bits) < ( + not best["feasible"], + best["qubo_energy"], + best["bits"], + ): + best = row + return { + "status": "infeasible_domain" + if best is None + else "feasible" + if best["feasible"] + else "no_feasible_sample", + "best": best, + **({"counts": dict(counts)} if best is not None else {}), + "shots": sum(counts.values()), + "seed": seed, + "circuit_evaluations": 0, + "runtime_seconds": perf_counter() - started, + } + + +def components( + reduction: Reduction, + seed: int, + *, + quantum: bool, + shots: int = 512, + per_restart: int = 60, +) -> dict[str, Any]: + """Divide a fixed total budget over exact components, then restore samples.""" + started = perf_counter() + models = reduction.components + runs = [] + best = None + allocations = [] + if reduction.infeasible_task is not None: + status = "infeasible_propagation" + elif quantum and any(len(m.variables) > MAX_QUBITS for m in models): + status = "resource_limit" + else: + try: + sampled = allocate(shots, len(models)) + iterations = ( + allocate(per_restart, len(models), 6) if quantum else [0] * len(models) + ) + except ValueError: + status = "budget_insufficient" + else: + seeds = ( + [seed] + if len(models) == 1 + else [ + int(s.generate_state(1)[0]) + for s in np.random.SeedSequence(seed).spawn(len(models)) + ] + ) + for model, child_seed, budget, maxiter in zip( + models, seeds, sampled, iterations, strict=True + ): + run = ( + solve_qaoa(model, child_seed, shots=budget, maxiter=maxiter) + if quantum + else uniform(model, child_seed, budget) + ) + check_sample(model, run) + runs.append(run) + allocations.append( + { + "shots": budget, + "maxiter_per_restart": maxiter, + "seed": child_seed, + } + ) + best = reduction.restore([r["best"]["bits"] for r in runs]) + status = ( + "classical_propagation" + if not models + else "feasible" + if best["feasible"] + else "no_feasible_sample" + ) + return { + "status": status, + "best": best, + "components": runs, + "allocations": allocations, + "shots": sum(r["shots"] for r in runs), + "circuit_evaluations": sum(r["circuit_evaluations"] for r in runs), + "runtime_seconds": perf_counter() - started, + } + + +def joint_hit(probabilities: list[float], shots: int) -> float: + """Chance every component yields an optimum with a shared total shot cap.""" + return prod( + hit_probability(p, s) + for p, s in zip(probabilities, allocate(shots, len(probabilities)), strict=True) + ) + + +def profile(model: Model, run: dict[str, Any]) -> dict[str, Any] | None: + """Certify all tied optima only after every solver has finished.""" + if "parameters" not in run: + return None + result = distribution_metrics( + model, circuit_probabilities(model, run["parameters"]) + ) + return { + k: result[k] + for k in ( + "exact_objective", + "optimal_count", + "combinations", + "optimal_probability", + "uniform_optimal_probability", + ) + } + + +def compare(model: Model, reduction: Reduction, seed: int) -> dict[str, Any]: + """Run four uncorrected samplers, then add certificates and budget audits.""" + direct = solve_qaoa(model, seed=seed) + reduced = components(reduction, seed, quantum=True) + random_direct = uniform(model, seed, 512) + random_reduced = components(reduction, seed, quantum=False) + paths = { + "direct": direct, + "reduced": reduced, + "uniform_direct": random_direct, + "uniform_reduced": random_reduced, + } + for run in paths.values(): + check_sample(model, run) + if run["circuit_evaluations"] > 120: + raise RuntimeError("training cap exceeded") + used = run.get("shots", 0) + if used > 512 or (run.get("counts") and sum(run["counts"].values()) != used): + raise RuntimeError("sampling cap violated") + started = perf_counter() + exact = solve_exact(model) + optimum = exact["best"]["objective"] if exact["best"] else None + direct_profile = profile(model, direct) + local = [ + profile(m, r) + for m, r in zip(reduction.components, reduced["components"], strict=True) + ] + curves = [] + if optimum is not None: + for shots in (8, 16, 32, 64, 128, 512): + curves.append( + { + "total_shots": shots, + "direct": hit_probability( + direct_profile["optimal_probability"], shots + ), + "uniform_direct": hit_probability( + direct_profile["uniform_optimal_probability"], shots + ), + "reduced": joint_hit( + [m["optimal_probability"] for m in local], shots + ), + "uniform_reduced": joint_hit( + [m["uniform_optimal_probability"] for m in local], shots + ), + } + ) + return { + "seed": seed, + "paths": paths, + "exact_objective": optimum, + "absolute_gaps": { + key: run["best"]["objective"] - optimum + if optimum is not None and run["best"] and run["best"]["feasible"] + else None + for key, run in paths.items() + }, + "direct_profile": direct_profile, + "component_profiles": local, + "conditional_hit_curve": curves, + "diagnostic_seconds": perf_counter() - started, + } + + +def run(corpus: Path, output: Path) -> None: + """Execute every frozen input/seed; save all failures and source fingerprints.""" + manifest_path = corpus / "manifest.json" + manifest = json.loads(manifest_path.read_text()) + generator = HERE / "make_corpus_v2.py" + if ( + hashlib.sha256(generator.read_bytes()).hexdigest() + != manifest["generator_sha256"] + ): + raise ValueError("frozen generator changed") + for item in manifest["instances"]: + if ( + hashlib.sha256((corpus / item["file"]).read_bytes()).hexdigest() + != item["sha256"] + ): + raise ValueError(f"frozen input changed: {item['file']}") + import pyqpanda_alg.LabScheduling as package + + output.mkdir(parents=True, exist_ok=True) + sources = [ + Path(__file__), + HERE / "benchmark_v2.py", + HERE / "ablation.py", + HERE / "stress_benchmark.py", + generator, + ROOT / "Tutorials/LabScheduling/MATCHED_BUDGET_PROTOCOL.md", + *Path(package.__file__).parent.glob("*.py"), + ] + write_json( + output / "provenance.json", + { + "manifest": manifest, + "manifest_sha256": hashlib.sha256(manifest_path.read_bytes()).hexdigest(), + "sources": { + str(p.relative_to(ROOT)): hashlib.sha256(p.read_bytes()).hexdigest() + for p in sources + }, + "python": platform.python_version(), + "platform": platform.platform(), + "packages": { + n: version(n) for n in ("numpy", "scipy", "pyqpanda3", "pyqpanda_alg") + }, + "training": { + "layers": 1, + "restarts": 2, + "total_evaluation_cap": 120, + "total_shots": 512, + }, + }, + ) + started = perf_counter() + count = 0 + with (output / "runs.jsonl").open("w", encoding="utf-8") as stream: + for item in manifest["instances"]: + model = compile_qubo(load_problem(corpus / item["file"])) + reduction = reduce_model(model) + for seed in manifest["optimization_seeds"]: + row = {"instance": item["file"], **compare(model, reduction, seed)} + stream.write( + json.dumps( + row, ensure_ascii=False, allow_nan=False, separators=(",", ":") + ) + + "\n" + ) + count += 1 + stream.flush() + print(item["file"], "complete", flush=True) + write_json( + output / "execution.json", + {"paired_records": count, "runtime_seconds": perf_counter() - started}, + ) + + +def main() -> None: + """Run the fixed supplemental budget audit without modifying older archives.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--corpus", type=Path, default=HERE / "data/corpus-v2") + parser.add_argument("--output", type=Path, default=Path("reports/lab-matched")) + args = parser.parse_args() + run(args.corpus, args.output) + + +if __name__ == "__main__": + main() diff --git a/pyqpanda-algorithm/example/LabScheduling/plot_results.py b/pyqpanda-algorithm/example/LabScheduling/plot_results.py new file mode 100644 index 00000000..9ad1438e --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/plot_results.py @@ -0,0 +1,132 @@ +"""Render a shareable SVG from saved measurements, without rerunning a solver.""" + +import argparse +import csv +import hashlib +import json +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np + + +def main() -> None: + """Plot all seeds' feasibility and the medium XY seed-7 schedule.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("results", type=Path) + args = parser.parse_args() + with (args.results / "summary.csv").open(encoding="utf-8") as stream: + rows = list(csv.DictReader(stream)) + report = json.loads((args.results / "medium-xy-7.json").read_text(encoding="utf-8")) + source = Path(__file__).parent / "data" / "medium.json" + if hashlib.sha256(source.read_bytes()).hexdigest() != report["input_sha256"]: + raise ValueError("medium input changed since these results were recorded") + problem = json.loads(source.read_text(encoding="utf-8")) + seeds = ", ".join(dict.fromkeys(row["seed"] for row in rows)) + best = report["quantum"]["best"] + fig, axes = plt.subplots(1, 2, figsize=(12, 4.6), layout="constrained") + for i, dataset in enumerate(("simple", "medium")): + for j, mixer in enumerate(("xy", "x")): + values = [ + float(r["feasible_probability"]) + for r in rows + if r["dataset"] == dataset and r["mixer"] == mixer + ] + axes[0].bar( + i + (j - 0.5) * 0.3, + np.mean(values), + width=0.27, + color=("#167d9a", "#c67944")[j], + label=("XY", "X")[j] if i == 0 else None, + ) + axes[0].scatter( + np.full(len(values), i + (j - 0.5) * 0.3), + values, + color="#152536", + s=18, + zorder=3, + ) + uniform = float( + next(r for r in rows if r["dataset"] == dataset)[ + "uniform_feasible_probability" + ] + ) + axes[0].hlines( + uniform, + i - 0.4, + i + 0.4, + color="#586363", + linestyles="--", + label="Uniform one-hot" if i == 0 else None, + ) + axes[0].set( + xticks=[0, 1], + xticklabels=["Simple (6 qubits)", "Medium (12 qubits)"], + ylabel="Feasible Born probability", + ylim=(0, 1), + title=f"Seeds: {seeds}; p={report['quantum']['layers']}, " + f"{report['quantum']['shots']} shots", + ) + axes[0].legend(frameon=False) + axes[0].grid(axis="y", alpha=0.15) + resource_y = {"spectrometer": 1, "microscope": 0} + for row in report["quantum"]["best"]["schedule"]: + y = resource_y[row["resource"]] + color = "#167d9a" if row["group"] == "group-A" else "#c67944" + axes[1].barh( + y, row["end"] - row["start"], left=row["start"], height=0.5, color=color + ) + axes[1].barh( + y, + row["reserved_end"] - row["end"], + left=row["end"], + height=0.5, + color=color, + alpha=0.25, + hatch="///", + ) + axes[1].text( + (row["start"] + row["end"]) / 2, + y, + row["task"], + ha="center", + va="center", + color="white", + fontsize=9, + ) + for resource in problem["resources"]: + for start, end in resource.get("downtime", []): + axes[1].barh( + resource_y[resource["id"]], + end - start, + left=start, + height=0.65, + color="#cccccc", + hatch="xx", + ) + axes[1].set( + yticks=[0, 1], + yticklabels=["Microscope", "Spectrometer"], + xticks=range(13), + xlim=(0, 12), + xlabel="Time slot (hatched tail = cleanup)", + title=f"Medium / XY / seed 7: cost {best['objective']}, " + f"violations {best['violation_count']}", + ) + axes[1].grid(axis="x", alpha=0.15) + matplotlib.rcParams["svg.hashsalt"] = "lab-reschedule" + matplotlib.rcParams["svg.fonttype"] = "none" + output = args.results / "overview.svg" + fig.savefig(output, metadata={"Date": None}) + output.write_text( + "\n".join(line.rstrip() for line in output.read_text().splitlines()) + "\n", + encoding="utf-8", + ) + plt.close(fig) + + +if __name__ == "__main__": + main() diff --git a/pyqpanda-algorithm/example/LabScheduling/requirements-dev.txt b/pyqpanda-algorithm/example/LabScheduling/requirements-dev.txt new file mode 100644 index 00000000..3d7b61be --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/requirements-dev.txt @@ -0,0 +1,9 @@ +# Upstream eager imports currently need these undeclared runtime dependencies. +pandas +scikit-learn +# Application verification tools. +pytest +pytest-cov +pytest-timeout +allure-pytest +ruff diff --git a/pyqpanda-algorithm/example/LabScheduling/sensitivity.py b/pyqpanda-algorithm/example/LabScheduling/sensitivity.py new file mode 100644 index 00000000..0fee2326 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/sensitivity.py @@ -0,0 +1,306 @@ +"""Frozen change-cost analysis for the existing three-booking tradeoff demo.""" + +import copy +import csv +import hashlib +import json +from itertools import product +from pathlib import Path +from typing import Any + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from pyqpanda_alg.LabScheduling import Problem, load_problem, validate_assignment +from pyqpanda_alg.LabScheduling import report as report_module +from stress_benchmark import write_json +from summarize_matched import without_timing + +HERE = Path(__file__).resolve().parent + + +def weighted_input(base: dict[str, Any], weight: int) -> dict[str, Any]: + """Copy an input and change only the uniform nonnegative integer change cost.""" + if type(weight) is not int or not 0 <= weight <= 1_000_000: + raise ValueError("weight must be an integer in [0, 1000000]") + result = copy.deepcopy(base) + for task in result["tasks"]: + task["change_cost"] = weight + Problem.from_dict(result) + return result + + +def feasible_schedules(problem: Problem) -> list[dict[str, Any]]: + """Enumerate raw choices with the independent semantic checker for this demo.""" + combinations = 1 + for task in problem.tasks: + combinations *= len(task.options) + if combinations > 1_000_000: + raise ValueError("sensitivity enumeration is limited to one million choices") + rows = [] + for choices in product(*(range(len(t.options)) for t in problem.tasks)): + row = validate_assignment(problem, choices) + if row["feasible"]: + rows.append(row) + return rows + + +def sample_metrics( + report: dict[str, Any], alternatives: list[dict[str, Any]] +) -> dict[str, Any]: + """Accept any tied optimum; keep infeasible and nonoptimal samples unmodified.""" + best = report["quantum"]["best"] + feasible = best is not None and best["feasible"] + optimum = min(row["objective"] for row in alternatives) + business = report["quantum"].get("business_metrics") if feasible else None + if feasible and business is None: + raise ValueError("feasible sample lacks a raw-input business audit") + gap = best["objective"] - optimum if feasible else None + if gap != report["absolute_gap"] or gap is not None and gap < 0: + raise ValueError("sample gap contradicts independent enumeration") + if feasible: + matching = [r for r in alternatives if r["choices"] == business["choices"]] + if len(matching) != 1 or matching[0] != business: + raise ValueError("sample business audit differs from raw enumeration") + return { + "seed": report["quantum"]["seed"], + "status": report["status"], + "objective": best["objective"] if feasible else None, + "violation_count": best["violation_count"] if best is not None else None, + "preference_cost": business["preference_cost"] if business else None, + "change_cost": business["change_cost"] if business else None, + "changed_tasks": business["changed_tasks"] if business else None, + "choices": business["choices"] if business else None, + "absolute_gap": gap, + "optimal": feasible and gap == 0, + "runtime_seconds": report["runtime_seconds"], + } + + +def verify_replay(original: Path, current: dict[str, Any]) -> None: + """Reject provenance or sample changes, normalizing both sides through JSON.""" + recorded = json.loads(original.read_text(encoding="utf-8")) + normalized = json.loads(json.dumps(current, allow_nan=False)) + if without_timing(recorded) != without_timing(normalized): + raise ValueError("sensitivity replay differs beyond timing") + + +def _plot(rows: list[dict[str, Any]], output: Path) -> None: + """Plot every certified cost line and actual gaps without hiding failures.""" + matplotlib.rcParams["svg.hashsalt"] = "lab-sensitivity" + matplotlib.rcParams["svg.fonttype"] = "none" + fig, (costs, gaps) = plt.subplots(1, 2, figsize=(11, 4), layout="constrained") + weights = [row["weight"] for row in rows] + for index in range(len(rows[0]["alternatives"])): + costs.plot( + weights, + [r["alternatives"][index]["objective"] for r in rows], + color="#bbbbbb", + linewidth=1, + alpha=0.6, + ) + costs.plot(weights, [6 + 3 * w for w in weights], label="Shift all: 6 + 3w") + costs.plot(weights, [12 + w for w in weights], label="Backup: 12 + w") + costs.scatter( + weights, + [r["optimum"] for r in rows], + marker="*", + s=80, + color="black", + label="Certified optimum", + zorder=4, + ) + costs.axvline(3, linestyle=":", color="gray") + costs.set( + xlabel="Change cost w per changed booking", + ylabel="Total cost (points)", + title="All nine feasible schedules; synthetic data", + ) + costs.legend(fontsize=8) + for index, marker in enumerate(("o", "x", "+")): + samples = [r["samples"][index] for r in rows] + gaps.scatter( + [ + w + for w, s in zip(weights, samples, strict=True) + if s["absolute_gap"] is not None + ], + [s["absolute_gap"] for s in samples if s["absolute_gap"] is not None], + marker=marker, + s=70, + label=f"Seed {samples[0]['seed']}", + ) + failures = [ + (r["weight"], s["seed"]) + for r in rows + for s in r["samples"] + if s["absolute_gap"] is None + ] + gaps.set( + xlabel="Change cost w per changed booking", + ylabel="Sampled optimality gap", + title="Actual QAOA samples (overlapping points retained)", + ) + gaps.text( + 0.02, + 0.96, + f"No feasible sample: {failures or 'none'}", + transform=gaps.transAxes, + va="top", + fontsize=8, + ) + gaps.legend(loc="lower right", fontsize=8) + for ax in (costs, gaps): + ax.set_xticks(weights) + ax.grid(alpha=0.2) + fig.savefig(output / "sensitivity.svg", metadata={"Date": None}) + plt.close(fig) + + +def run_sensitivity( + output: Path, *, verify_against: Path | None = None +) -> dict[str, Any]: + """Run the frozen 21 experiments, certify all choices, and save full evidence.""" + config_path = HERE / "data/sensitivity.json" + config = json.loads(config_path.read_text(encoding="utf-8")) + source = HERE / "data" / config["base_input"] + snapshot = source.read_bytes() + if hashlib.sha256(snapshot).hexdigest() != config["base_sha256"]: + raise ValueError("sensitivity base input changed after protocol freeze") + base = json.loads(snapshot) + inputs = output / "inputs" + inputs.mkdir(parents=True, exist_ok=True) + rows, records, input_hashes = [], [], [] + choices_reference = None + for weight in config["weights"]: + path = inputs / f"w{weight}.json" + write_json(path, weighted_input(base, weight)) + reports = [ + report_module.run_experiment(path, seed=seed, **config["solver"]) + for seed in config["seeds"] + ] + # Enumeration is downstream of all actual quantum runs for this input. + alternatives = feasible_schedules(load_problem(path)) + choices = [row["choices"] for row in alternatives] + if ( + not alternatives + or choices_reference is not None + and choices != choices_reference + ): + raise RuntimeError("changing costs changed the feasible schedule set") + choices_reference = choices + optimum = min(row["objective"] for row in alternatives) + optimal = [row for row in alternatives if row["objective"] == optimum] + samples = [] + for report in reports: + if ( + report["exact"]["best"]["objective"] != optimum + or report["milp"]["status"] != "optimal" + or report["milp"]["best"]["objective"] != optimum + or report["exact"]["feasible_count"] != len(alternatives) + ): + raise RuntimeError("independent certificates disagree") + for component in report["quantum"]["components"]: + key = "".join(map(str, component["best"]["bits"][::-1])) + if component["counts"].get(key, 0) <= 0: + raise RuntimeError("chosen quantum result was not sampled") + samples.append(sample_metrics(report, alternatives)) + records.append( + {"weight": weight, "seed": report["quantum"]["seed"], "report": report} + ) + rows.append( + { + "weight": weight, + "optimum": optimum, + "optimal_choices": [row["choices"] for row in optimal], + "optimal_changed_tasks": sorted( + {row["changed_tasks"] for row in optimal} + ), + "alternatives": alternatives, + "samples": samples, + } + ) + input_hashes.append( + { + "weight": weight, + "file": f"inputs/{path.name}", + "sha256": reports[0]["input_sha256"], + } + ) + package = Path(report_module.__file__).parent + files = {f"LabScheduling/{p.name}": p for p in sorted(package.glob("*.py"))} + files.update( + { + f"example/{name}": HERE / name + for name in ( + "sensitivity.py", + "tradeoff_demo.py", + "stress_benchmark.py", + "summarize_matched.py", + ) + } + ) + files["protocol"] = ( + HERE.parents[2] / "Tutorials/LabScheduling/SENSITIVITY_PROTOCOL.md" + ) + files["configuration"] = config_path + result = { + "config": config, + "input_sha256": input_hashes, + "source_sha256": { + name: hashlib.sha256(p.read_bytes()).hexdigest() + for name, p in files.items() + }, + "environment": records[0]["report"]["environment"], + "weights": rows, + "runs": records, + } + if verify_against: + verify_replay(verify_against, result) + write_json( + output / "replay.json", + { + "identical_except_timing": True, + "weights": len(rows), + "runs": len(records), + }, + ) + # Compact full JSON avoids duplicating thousands of formatted optimizer lines. + (output / "results.json").write_text( + json.dumps(result, ensure_ascii=False, allow_nan=False, separators=(",", ":")) + + "\n", + encoding="utf-8", + ) + summaries = [ + { + "weight": row["weight"], + "certified_optimum": row["optimum"], + "optimal_choices": json.dumps(row["optimal_choices"]), + "optimal_changed_tasks": json.dumps(row["optimal_changed_tasks"]), + "feasible_runs": sum(s["absolute_gap"] is not None for s in row["samples"]), + "optimal_runs": sum(s["optimal"] for s in row["samples"]), + "sampled_objectives": json.dumps([s["objective"] for s in row["samples"]]), + "sampled_gaps": json.dumps([s["absolute_gap"] for s in row["samples"]]), + } + for row in rows + ] + with (output / "summary.csv").open("w", encoding="utf-8", newline="") as stream: + writer = csv.DictWriter( + stream, fieldnames=list(summaries[0]), lineterminator="\n" + ) + writer.writeheader() + writer.writerows(summaries) + _plot(rows, output) + print( + json.dumps( + { + "sensitivity_runs": len(records), + "decisions": summaries, + "output": str(output), + }, + ensure_ascii=False, + indent=2, + ) + ) + return result diff --git a/pyqpanda-algorithm/example/LabScheduling/stress_benchmark.py b/pyqpanda-algorithm/example/LabScheduling/stress_benchmark.py new file mode 100644 index 00000000..92b7a6c9 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/stress_benchmark.py @@ -0,0 +1,208 @@ +"""Run the frozen corpus without tuning/filtering; save every optimizer outcome.""" + +import argparse +import csv +import hashlib +import json +import platform +import resource +from importlib.metadata import version +from pathlib import Path +from time import perf_counter +from typing import Any + +import numpy as np +from pyqpanda_alg.LabScheduling import ( + circuit_probabilities, + compile_qubo, + evaluate, + load_problem, + reduce_model, + solve_exact, + solve_milp, + solve_qaoa, +) +from pyqpanda_alg.LabScheduling.classical import solve_greedy +from pyqpanda_alg.LabScheduling.metrics import circuit_resources, distribution_metrics + + +def write_json(path: Path, value: Any) -> None: + """Persist finite JSON with a final newline.""" + path.write_text( + json.dumps(value, ensure_ascii=False, allow_nan=False, indent=2) + "\n", + encoding="utf-8", + ) + + +def run(corpus: Path, output: Path, split: str) -> None: + """Train 10 seeds per registered instance and audit fixed-parameter equivalence.""" + output.mkdir(parents=True, exist_ok=True) + manifest = json.loads((corpus / "manifest.json").read_text()) + source = Path(__file__).with_name("make_corpus.py") + if hashlib.sha256(source.read_bytes()).hexdigest() != manifest["generator_sha256"]: + raise ValueError("generator changed after corpus freeze") + for row in manifest["instances"]: + if ( + hashlib.sha256((corpus / row["file"]).read_bytes()).hexdigest() + != row["sha256"] + ): + raise ValueError(f"input hash mismatch: {row['file']}") + started = perf_counter() + script_hash = hashlib.sha256(Path(__file__).read_bytes()).hexdigest() + write_json( + output / "provenance.json", + { + "manifest": manifest, + "split": split, + "benchmark_sha256": script_hash, + "python": platform.python_version(), + "platform": platform.platform(), + "packages": { + n: version(n) for n in ("numpy", "scipy", "pyqpanda3", "pyqpanda_alg") + }, + "source_hashes": { + p.name: hashlib.sha256(p.read_bytes()).hexdigest() + for p in Path( + __import__( + "pyqpanda_alg.LabScheduling", fromlist=["__file__"] + ).__file__ + ).parent.glob("*.py") + }, + }, + ) + rows = [] + for spec in manifest["instances"]: + if spec["split"] != split: + continue + problem = load_problem(corpus / spec["file"]) + model = compile_qubo(problem) + exact, oracle = solve_exact(model), solve_milp(problem) + if exact["status"] != oracle["status"] or ( + exact["best"] and exact["best"]["objective"] != oracle["best"]["objective"] + ): + raise RuntimeError("independent oracle disagreement") + greedy = solve_greedy(model) + for seed in manifest["optimization_seeds"]: + quantum = solve_qaoa(model, seed=seed, **manifest["training"]) + diagnostics_started = perf_counter() + probabilities = circuit_probabilities(model, quantum["parameters"]) + full = circuit_probabilities( + model, quantum["parameters"], phase_mode="full" + ) + error = float(np.max(np.abs(probabilities - full))) + if error > 1e-10: + raise RuntimeError("phase equivalence failed") + metrics = distribution_metrics(model, probabilities) + samples = [] + for shots in manifest["shots"]: + rng = np.random.default_rng( + np.random.SeedSequence([seed, shots, 20260928]) + ) + # Fresh samples; initialization and training RNG streams are not reused. + draws = rng.choice(len(probabilities), size=shots, p=probabilities) + candidates = [ + evaluate( + model, [(int(i) >> j) & 1 for j in range(len(model.variables))] + ) + for i in set(draws) + ] + feasible = [r["objective"] for r in candidates if r["feasible"]] + uniform = [] + for _ in range(shots): + bits = [0] * len(model.variables) + for domain in model.domains: + bits[int(rng.choice(domain))] = 1 + candidate = evaluate(model, bits) + if candidate["feasible"]: + uniform.append(candidate["objective"]) + samples.append( + { + "shots": shots, + "quantum_objective": min(feasible, default=None), + "uniform_objective": min(uniform, default=None), + } + ) + gates = circuit_resources(model, quantum["parameters"]) + record = { + "input": spec, + "model": model.audit(), + "quantum": quantum, + "metrics": metrics, + "raw_milp": oracle, + "exact": exact, + "greedy": greedy, + "resources": gates, + "phase_max_probability_error": error, + "samples": samples, + "reduction": reduce_model(model).audit(), + "diagnostic_seconds": perf_counter() - diagnostics_started, + } + name = f"{Path(spec['file']).stem}-seed{seed}" + write_json(output / f"{name}.json", record) + rows.append( + { + "instance": spec["file"], + "seed": seed, + "qubits": len(model.variables), + "conflicts": len(model.conflicts), + "exact_status": exact["status"], + "exact_objective": metrics["exact_objective"], + "quantum_objective": quantum["best"]["objective"], + "greedy_objective": greedy["best"]["objective"] + if greedy["best"] + else None, + "p_opt": metrics["optimal_probability"], + "p_feasible": metrics["feasible_probability"], + "uniform_p_opt": metrics["uniform_optimal_probability"], + "hit16": metrics["hit_curve"][1]["quantum"], + "uniform_hit16": metrics["hit_curve"][1]["uniform"], + "cnot_full": gates["full"]["cnot"], + "cnot_auto": gates["auto"]["cnot"], + "depth_full": gates["full"]["depth"], + "depth_auto": gates["auto"]["depth"], + "phase_max_error": error, + "evaluations": quantum["circuit_evaluations"], + "quantum_seconds": quantum["runtime_seconds"], + "milp_seconds": oracle["runtime_seconds"], + "exact_seconds": exact["runtime_seconds"], + } + ) + print( + name, + quantum["status"], + f"p_opt={metrics['optimal_probability']:.5f}", + flush=True, + ) + with (output / "summary.csv").open("w", newline="", encoding="utf-8") as stream: + writer = csv.DictWriter(stream, fieldnames=list(rows[0]), lineterminator="\n") + writer.writeheader() + writer.writerows(rows) + write_json( + output / "execution.json", + { + "records": len(rows), + "runtime_seconds": perf_counter() - started, + "peak_process_rss_kib_linux": resource.getrusage( + resource.RUSAGE_SELF + ).ru_maxrss, + "rss_scope": "whole process high-water mark on Linux; not per circuit", + }, + ) + + +def main() -> None: + """Run development separately; evaluation uses the same frozen training budget.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--corpus", type=Path, default=Path(__file__).parent / "data/corpus" + ) + parser.add_argument("--output", type=Path, default=Path("reports/lab-enhanced")) + parser.add_argument( + "--split", choices=("development", "evaluation"), default="evaluation" + ) + args = parser.parse_args() + run(args.corpus, args.output, args.split) + + +if __name__ == "__main__": + main() diff --git a/pyqpanda-algorithm/example/LabScheduling/summarize_benchmark.py b/pyqpanda-algorithm/example/LabScheduling/summarize_benchmark.py new file mode 100644 index 00000000..1b4e15e5 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/summarize_benchmark.py @@ -0,0 +1,212 @@ +"""Archive all frozen-corpus runs and render instance-weighted evidence plots.""" + +import argparse +import json +import shutil +from collections import defaultdict +from itertools import combinations +from pathlib import Path +from typing import Any + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np + + +def summarize(source: Path, output: Path) -> None: + """Preserve failures; aggregate optimization seeds within each instance first.""" + provenance = json.loads((source / "provenance.json").read_text()) + records = [] + groups: dict[str, list[dict[str, Any]]] = defaultdict(list) + for instance in provenance["manifest"]["instances"]: + if instance["split"] != provenance["split"]: + continue + for seed in provenance["manifest"]["optimization_seeds"]: + name = f"{Path(instance['file']).stem}-seed{seed}.json" + record = json.loads((source / name).read_text()) + records.append(record) + groups[instance["file"]].append(record) + output.mkdir(parents=True, exist_ok=True) + for name in ("summary.csv", "provenance.json", "execution.json"): + shutil.copyfile(source / name, output / name) + with (output / "runs.jsonl").open("w", encoding="utf-8") as stream: + for record in records: + stream.write( + json.dumps( + record, ensure_ascii=False, allow_nan=False, separators=(",", ":") + ) + + "\n" + ) + aggregate = [] + for name, group in groups.items(): + first = group[0] + p = [r["metrics"]["optimal_probability"] for r in group] + baseline = first["metrics"]["uniform_optimal_probability"] + aggregate.append( + { + "instance": name, + "status": first["exact"]["status"], + "qubits": first["model"]["qubits"], + "candidate_conflict_density": len(first["model"]["conflicts"]) + / sum( + len(a) * len(b) + for a, b in combinations(first["model"]["domains"], 2) + ), + "mean_p_opt": float(np.mean(p)), + "min_p_opt": min(p), + "max_p_opt": max(p), + "uniform_p_opt": baseline, + "seeds_better_than_uniform": sum( + value > baseline + 1e-12 for value in p + ), + "cnot_full": first["resources"]["full"]["cnot"], + "cnot_auto": first["resources"]["auto"]["cnot"], + "depth_full": first["resources"]["full"]["depth"], + "depth_auto": first["resources"]["auto"]["depth"], + "mean_training_seconds": float( + np.mean([r["quantum"]["runtime_seconds"] for r in group]) + ), + "milp_seconds": first["raw_milp"]["runtime_seconds"], + } + ) + (output / "aggregate.json").write_text(json.dumps(aggregate, indent=2) + "\n") + md = [ + "# 冻结语料逐实例汇总", + "", + "完整训练记录见 runs.jsonl，每行一次训练；包含全部不可行实例。", + "", + "| 实例 | 状态 | 平均/最差 p_opt | 均匀 p_opt | 胜出种子/10 | CNOT 原始→精简 |", + "|---|---|---|---|---|---|", + ] + for row in aggregate: + md.append( + f"| {row['instance']} | {row['status']} | " + f"{row['mean_p_opt']:.6f} / {row['min_p_opt']:.6f} | " + f"{row['uniform_p_opt']:.6f} | {row['seeds_better_than_uniform']} | " + f"{row['cnot_full']} → {row['cnot_auto']} |" + ) + (output / "SUMMARY.md").write_text("\n".join(md) + "\n", encoding="utf-8") + matplotlib.rcParams["svg.hashsalt"] = "lab-enhancement-v1" + matplotlib.rcParams["svg.fonttype"] = "none" + labels = [f"q{r['qubits']}/d{i % 3}" for i, r in enumerate(aggregate)] + x = np.arange(len(labels)) + fig, ax = plt.subplots(figsize=(10, 4), layout="constrained") + ax.bar( + x - 0.2, + [r["cnot_full"] for r in aggregate], + 0.4, + label="Full phase", + color="#b47750", + ) + ax.bar( + x + 0.2, + [r["cnot_auto"] for r in aggregate], + 0.4, + label="Simplified XY", + color="#167d9a", + ) + ax.set( + xticks=x, + xticklabels=labels, + ylabel="Native CNOT operations", + title="Same trained parameters; p=1; all 12 registered instances", + ) + ax.legend(frameon=False) + fig.savefig(output / "resources.svg", metadata={"Date": None}) + plt.close(fig) + feasible_groups = [ + g for g in groups.values() if g[0]["exact"]["status"] == "optimal" + ] + budgets = provenance["manifest"]["shots"] + quantum, uniform, observed_q, observed_u = [], [], [], [] + for i, _shots in enumerate(budgets): + quantum.append( + np.mean( + [ + np.mean([r["metrics"]["hit_curve"][i]["quantum"] for r in g]) + for g in feasible_groups + ] + ) + ) + uniform.append( + np.mean( + [g[0]["metrics"]["hit_curve"][i]["uniform"] for g in feasible_groups] + ) + ) + for target, key in ( + (observed_q, "quantum_objective"), + (observed_u, "uniform_objective"), + ): + target.append( + np.mean( + [ + np.mean( + [ + r["samples"][i][key] == r["metrics"]["exact_objective"] + for r in g + ] + ) + for g in feasible_groups + ] + ) + ) + fig, ax = plt.subplots(figsize=(8, 4), layout="constrained") + ax.plot( + budgets, quantum, "o-", label="XY: analytic after training", color="#167d9a" + ) + ax.plot(budgets, uniform, "o--", label="Uniform one-hot: analytic", color="#b47750") + ax.scatter( + budgets, + observed_q, + marker="x", + label="XY: independent sample runs", + color="#167d9a", + ) + ax.scatter( + budgets, observed_u, marker="+", label="Uniform: sample runs", color="#b47750" + ) + ax.set( + xscale="log", + xticks=budgets, + xticklabels=budgets, + ylim=(0, 1.03), + xlabel="Shots (training cost excluded)", + ylabel="At least one optimum", + title=( + f"Equal weight over {len(feasible_groups)} feasible instances; " + "10 seeds each" + ), + ) + ax.legend(frameon=False, fontsize=9) + ax.grid(alpha=0.15) + fig.savefig(output / "hit-curves.svg", metadata={"Date": None}) + plt.close(fig) + (output / "hit-curves.json").write_text( + json.dumps( + { + "shots": budgets, + "analytic_xy": quantum, + "analytic_uniform": uniform, + "observed_xy": observed_q, + "observed_uniform": observed_u, + "aggregation": "seed mean within instance, then equal instance mean", + }, + indent=2, + ) + + "\n" + ) + + +def main() -> None: + """Produce reviewable plots and a single lossless JSONL archive.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("source", type=Path) + parser.add_argument("--output", type=Path, default=Path("reports/lab-evidence")) + args = parser.parse_args() + summarize(args.source, args.output) + + +if __name__ == "__main__": + main() diff --git a/pyqpanda-algorithm/example/LabScheduling/summarize_matched.py b/pyqpanda-algorithm/example/LabScheduling/summarize_matched.py new file mode 100644 index 00000000..7fc41b66 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/summarize_matched.py @@ -0,0 +1,226 @@ +"""Summarize every matched-budget record, keeping classical-only cases separate.""" + +import argparse +import csv +import json +from collections import Counter +from pathlib import Path +from statistics import mean +from typing import Any + +from stress_benchmark import write_json + +PATHS = ("direct", "reduced", "uniform_direct", "uniform_reduced") + + +def without_timing(value: Any) -> Any: + """Exclude only timing fields; preserve parameters, counts and all failures.""" + if isinstance(value, dict): + return { + key: without_timing(item) + for key, item in value.items() + if not key.endswith("_seconds") + } + if isinstance(value, list): + return [without_timing(item) for item in value] + return value + + +def verify_replay(original: Path, replay: Path) -> None: + """Reject environment/source changes or any non-timing record discrepancy.""" + left_provenance = json.loads((original / "provenance.json").read_text()) + right_provenance = json.loads((replay / "provenance.json").read_text()) + if left_provenance != right_provenance: + raise ValueError("replay provenance changed") + left = [ + json.loads(line) for line in (original / "runs.jsonl").read_text().splitlines() + ] + right = [ + json.loads(line) for line in (replay / "runs.jsonl").read_text().splitlines() + ] + if without_timing(left) != without_timing(right): + raise ValueError("replay records differ beyond timing fields") + write_json( + original / "replay.json", + { + "records_compared": len(left), + "identical_except_timing": True, + "excluded_fields": "only keys ending in _seconds, recursively", + "replay_execution": json.loads((replay / "execution.json").read_text()), + }, + ) + + +def summarize(folder: Path) -> None: + """Write instance-first summaries and conditional total-shot hit curves.""" + records = [ + json.loads(line) for line in (folder / "runs.jsonl").read_text().splitlines() + ] + manifest = json.loads((folder / "provenance.json").read_text())["manifest"] + expected = { + (item["file"], seed) + for item in manifest["instances"] + for seed in manifest["optimization_seeds"] + } + if ( + len(records) != len(expected) + or {(r["instance"], r["seed"]) for r in records} != expected + ): + raise ValueError("missing, duplicate or unexpected instance/seed record") + rows = [] + quantum_groups = [] + for item in manifest["instances"]: + group = [r for r in records if r["instance"] == item["file"]] + first = group[0] + category = ( + "infeasible" + if first["exact_objective"] is None + else "classical_only" + if first["paths"]["reduced"]["status"] == "classical_propagation" + else "quantum_required" + ) + if category == "quantum_required": + quantum_groups.append(group) + row: dict[str, Any] = { + "instance": item["file"], + "category": category, + "optimum": first["exact_objective"], + } + for path in PATHS: + row[f"{path}_optimal_seeds"] = sum( + r["absolute_gaps"][path] == 0 for r in group + ) + row[f"{path}_feasible_seeds"] = sum( + bool(r["paths"][path]["best"] and r["paths"][path]["best"]["feasible"]) + for r in group + ) + for field in ("circuit_evaluations", "shots", "runtime_seconds"): + row[f"{path}_mean_{field}"] = mean( + r["paths"][path].get(field, 0) for r in group + ) + rows.append(row) + with (folder / "summary.csv").open("w", newline="", encoding="utf-8") as stream: + writer = csv.DictWriter(stream, fieldnames=list(rows[0]), lineterminator="\n") + writer.writeheader() + writer.writerows(rows) + curves = [] + for index, shots in enumerate((8, 16, 32, 64, 128, 512)): + row = {"total_shots": shots} + for path in PATHS: + row[path] = mean( + mean(r["conditional_hit_curve"][index][path] for r in group) + for group in quantum_groups + ) + deltas = [ + mean( + r["conditional_hit_curve"][index]["reduced"] + - r["conditional_hit_curve"][index]["uniform_reduced"] + for r in group + ) + for group in quantum_groups + ] + row["reduced_vs_uniform_instance_delta_min"] = min(deltas) + row["reduced_vs_uniform_instance_delta_max"] = max(deltas) + row["instances_reduced_better_than_uniform"] = sum(d > 1e-12 for d in deltas) + row["instances_reduced_worse_than_uniform"] = sum(d < -1e-12 for d in deltas) + curves.append(row) + aggregate = { + "paired_records": len(records), + "instance_categories": dict(Counter(row["category"] for row in rows)), + "statuses": { + path: dict(Counter(r["paths"][path]["status"] for r in records)) + for path in PATHS + }, + "quantum_required_optimal_seeds": { + path: sum( + r["absolute_gaps"][path] == 0 for group in quantum_groups for r in group + ) + for path in PATHS + }, + "all_feasible_optimal_seeds": { + path: sum( + r["absolute_gaps"][path] == 0 + for r in records + if r["exact_objective"] is not None + ) + for path in PATHS + }, + "quantum_required_mean_evaluations": { + path: mean( + row[f"{path}_mean_circuit_evaluations"] + for row in rows + if row["category"] == "quantum_required" + ) + for path in PATHS + }, + "conditional_hit_curves_quantum_required": curves, + "interpretation": ( + "Equal caps, not equal actual work. Instance-first means; curves " + "condition on trained states. Known synthetic corpus, not a new holdout." + ), + } + write_json(folder / "aggregate.json", aggregate) + lines = [ + "# 同总预算对照结果", + "", + "全部 36 个实例、每实例 10 种子；Q=直接量子，R=分量量子，" + "U=直接均匀随机，S=分量均匀随机。", + "", + "下表为实际 512 总 shots 下采到精确最优的种子数；不可行行无最优值，记 0。", + "", + "| 实例 | 类别 | 最优值 | Q | R | U | S |", + "|---|---|---:|---:|---:|---:|---:|", + ] + for row in rows: + cells = [ + row["instance"], + row["category"], + row["optimum"], + *[row[f"{p}_optimal_seeds"] for p in PATHS], + ] + lines.append( + "| " + " | ".join(str(v) if v is not None else "—" for v in cells) + " |" + ) + lines += [ + "", + "## 同总 shots 的条件命中概率", + "", + "仅 22 个仍需量子的可行实例，先平均每实例的十种子，再等权平均实例。", + "这是训练后理论曲线，低 shots 未重新训练或实际抽样，不包括训练成本。", + "", + "| 总 shots | Q | R | U | S | R>S 实例 | R None: + """Summarize a complete frozen 360-record evaluation directory.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("folder", type=Path) + parser.add_argument("--replay", type=Path, help="also verify an independent rerun") + args = parser.parse_args() + summarize(args.folder) + if args.replay: + verify_replay(args.folder, args.replay) + + +if __name__ == "__main__": + main() diff --git a/pyqpanda-algorithm/example/LabScheduling/summarize_v2.py b/pyqpanda-algorithm/example/LabScheduling/summarize_v2.py new file mode 100644 index 00000000..b69028d9 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/summarize_v2.py @@ -0,0 +1,247 @@ +"""Summarize every heterogeneous instance without discarding failed runs.""" + +import argparse +import csv +import json +from collections import Counter, defaultdict +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +from stress_benchmark import write_json + + +def summarize(source: Path) -> None: + """Add compact instance/cell tables and stage-resource plots to a run archive.""" + instances = [ + json.loads(line) + for line in (source / "instances.jsonl").read_text().splitlines() + ] + runs = [ + json.loads(line) for line in (source / "runs.jsonl").read_text().splitlines() + ] + groups = defaultdict(list) + for run in runs: + groups[run["instance"]].append(run) + rows = [] + for instance in instances: + spec = instance["input"] + group = groups[spec["file"]] + stages = instance["ablation"]["stages"] + opt = instance["ablation"]["exact_objective"] + profiles = [ + r["direct_metrics"] for r in group if r["direct_metrics"] is not None + ] + final = stages.get("components_auto_phase") + rows.append( + { + "instance": spec["file"], + "tasks": spec["tasks"], + "scenario": spec["scenario"], + "replicate": spec["replicate"], + "feasible": opt is not None, + "optimum": opt, + "raw_candidates": stages["raw_input"]["candidate_count"], + "calendar_qubits": stages["calendar_full_phase"]["total_qubits"], + "propagation_qubits": stages.get("propagation_full_phase", {}).get( + "total_qubits" + ), + "max_component_qubits": final["max_component_qubits"] + if final + else None, + "cnot_calendar_full": stages["calendar_full_phase"]["cnot"], + "cnot_components_full": stages.get("components_full_phase", {}).get( + "cnot" + ), + "cnot_components_auto": final["cnot"] if final else None, + "mean_p_opt": float( + np.mean([p["optimal_probability"] for p in profiles]) + ) + if profiles + else 0, + "min_p_opt": min( + (p["optimal_probability"] for p in profiles), default=0 + ), + "uniform_p_opt": profiles[0]["uniform_optimal_probability"] + if profiles + else 0, + "direct_optimal_samples": sum( + r["direct"]["best"] is not None + and opt is not None + and r["direct"]["best"]["objective"] == opt + for r in group + ), + "reduced_optimal_samples": sum( + r["reduced"]["best"] is not None + and opt is not None + and r["reduced"]["best"]["objective"] == opt + for r in group + ), + "direct_mean_evaluations": float( + np.mean([r["direct"]["circuit_evaluations"] for r in group]) + ), + "reduced_mean_evaluations": float( + np.mean([r["reduced"]["circuit_evaluations"] for r in group]) + ), + "direct_mean_seconds": float( + np.mean([r["direct"]["runtime_seconds"] for r in group]) + ), + "reduced_mean_seconds": float( + np.mean([r["reduced"]["runtime_seconds"] for r in group]) + ), + "direct_shots": group[0]["direct"].get("shots", 0), + "reduced_total_shots": group[0]["reduced"].get("total_shots", 0), + "milp_seconds": instance["milp"]["runtime_seconds"], + "feasible_assignments": instance["ablation"]["feasible_count"], + } + ) + with (source / "summary.csv").open("w", newline="", encoding="utf-8") as stream: + writer = csv.DictWriter(stream, fieldnames=list(rows[0]), lineterminator="\n") + writer.writeheader() + writer.writerows(rows) + cells = [] + for tasks in (3, 4, 5): + for scenario in range(3): + cell = [ + r for r in rows if r["tasks"] == tasks and r["scenario"] == scenario + ] + feasible = [r for r in cell if r["feasible"]] + cells.append( + { + "tasks": tasks, + "scenario": scenario, + "instances": len(cell), + "feasible": len(feasible), + "instance_mean_p_opt": float( + np.mean([r["mean_p_opt"] for r in feasible]) + ) + if feasible + else None, + "instance_mean_uniform_p_opt": float( + np.mean([r["uniform_p_opt"] for r in feasible]) + ) + if feasible + else None, + "instance_mean_delta_range": [ + min(r["mean_p_opt"] - r["uniform_p_opt"] for r in feasible), + max(r["mean_p_opt"] - r["uniform_p_opt"] for r in feasible), + ] + if feasible + else None, + } + ) + feasible_profiles = [ + r["direct_metrics"] + for r in runs + if r["direct_metrics"] and r["direct_metrics"]["exact_objective"] is not None + ] + aggregate = { + "instances": len(instances), + "feasible_instances": sum(r["feasible"] for r in rows), + "paired_seed_records": len(runs), + "direct_statuses": dict(Counter(r["direct"]["status"] for r in runs)), + "reduced_statuses": dict(Counter(r["reduced"]["status"] for r in runs)), + "direct_quantum_executions": sum( + r["direct"]["circuit_evaluations"] > 0 for r in runs + ), + "reduced_quantum_executions": sum( + r["reduced"]["circuit_evaluations"] > 0 for r in runs + ), + "reduced_component_trainings": sum( + len(r["reduced"]["components"]) for r in runs + ), + "feasible_training_seeds": len(feasible_profiles), + "seeds_better_than_uniform": sum( + p["optimal_probability"] > p["uniform_optimal_probability"] + 1e-12 + for p in feasible_profiles + ), + "seeds_worse_than_uniform": sum( + p["optimal_probability"] < p["uniform_optimal_probability"] - 1e-12 + for p in feasible_profiles + ), + "phase_max_error": max( + ( + r["direct_metrics"]["phase_max_error"] + for r in runs + if r["direct_metrics"] + ), + default=0, + ), + "cells": cells, + "scope": ( + "synthetic instances; seed means within instance; " + "unequal component budgets; no speedup claim" + ), + } + write_json(source / "aggregate.json", aggregate) + text = [ + "# 第二批逐实例结果", + "", + "36 个实例 × 10 个种子，两条执行路径；不可行和纯经典传播均保留。", + "", + "原始模型只有候选计数，不虚构含无效候选的线路。分解前后预算不同，不能用命中数宣称公平胜率优势。", + "", + "| 实例 | 最优值 | 原始→日历→传播→最大组件候选 | " + "CNOT 日历完整→组件完整→精简 | 直接/化简最优命中（各10） |", + "|---|---|---|---|---|", + ] + for r in rows: + text.append( + f"| {r['instance']} | {r['optimum']} | " + f"{r['raw_candidates']}→{r['calendar_qubits']}→" + f"{r['propagation_qubits']}→{r['max_component_qubits']} | " + f"{r['cnot_calendar_full']}→{r['cnot_components_full']}→" + f"{r['cnot_components_auto']} | " + f"{r['direct_optimal_samples']}/{r['reduced_optimal_samples']} |" + ) + (source / "SUMMARY.md").write_text("\n".join(text) + "\n", encoding="utf-8") + matplotlib.rcParams["svg.hashsalt"] = "lab-v2" + matplotlib.rcParams["svg.fonttype"] = "none" + fig, axes = plt.subplots(1, 2, figsize=(11, 4), layout="constrained") + x = np.arange(len(rows)) + for key, label in ( + ("raw_candidates", "Raw candidates"), + ("calendar_qubits", "After calendar"), + ("max_component_qubits", "Largest remaining component"), + ): + axes[0].plot( + x, + [r[key] if r[key] is not None else np.nan for r in rows], + ".-", + label=label, + ) + axes[0].set( + xlabel="Registered instance index (0–35)", + ylabel="Candidate / qubit count", + title="Missing final points = propagation proved infeasible", + ) + axes[0].legend(frameon=False, fontsize=8) + feasible_rows = [r for r in rows if r["feasible"]] + axes[1].scatter( + [r["uniform_p_opt"] for r in feasible_rows], + [r["mean_p_opt"] for r in feasible_rows], + color="#167d9a", + ) + axes[1].plot([0, 1], [0, 1], "--", color="gray") + axes[1].set( + xlabel="Uniform optimum probability", + ylabel="XY optimum probability (mean of 10 seeds)", + title="Each dot = one feasible instance; training excluded", + ) + fig.savefig(source / "overview.svg", metadata={"Date": None}) + plt.close(fig) + print(json.dumps({k: v for k, v in aggregate.items() if k != "cells"}, indent=2)) + + +def main() -> None: + """Summarize a completed archive in place.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("source", type=Path) + summarize(parser.parse_args().source) + + +if __name__ == "__main__": + main() diff --git a/pyqpanda-algorithm/example/LabScheduling/support_pruning.py b/pyqpanda-algorithm/example/LabScheduling/support_pruning.py new file mode 100644 index 00000000..a8e90f46 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/support_pruning.py @@ -0,0 +1,214 @@ +"""Verify lossless arc pruning and reproduce a constructed resource boundary.""" + +import argparse +import hashlib +import json +from dataclasses import replace +from itertools import product +from math import prod +from pathlib import Path +from typing import Any + +from ablation import feasible_vectors +from pyqpanda_alg.LabScheduling import ( + Model, + Problem, + Reduction, + compile_qubo, + load_problem, + reduce_model, + validate_assignment, +) +from pyqpanda_alg.LabScheduling.report import run_experiment +from stress_benchmark import write_json +from summarize_matched import without_timing + +HERE = Path(__file__).resolve().parent + + +def verify_removals(model: Model, reduction: Reduction) -> None: + """Replay each deletion witness, including its complete then-current domain.""" + active = [set(d) for d in model.domains] + conflicts = {(i, j): reasons for i, j, reasons in model.conflicts} + tasks = {t.id: i for i, t in enumerate(model.problem.tasks)} + for row in reduction.removals: + i = row["variable"] + if i not in active[model.variables[i].task]: + raise ValueError("deletion witness refers to an inactive candidate") + if row.get("rule") == "no_support": + opposing = active[tasks[row["against_task"]]] + blockers = row["blockers"] + if not opposing or [b["variable"] for b in blockers] != sorted(opposing): + raise ValueError("deletion witness must contain the entire live domain") + else: + forced = row["forced_by"] + if active[model.variables[forced].task] != {forced}: + raise ValueError("forced deletion witness is not a singleton") + blockers = [{"variable": forced, "reasons": row["reasons"]}] + for blocker in blockers: + pair = tuple(sorted((i, blocker["variable"]))) + if tuple(blocker["reasons"]) != conflicts.get(pair): + raise ValueError("deletion witness lacks a matching forbidden pair") + active[model.variables[i].task].remove(i) + if reduction.infeasible_task is not None: + if active[tasks[reduction.infeasible_task]]: + raise ValueError("infeasibility witness has no empty domain") + else: + surviving = set().union(*active) if active else set() + mapped = set(reduction.fixed).union(*map(set, reduction.mappings)) + if surviving != mapped: + raise ValueError("restoration map differs from the surviving domains") + + +def audit_problem(problem: Problem) -> dict[str, Any]: + """Compare both reductions to the full raw-input feasible set, with costs.""" + if prod(len(t.options) for t in problem.tasks) > 1_000_000: + raise ValueError("support audit is limited to one million raw combinations") + model = compile_qubo(problem) + raw = {} + for choices in product(*(range(len(t.options)) for t in problem.tasks)): + result = validate_assignment(problem, choices) + if result["feasible"]: + raw[choices] = result["objective"] + modes = {} + for mode in ("singleton", "arc"): + reduction = reduce_model(model, pruning=mode) + verify_removals(model, reduction) + restored = {} + if reduction.infeasible_task is None: + local = [feasible_vectors(m) for m in reduction.components] + for assignment in product(*(list(vectors) for vectors in local)): + result = reduction.restore(assignment) + expected = reduction.audit()["fixed_cost"] + sum( + vectors[bits] + for vectors, bits in zip(local, assignment, strict=True) + ) + if not result["feasible"] or result["objective"] != expected: + raise RuntimeError("restoration changed feasibility or cost") + choices = tuple( + task.options.index( + model.variables[ + next(i for i in domain if result["bits"][i]) + ].option + ) + for task, domain in zip(problem.tasks, model.domains, strict=True) + ) + if choices in restored: + raise RuntimeError("restoration is not injective") + restored[choices] = result["objective"] + if restored != raw: + raise RuntimeError("reduction changed the complete raw feasible set") + modes[mode] = reduction.audit() + return { + "problem": problem.name, + "feasible_count": len(raw), + "exact_objective": min(raw.values(), default=None), + "proof": "all raw feasible schedules and costs equal all restored schedules", + "modes": modes, + } + + +def run_evaluation() -> dict[str, Any]: + """Audit all 36 existing V2 inputs and run three real CPU boundary-case seeds.""" + sources = sorted((HERE / "data/corpus-v2").glob("n*.json")) + if len(sources) != 36: + raise ValueError("expected the complete 36-instance V2 corpus") + boundary = HERE / "data/support-chain.json" + problem = load_problem(boundary) + original = [ + next(i for i, o in enumerate(t.options) if (o.resource, o.start) == t.original) + for t in problem.tasks + ] + control = replace( + problem, resources=tuple(replace(r, downtime=()) for r in problem.resources) + ) + rows = [] + for path in [*sources, boundary]: + row = audit_problem(load_problem(path)) + row["input"] = path.relative_to(HERE).as_posix() + row["input_sha256"] = hashlib.sha256(path.read_bytes()).hexdigest() + rows.append(row) + # Solver results are kept separate from enumeration, never repaired with it. + default = run_experiment(boundary, reduce=True) + runs = [ + run_experiment(boundary, reduce=True, pruning="arc", seed=seed) + for seed in (7, 19, 42) + ] + for report in runs: + for component in report["quantum"]["components"]: + key = "".join(map(str, component["best"]["bits"][::-1])) + if component["counts"].get(key, 0) <= 0: + raise RuntimeError("selected component result was not sampled") + package = HERE.parents[1] / "pyqpanda_alg/LabScheduling" + root = HERE.parents[2] + files = [ + *sorted(package.glob("*.py")), + Path(__file__), + HERE / "ablation.py", + root / "Tutorials/LabScheduling/ARC_PROTOCOL.md", + ] + return { + "protocol": "ARC_PROTOCOL.md; known V2 corpus and constructed boundary case", + "source_sha256": { + p.relative_to(root).as_posix(): hashlib.sha256(p.read_bytes()).hexdigest() + for p in files + }, + "instances": rows, + "boundary_original": { + "before_outage": validate_assignment(control, original), + "after_outage": validate_assignment(problem, original), + }, + "boundary_default": default, + "boundary_arc": runs, + } + + +def main() -> None: + """Write complete evidence, optionally requiring an exact non-timing replay.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output", type=Path, default=Path("reports/support-pruning")) + parser.add_argument("--verify-against", type=Path) + args = parser.parse_args() + # Compare the JSON representation on both sides, including tuple domains. + result = json.loads(json.dumps(run_evaluation(), allow_nan=False)) + args.output.mkdir(parents=True, exist_ok=True) + if args.verify_against: + original = json.loads(args.verify_against.read_text(encoding="utf-8")) + if without_timing(original) != without_timing(result): + raise ValueError("support-pruning replay differs beyond timing") + write_json(args.output / "replay.json", {"identical_except_timing": True}) + write_json(args.output / "results.json", result) + corpus = result["instances"][:-1] + print( + json.dumps( + { + "audited_instances": len(result["instances"]), + "v2_strictly_smaller_max_component": sum( + r["modes"]["arc"]["max_component_qubits"] + < r["modes"]["singleton"]["max_component_qubits"] + for r in corpus + ), + "boundary_component_qubits": { + mode: result["instances"][-1]["modes"][mode]["component_qubits"] + for mode in ("singleton", "arc") + }, + "boundary_default_status": result["boundary_default"]["status"], + "boundary_arc": [ + { + "seed": r["quantum"]["seed"], + "status": r["status"], + "objective": r["quantum"]["best"]["objective"] + if r["quantum"]["best"] + else None, + "gap": r["absolute_gap"], + } + for r in result["boundary_arc"] + ], + }, + indent=2, + ) + ) + + +if __name__ == "__main__": + main() diff --git a/pyqpanda-algorithm/example/LabScheduling/tradeoff_demo.py b/pyqpanda-algorithm/example/LabScheduling/tradeoff_demo.py new file mode 100644 index 00000000..f17c5e44 --- /dev/null +++ b/pyqpanda-algorithm/example/LabScheduling/tradeoff_demo.py @@ -0,0 +1,148 @@ +"""Show the cost of shifting three bookings versus renting a backup instrument.""" + +import argparse +import copy +import csv +import json +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from ablation import audit_stages, feasible_vectors +from pyqpanda_alg.LabScheduling import compile_qubo, evaluate, load_problem +from pyqpanda_alg.LabScheduling.report import run_experiment +from stress_benchmark import write_json + + +def run_demo(output: Path) -> None: + """Run actual QAOA, then show every feasible alternative and an outage control.""" + source = Path(__file__).parent / "data/tradeoff.json" + problem = load_problem(source) + output.mkdir(parents=True, exist_ok=True) + report = run_experiment(source, reduce=True) + write_json(output / "outage-report.json", report) + raw = json.loads(source.read_text(encoding="utf-8")) + control = copy.deepcopy(raw) + for resource in control["resources"]: + resource["downtime"] = [] + write_json(output / "outage-input.json", raw) + write_json(output / "control-input.json", control) + write_json( + output / "control-report.json", + run_experiment(output / "control-input.json", reduce=True), + ) + write_json(output / "ablation.json", audit_stages(problem)) + model = compile_qubo(problem) + best = report["quantum"]["best"] + alternatives = [] + for bits, cost in feasible_vectors(model).items(): + candidate = evaluate(model, bits) + alternatives.append( + { + "objective": cost, + "changed_tasks": sum(r["changed"] for r in candidate["schedule"]), + "schedule": "; ".join( + f"{r['task']}:{r['resource']}@{r['start']}" + for r in candidate["schedule"] + ), + "sampled_choice": bool(best and list(bits) == best["bits"]), + } + ) + alternatives.sort(key=lambda r: (r["objective"], r["schedule"])) + with (output / "alternatives.csv").open( + "w", newline="", encoding="utf-8" + ) as stream: + writer = csv.DictWriter( + stream, fieldnames=list(alternatives[0]), lineterminator="\n" + ) + writer.writeheader() + writer.writerows(alternatives) + if best and best["feasible"]: + matplotlib.rcParams["svg.hashsalt"] = "lab-tradeoff" + matplotlib.rcParams["svg.fonttype"] = "none" + fig, axes = plt.subplots( + 2, 1, figsize=(9, 4.6), layout="constrained", sharex=True + ) + for ax, after in zip(axes, (False, True), strict=True): + for ti, task in enumerate(problem.tasks): + row = next(r for r in best["schedule"] if r["task"] == task.id) + rid, start = (row["resource"], row["start"]) if after else task.original + y = {"oven": 0, "backup": 1}[rid] + ax.barh( + y, + task.duration, + left=start, + height=0.5, + color=plt.get_cmap("tab10")(ti), + ) + ax.barh( + y, + 1, + left=start + task.duration, + height=0.5, + color="#dddddd", + hatch="//", + ) + ax.text(start + task.duration / 2, y, task.id, ha="center", va="center") + ax.axvspan(0, 1, ymin=0.08, ymax=0.46, color="red", alpha=0.15) + ax.set( + yticks=[0, 1], + yticklabels=["Oven", "Backup"], + ylim=(-0.7, 1.5), + xlim=(0, 14), + title="Actual sampled reschedule" + if after + else "Original reservations; outage on oven", + ) + ax.grid(axis="x", alpha=0.2) + axes[-1].set_xlabel("Time slot; gray hatch = cleanup; synthetic data") + fig.savefig(output / "tradeoff.svg", metadata={"Date": None}) + plt.close(fig) + print( + json.dumps( + { + "status": report["status"], + "sampled_objective": best["objective"] if best else None, + "certified_optimum": report["milp"]["best"]["objective"], + "gap": report["absolute_gap"], + "component_qubits": report["quantum"]["reduction"]["component_qubits"], + "fixed_variables": report["quantum"]["reduction"]["fixed_variables"], + "feasible_alternatives": len(alternatives), + "output": str(output), + }, + ensure_ascii=False, + indent=2, + ) + ) + + +def main() -> None: + """Write the complete offline demonstration and all alternative schedules.""" + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output", type=Path, default=Path("reports/tradeoff-demo")) + parser.add_argument( + "--sensitivity", + action="store_true", + help="also run 21 frozen change-cost experiments", + ) + parser.add_argument( + "--verify-sensitivity", + type=Path, + help="previous sensitivity/results.json to replay", + ) + args = parser.parse_args() + if args.verify_sensitivity and not args.sensitivity: + parser.error("--verify-sensitivity requires --sensitivity") + run_demo(args.output) + if args.sensitivity: + from sensitivity import run_sensitivity + + run_sensitivity( + args.output / "sensitivity", verify_against=args.verify_sensitivity + ) + + +if __name__ == "__main__": + main() diff --git a/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/__init__.py b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/__init__.py new file mode 100644 index 00000000..ca7ffbed --- /dev/null +++ b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/__init__.py @@ -0,0 +1,27 @@ +"""QUBO-based, auditable shared-laboratory rescheduling application.""" + +from .classical import solve_exact +from .milp import solve_milp, validate_assignment +from .model import Model, compile_qubo, evaluate +from .problem import Problem, load_problem, parse_problem +from .quantum import build_circuit, circuit_probabilities, solve_qaoa, to_ising +from .reduction import Reduction, reduce_model, solve_reduced + +__all__ = [ + "Model", + "Reduction", + "reduce_model", + "solve_reduced", + "solve_milp", + "validate_assignment", + "Problem", + "build_circuit", + "circuit_probabilities", + "compile_qubo", + "evaluate", + "load_problem", + "parse_problem", + "solve_exact", + "solve_qaoa", + "to_ising", +] diff --git a/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/__main__.py b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/__main__.py new file mode 100644 index 00000000..eb5afff9 --- /dev/null +++ b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/__main__.py @@ -0,0 +1,97 @@ +"""Run with python -m pyqpanda_alg.LabScheduling INPUT.json.""" + +import argparse +import json +from pathlib import Path +from tempfile import NamedTemporaryFile + +from .report import run_experiment + + +def _report_destination(source: Path, output: Path) -> Path: + """Resolve links and reject input aliases before starting or saving a run.""" + destination = output.resolve() + if destination == source.resolve() or ( + destination.exists() and source.exists() and destination.samefile(source) + ): + raise ValueError("output must not overwrite the input file (including links)") + if destination.exists() and not destination.is_file(): + raise ValueError("output must be a regular report file") + return destination + + +def _save_report(source: Path, output: Path, text: str) -> None: + """Replace a report only after a complete same-directory temporary write.""" + destination = _report_destination(source, output) + destination.parent.mkdir(parents=True, exist_ok=True) + temporary: Path | None = None + try: + with NamedTemporaryFile( + mode="w", + encoding="utf-8", + dir=destination.parent, + prefix=f".{destination.name}.", + suffix=".tmp", + delete=False, + ) as stream: + temporary = Path(stream.name) + stream.write(text + "\n") + # Recheck links in case an output alias changed while writing. + _report_destination(source, destination) + temporary.replace(destination) + finally: + if temporary is not None: + temporary.unlink(missing_ok=True) + + +def main(argv: list[str] | None = None) -> int: + """Print JSON; exit 2 for I/O/input errors, 3/4/5 for infeasible/miss/limit.""" + parser = argparse.ArgumentParser(description="共享实验室 QUBO 最小扰动重排（CPU）") + parser.add_argument("input", type=Path) + parser.add_argument("--seed", type=int, default=7) + parser.add_argument("--layers", type=int, default=1) + parser.add_argument("--shots", type=int, default=512) + parser.add_argument("--maxiter", type=int, default=60) + parser.add_argument("--restarts", type=int, default=2) + parser.add_argument("--mixer", choices=("xy", "x"), default="xy") + parser.add_argument( + "--reduce", action="store_true", help="exact propagation and component solving" + ) + parser.add_argument("--output", type=Path, help="also save the JSON report here") + parser.add_argument( + "--pruning", + choices=("singleton", "arc"), + default="singleton", + help="propagation strength; arc requires --reduce", + ) + args = parser.parse_args(argv) + try: + if args.output: + _report_destination(args.input, args.output) + report = run_experiment( + args.input, + args.seed, + args.layers, + args.shots, + args.maxiter, + args.restarts, + args.mixer, + reduce=args.reduce, + pruning=args.pruning, + ) + text = json.dumps(report, ensure_ascii=False, indent=2, allow_nan=False) + if args.output: + _save_report(args.input, args.output, text) + except (ValueError, OSError) as error: + parser.error(str(error)) + print(text) + return { + "feasible": 0, + "infeasible": 3, + "no_feasible_sample": 4, + "resource_limit": 5, + }[report["status"]] + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/bookings.py b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/bookings.py new file mode 100644 index 00000000..bc9b038c --- /dev/null +++ b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/bookings.py @@ -0,0 +1,98 @@ +"""Transparent CSV booking windows to explicit scheduling JSON candidates.""" + +from __future__ import annotations + +import csv +from pathlib import Path +from typing import Any + +from .problem import Problem + +COLUMNS = ( + "id", + "group", + "duration", + "original_resource", + "original_start", + "allowed_resources", + "earliest", + "latest", + "step", + "change_cost", + "shift_cost", +) + + +def import_bookings(path: str | Path, calendar: dict[str, Any]) -> dict[str, Any]: + """Expand every allowed resource/start in inclusive CSV booking windows. + + calendar uses the regular problem schema without tasks. Resources are pipe + separated; cost is shift_cost*abs(start-original_start). No candidate is + silently dropped: horizon/downtime pruning belongs to the QUBO audit. + At most 256 raw candidates are expanded; costs and references use the same + strict Problem validation as manually authored JSON. + """ + if not isinstance(calendar, dict) or set(calendar) - { + "name", + "horizon", + "resources", + "precedence", + }: + raise ValueError("calendar must contain problem fields without tasks") + tasks = [] + total = 0 + with Path(path).open(newline="", encoding="utf-8-sig") as stream: + reader = csv.DictReader(stream) + if reader.fieldnames != list(COLUMNS): + raise ValueError(f"CSV headers must be: {','.join(COLUMNS)}") + for row in reader: + if set(row) != set(COLUMNS) or any(v is None for v in row.values()): + raise ValueError("CSV row has missing or extra cells") + numbers = {} + for key in ( + "duration", + "original_start", + "earliest", + "latest", + "step", + "change_cost", + "shift_cost", + ): + value = row[key] + if not value.isascii() or not value.isdecimal(): + raise ValueError(f"CSV {key} must be a nonnegative integer") + numbers[key] = int(value) + if numbers[key] > 1_000_000: + raise ValueError(f"CSV {key} exceeds 1000000") + if numbers["step"] < 1 or numbers["earliest"] > numbers["latest"]: + raise ValueError("CSV requires step >= 1 and earliest <= latest") + resources = row["allowed_resources"].split("|") + starts = range(numbers["earliest"], numbers["latest"] + 1, numbers["step"]) + total += len(resources) * len(starts) + if total > 256: + raise ValueError("CSV expansion supports at most 256 raw candidates") + tasks.append( + { + "id": row["id"], + "group": row["group"], + "duration": numbers["duration"], + "original": { + "resource": row["original_resource"], + "start": numbers["original_start"], + }, + "change_cost": numbers["change_cost"], + "options": [ + { + "resource": resource, + "start": start, + "cost": numbers["shift_cost"] + * abs(start - numbers["original_start"]), + } + for resource in resources + for start in starts + ], + } + ) + data = {**calendar, "tasks": tasks} + Problem.from_dict(data) + return data diff --git a/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/classical.py b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/classical.py new file mode 100644 index 00000000..25cf7985 --- /dev/null +++ b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/classical.py @@ -0,0 +1,77 @@ +"""Independent exact certification by Cartesian enumeration of task choices.""" + +from itertools import product +from math import prod +from time import perf_counter +from typing import Any + +from .model import Model, evaluate + + +def solve_exact(model: Model, max_combinations: int = 1_000_000) -> dict[str, Any]: + """Certify the optimum using schedule constraints, never QUBO minimization. + + An empty domain proves infeasibility. A search budget overflow raises before + enumeration; it is never misreported as an infeasible instance. + """ + if type(max_combinations) is not int or max_combinations < 1: + raise ValueError("max_combinations must be a positive integer") + started = perf_counter() + combinations = prod(map(len, model.domains)) + if combinations > max_combinations: + raise ValueError( + f"exact search needs {combinations} combinations; limit={max_combinations}" + ) + best = None + feasible_count = 0 + for choice in product(*model.domains): + bits = [0] * len(model.variables) + for index in choice: + bits[index] = 1 + result = evaluate(model, bits) + if result["feasible"]: + feasible_count += 1 + if best is None or result["objective"] < best["objective"]: + best = result + return { + "status": "optimal" if best is not None else "infeasible", + "best": best, + "combinations": combinations, + "feasible_count": feasible_count, + "runtime_seconds": perf_counter() - started, + } + + +def solve_greedy(model: Model) -> dict[str, Any]: + """Choose the cheapest currently compatible option in input task order. + + No backtracking or repair is performed. A dead end is not an infeasibility + certificate; this is a transparent operational heuristic for comparisons. + """ + started = perf_counter() + bits = [0] * len(model.variables) + selected: set[int] = set() + conflicts = {frozenset((i, j)) for i, j, _ in model.conflicts} + for domain in model.domains: + options = sorted(domain, key=lambda i: (model.variables[i].cost, i)) + chosen = next( + ( + i + for i in options + if all(frozenset((i, j)) not in conflicts for j in selected) + ), + None, + ) + if chosen is None: + return { + "status": "dead_end", + "best": None, + "runtime_seconds": perf_counter() - started, + } + bits[chosen] = 1 + selected.add(chosen) + return { + "status": "feasible", + "best": evaluate(model, bits), + "runtime_seconds": perf_counter() - started, + } diff --git a/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/metrics.py b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/metrics.py new file mode 100644 index 00000000..6b6a2ff9 --- /dev/null +++ b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/metrics.py @@ -0,0 +1,108 @@ +"""Post-training diagnostics: optimum probability and native circuit resources.""" + +from __future__ import annotations + +from itertools import product +from math import ceil, log, log1p, prod +from typing import Any + +import numpy as np + +from .model import Model, evaluate +from .quantum import build_circuit + +SHOT_BUDGETS = (8, 16, 32, 64, 128, 512) + + +def hit_probability(probability: float, shots: int) -> float: + """Probability of at least one hit in independent draws from a fixed state.""" + if not np.isfinite(probability) or not 0 <= probability <= 1: + raise ValueError("probability must be in [0, 1]") + if type(shots) is not int or shots < 1: + raise ValueError("shots must be a positive integer") + return float(-np.expm1(shots * np.log1p(-probability))) if probability < 1 else 1.0 + + +def distribution_metrics(model: Model, probabilities: np.ndarray) -> dict[str, Any]: + """Enumerate schedules AFTER training; count all tied optima, not one witness. + + Hit curves condition on this already trained noiseless state. They exclude + training and statevector diagnostic costs and are not end-to-end speedups. + """ + if ( + probabilities.shape != (1 << len(model.variables),) + or not np.all(np.isfinite(probabilities)) + or np.any(probabilities < 0) + or not np.isclose(probabilities.sum(), 1.0) + ): + raise ValueError("expected a normalized finite nonnegative probability vector") + combinations = prod(map(len, model.domains)) + if combinations > 1_000_000: + raise ValueError("distribution certification exceeds 1000000 combinations") + optimum = None + optimal_indices: list[int] = [] + feasible_count = 0 + feasible_probability = 0.0 + for choice in product(*model.domains): + bits = [0] * len(model.variables) + for i in choice: + bits[i] = 1 + row = evaluate(model, bits) + if not row["feasible"]: + continue + index = sum(1 << i for i in choice) + feasible_count += 1 + feasible_probability += float(probabilities[index]) + cost = row["objective"] + if optimum is None or cost < optimum: + optimum, optimal_indices = cost, [index] + elif cost == optimum: + optimal_indices.append(index) + probability = min(1.0, float(probabilities[optimal_indices].sum())) + uniform = len(optimal_indices) / combinations if combinations else 0.0 + return { + "exact_objective": optimum, + "optimal_count": len(optimal_indices), + "combinations": combinations, + "optimal_probability": probability, + "feasible_probability": min(1.0, feasible_probability), + "uniform_optimal_probability": uniform, + "uniform_feasible_probability": feasible_count / combinations + if combinations + else 0, + "shots_for_95_percent": ( + 1 + if probability == 1 + else ceil(log(0.05) / log1p(-probability)) + if probability > 0 + else None + ), + "hit_curve": [ + { + "shots": s, + "quantum": hit_probability(probability, s), + "uniform": hit_probability(uniform, s), + } + for s in SHOT_BUDGETS + ], + "interpretation": "conditional on trained state; training cost excluded", + } + + +def circuit_resources( + model: Model, + parameters: list[float], + mixer: str = "xy", +) -> dict[str, Any]: + """Compare native CPU circuit operations; CRY is not decomposed into CNOTs.""" + result = {} + for mode in ("full", "auto"): + program = build_circuit(model, parameters, mixer, phase_mode=mode) + operations = dict(program.count_ops()) + result[mode] = { + "depth": program.depth(), + "operations": operations, + "cnot": operations.get("CNOT", 0), + "cry": operations.get("CRY", 0), + } + return result diff --git a/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/milp.py b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/milp.py new file mode 100644 index 00000000..6736d357 --- /dev/null +++ b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/milp.py @@ -0,0 +1,212 @@ +"""Independent raw-input MILP oracle; does not import the QUBO compiler.""" + +from __future__ import annotations + +from collections.abc import Sequence +from itertools import combinations +from time import perf_counter +from typing import Any + +import numpy as np +from scipy.optimize import Bounds, LinearConstraint, milp +from scipy.sparse import csc_matrix + +from .problem import Option, Problem, Task + +MAX_RAW_CANDIDATES = 2048 + + +def validate_assignment(problem: Problem, choices: Sequence[int]) -> dict[str, Any]: + """Check raw option indices, unary calendars, pairs and original-booking cost. + + This checker deliberately uses neither compiled variables nor conflict edges. + One raw option index per task is mandatory; invalid indices raise ValueError. + """ + if len(choices) != len(problem.tasks) or any( + type(i) is not int or not 0 <= i < len(t.options) + for t, i in zip(problem.tasks, choices, strict=True) + ): + raise ValueError("expected one valid raw option index per task") + selected = [(t, t.options[i]) for t, i in zip(problem.tasks, choices, strict=True)] + violations: list[dict[str, Any]] = [] + for task, option in selected: + if not _available(problem, task, option): + violations.append({"kind": "calendar", "task": task.id}) + for (a, oa), (b, ob) in combinations(selected, 2): + if _incompatible(problem, a, oa, b, ob): + violations.append({"kind": "pair", "tasks": [a.id, b.id]}) + preference = sum(o.cost for _, o in selected) + changed = [ + (t, o) + for t, o in selected + if t.original is not None and t.original != (o.resource, o.start) + ] + change = sum(t.change_cost for t, _ in changed) + return { + "feasible": not violations, + "violations": violations, + "violation_count": len(violations), + "objective": preference + change if not violations else None, + "preference_cost": preference, + "change_cost": change, + "changed_tasks": len(changed), + "max_start_shift": max( + (abs(o.start - t.original[1]) for t, o in changed if t.original), default=0 + ), + "choices": list(choices), + "schedule": [ + { + "task": t.id, + "resource": o.resource, + "start": o.start, + "end": o.start + t.duration, + } + for t, o in selected + ], + } + + +def solve_milp(problem: Problem, time_limit: float = 30.0) -> dict[str, Any]: + """Minimize raw-option cost with exact-one, calendar bounds and pair rows. + + HiGHS status, incumbent, dual bound and relative gap are reported separately. + A time limit (solver time only) is never presented as an optimality certificate. + At most 2048 raw candidates are accepted to bound quadratic model construction. + A task with no calendar-compatible raw option proves infeasibility before + constructing pair rows; this independent check does not use QUBO pruning. + """ + if isinstance(time_limit, bool) or not np.isfinite(time_limit) or time_limit <= 0: + raise ValueError("time_limit must be positive and finite") + started = perf_counter() + options = [ + (ti, oi, t, o) + for ti, t in enumerate(problem.tasks) + for oi, o in enumerate(t.options) + ] + if len(options) > MAX_RAW_CANDIDATES: + raise ValueError(f"MILP supports at most {MAX_RAW_CANDIDATES} raw candidates") + base: dict[str, Any] = { + "variables": len(options), + "best": None, + "dual_bound": None, + "gap": None, + } + if not problem.tasks or any(not t.options for t in problem.tasks): + feasible = not problem.tasks + base.update( + status="optimal" if feasible else "infeasible", + best=validate_assignment(problem, []) if feasible else None, + runtime_seconds=perf_counter() - started, + ) + return base + costs = [ + o.cost + + ( + t.change_cost + if t.original is not None and t.original != (o.resource, o.start) + else 0 + ) + for _, _, t, o in options + ] + upper = [int(_available(problem, t, o)) for _, _, t, o in options] + available_tasks = { + ti for allowed, (ti, _, _, _) in zip(upper, options, strict=True) if allowed + } + for ti, task in enumerate(problem.tasks): + if ti not in available_tasks: + base.update( + status="infeasible", + infeasible_task=task.id, + reason="no raw option satisfies the resource calendar", + constraints=0, + runtime_seconds=perf_counter() - started, + ) + return base + rows: list[list[int]] = [ + [i for i, (ti, _, _, _) in enumerate(options) if ti == task] + for task in range(len(problem.tasks)) + ] + lower = [1.0] * len(rows) + bounds = [1.0] * len(rows) + # If upper[i] == 0, y_i + y_j <= 1 follows from y_j <= 1 already. + # Keep every raw variable and its index, but omit these redundant pair rows. + available_indices = [i for i, allowed in enumerate(upper) if allowed] + for i, j in combinations(available_indices, 2): + ti, _, a, oa = options[i] + tj, _, b, ob = options[j] + if ti != tj and _incompatible(problem, a, oa, b, ob): + rows.append([i, j]) + lower.append(-np.inf) + bounds.append(1.0) + row_indices = [r for r, columns in enumerate(rows) for _ in columns] + column_indices = [i for columns in rows for i in columns] + matrix = csc_matrix( + (np.ones(len(row_indices)), (row_indices, column_indices)), + shape=(len(rows), len(options)), + ) + result = milp( + np.asarray(costs, dtype=float), + integrality=np.ones(len(options)), + bounds=Bounds(np.zeros(len(options)), upper), + constraints=LinearConstraint(matrix, lower, bounds), + options={"time_limit": float(time_limit), "mip_rel_gap": 0.0}, + ) + status = {0: "optimal", 1: "limit", 2: "infeasible"}.get(result.status, "error") + if result.x is not None: + if not np.allclose(result.x, np.rint(result.x), atol=1e-6, rtol=0): + raise RuntimeError("MILP incumbent is not integral") + for ti in range(len(problem.tasks)): + if ( + sum( + int(x > 0.5) + for x, (task, _, _, _) in zip(result.x, options, strict=True) + if task == ti + ) + != 1 + ): + raise RuntimeError("MILP incumbent violates exact-one assignment") + chosen = [ + oi for x, (_, oi, _, _) in zip(result.x, options, strict=True) if x > 0.5 + ] + best = validate_assignment(problem, chosen) + if not best["feasible"] or abs(best["objective"] - result.fun) > 1e-5: + raise RuntimeError("MILP incumbent failed independent semantic check") + base["best"] = best + if status == "optimal" and base["best"] is None: + raise RuntimeError("MILP reported optimal without an incumbent") + for output, attribute in (("dual_bound", "mip_dual_bound"), ("gap", "mip_gap")): + value = getattr(result, attribute, None) + base[output] = ( + float(value) if value is not None and np.isfinite(value) else None + ) + base.update( + status=status, + solver_status=int(result.status), + message=str(result.message), + constraints=len(rows), + runtime_seconds=perf_counter() - started, + ) + return base + + +def _available(problem: Problem, task: Task, option: Option) -> bool: + resource = next(r for r in problem.resources if r.id == option.resource) + finish = option.start + task.duration + resource.cleanup + return finish <= problem.horizon and all( + finish <= begin or option.start >= end for begin, end in resource.downtime + ) + + +def _incompatible(problem: Problem, a: Task, oa: Option, b: Task, ob: Option) -> bool: + end_a, end_b = oa.start + a.duration, ob.start + b.duration + if oa.resource == ob.resource: + cleanup = next(r.cleanup for r in problem.resources if r.id == oa.resource) + if not (end_a + cleanup <= ob.start or end_b + cleanup <= oa.start): + return True + if a.group == b.group and not (end_a <= ob.start or end_b <= oa.start): + return True + return any( + (before == a.id and after == b.id and end_a > ob.start) + or (before == b.id and after == a.id and end_b > oa.start) + for before, after in problem.precedence + ) diff --git a/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/model.py b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/model.py new file mode 100644 index 00000000..fa62914d --- /dev/null +++ b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/model.py @@ -0,0 +1,254 @@ +"""Auditable candidate pruning, QUBO compilation and independent validation.""" + +from __future__ import annotations + +from collections.abc import Sequence +from dataclasses import dataclass +from itertools import combinations +from typing import Any + +from .problem import Option, Problem, Task + + +@dataclass(frozen=True) +class Variable: + """One surviving candidate; its tuple position is its qubit index.""" + + task: int + option: Option + cost: int + + +@dataclass(frozen=True) +class Model: + """Upper-triangular QUBO: offset + linear*x + sum(i int: + """Evaluate the full QUBO, including its constant offset.""" + _validate_bits(bits, len(self.variables)) + return ( + self.offset + + sum(c * b for c, b in zip(self.linear, bits, strict=True)) + + sum(c * bits[i] * bits[j] for i, j, c in self.quadratic) + ) + + def audit(self) -> dict[str, Any]: + """Return JSON-ready coefficients and a stable variable-to-qubit map.""" + return { + "qubits": len(self.variables), + "penalty": self.penalty, + "objective_bound": self.objective_bound, + "offset": self.offset, + "linear": list(self.linear), + "quadratic": list(self.quadratic), + "domains": self.domains, + "pruned": self.pruned, + "variables": [ + { + "index": i, + "task": self.problem.tasks[v.task].id, + "resource": v.option.resource, + "start": v.option.start, + "cost": v.cost, + } + for i, v in enumerate(self.variables) + ], + "conflicts": [ + {"i": i, "j": j, "reasons": reasons} for i, j, reasons in self.conflicts + ], + } + + +def compile_qubo(problem: Problem, penalty: int | None = None) -> Model: + """Prune unary-invalid options and compile exact-one and pair penalties. + + Let U be the sum of the largest surviving cost per task. For nonnegative + costs, A=U+1 makes every infeasible bit vector worse than every feasible + schedule. A custom penalty must be an integer strictly greater than U. + """ + resources = {r.id: r for r in problem.resources} + variables: list[Variable] = [] + domains: list[tuple[int, ...]] = [] + pruned: list[dict[str, Any]] = [] + for ti, task in enumerate(problem.tasks): + domain = [] + for option in task.options: + resource = resources[option.resource] + end = option.start + task.duration + resource.cleanup + reasons = [] + if end > problem.horizon: + reasons.append("horizon") + if any(_overlap(option.start, end, a, b) for a, b in resource.downtime): + reasons.append("downtime") + if reasons: + pruned.append( + { + "task": task.id, + "resource": option.resource, + "start": option.start, + "reasons": reasons, + } + ) + else: + domain.append(len(variables)) + variables.append(Variable(ti, option, _cost(task, option))) + if len(variables) > 256: + raise ValueError( + "model compilation supports at most 256 candidates" + ) + domains.append(tuple(domain)) + bound = sum(max((variables[i].cost for i in d), default=0) for d in domains) + if penalty is None: + penalty = bound + 1 + if type(penalty) is not int or penalty <= bound: + raise ValueError(f"penalty must be an integer greater than {bound}") + linear = [v.cost - penalty for v in variables] + quadratic: dict[tuple[int, int], int] = {} + for indices in domains: + for i, j in combinations(indices, 2): + quadratic[i, j] = 2 * penalty + conflicts = [] + precedence = set(problem.precedence) + for i, j in combinations(range(len(variables)), 2): + left, right = variables[i], variables[j] + if left.task == right.task: + continue + a, b = problem.tasks[left.task], problem.tasks[right.task] + oa, ob = left.option, right.option + ea, eb = oa.start + a.duration, ob.start + b.duration + reasons = [] + if oa.resource == ob.resource: + cleanup = resources[oa.resource].cleanup + if _overlap(oa.start, ea + cleanup, ob.start, eb + cleanup): + reasons.append("resource") + if a.group == b.group and _overlap(oa.start, ea, ob.start, eb): + reasons.append("group") + if ( + (a.id, b.id) in precedence + and ea > ob.start + or (b.id, a.id) in precedence + and eb > oa.start + ): + reasons.append("precedence") + if reasons: + # Count a forbidden candidate pair once, even with several reasons. + conflicts.append((i, j, tuple(reasons))) + quadratic[i, j] = penalty + return Model( + problem, + tuple(variables), + tuple(domains), + tuple(linear), + tuple((i, j, c) for (i, j), c in sorted(quadratic.items())), + penalty * len(problem.tasks), + penalty, + bound, + tuple(conflicts), + tuple(pruned), + ) + + +def evaluate(model: Model, bits: Sequence[int]) -> dict[str, Any]: + """Decode and check constraints from input semantics, independently of QUBO. + + Violation count counts violated exact-one task constraints and distinct + forbidden selected pairs. Detailed pair reasons may contain multiple causes. + An infeasible vector has objective=None; raw_cost remains available to audit. + """ + _validate_bits(bits, len(model.variables)) + selected = [i for i, bit in enumerate(bits) if bit] + rows: list[dict[str, Any]] = [] + violations = [] + raw_cost = 0 + resources = {r.id: r for r in model.problem.resources} + for ti, task in enumerate(model.problem.tasks): + chosen = [i for i in selected if model.variables[i].task == ti] + if len(chosen) != 1: + violations.append( + {"kind": "exact_one", "task": task.id, "count": len(chosen)} + ) + for i in chosen: + option = model.variables[i].option + raw_cost += _cost(task, option) + rows.append( + { + "variable": i, + "task": task.id, + "group": task.group, + "resource": option.resource, + "start": option.start, + "end": option.start + task.duration, + "reserved_end": option.start + + task.duration + + resources[option.resource].cleanup, + "changed": task.original is not None + and task.original != (option.resource, option.start), + } + ) + for a, b in combinations(rows, 2): + if a["task"] == b["task"]: + continue + reasons = [] + if a["resource"] == b["resource"] and _overlap( + a["start"], a["reserved_end"], b["start"], b["reserved_end"] + ): + reasons.append("resource") + if a["group"] == b["group"] and _overlap( + a["start"], a["end"], b["start"], b["end"] + ): + reasons.append("group") + for before, after in model.problem.precedence: + if ( + (a["task"], b["task"]) == (before, after) + and a["end"] > b["start"] + or (b["task"], a["task"]) == (before, after) + and b["end"] > a["start"] + ): + reasons.append("precedence") + if reasons: + violations.append( + { + "kind": "conflict", + "tasks": [a["task"], b["task"]], + "variables": [a["variable"], b["variable"]], + "reasons": reasons, + } + ) + return { + "feasible": not violations, + "objective": raw_cost if not violations else None, + "raw_cost": raw_cost, + "violation_count": len(violations), + "violations": violations, + "schedule": sorted(rows, key=lambda row: (row["start"], row["task"])), + "bits": list(bits), + "qubo_energy": model.energy(bits), + } + + +def _cost(task: Task, option: Option) -> int: + changed = task.original is not None and task.original != ( + option.resource, + option.start, + ) + return option.cost + task.change_cost * changed + + +def _overlap(a: int, b: int, c: int, d: int) -> bool: + return a < d and c < b + + +def _validate_bits(bits: Sequence[int], size: int) -> None: + if len(bits) != size or any(b not in (0, 1) for b in bits): + raise ValueError(f"expected {size} binary values in qubit-index order") diff --git a/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/problem.py b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/problem.py new file mode 100644 index 00000000..75cb225f --- /dev/null +++ b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/problem.py @@ -0,0 +1,189 @@ +"""Validated, immutable inputs for shared laboratory rescheduling.""" + +from __future__ import annotations + +import json +from dataclasses import dataclass +from pathlib import Path +from typing import Any + + +@dataclass(frozen=True) +class Resource: + """A unit-capacity instrument with cleanup time and half-open downtime.""" + + id: str + cleanup: int + downtime: tuple[tuple[int, int], ...] + + +@dataclass(frozen=True) +class Option: + """An explicitly allowed instrument/start assignment and preference cost.""" + + resource: str + start: int + cost: int + + +@dataclass(frozen=True) +class Task: + """An indivisible experiment belonging to one mutually exclusive group.""" + + id: str + group: str + duration: int + options: tuple[Option, ...] + original: tuple[str, int] | None + change_cost: int + + +@dataclass(frozen=True) +class Problem: + """Validated finite scheduling instance; empty domains may be infeasible.""" + + name: str + horizon: int + resources: tuple[Resource, ...] + tasks: tuple[Task, ...] + precedence: tuple[tuple[str, str], ...] + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> Problem: + """Parse strict JSON-shaped data; reject typos, booleans and bad references. + + Costs and time values are bounded nonnegative integers. A task with no + options is a valid but infeasible instance, as is a cyclic precedence + graph (durations are strictly positive). + """ + _keys( + data, {"name", "horizon", "resources", "tasks"}, {"precedence"}, "problem" + ) + name = _name(data["name"], "name") + horizon = _integer(data["horizon"], "horizon", minimum=1) + resources = [] + for raw in _list(data["resources"], "resources"): + _keys(raw, {"id"}, {"cleanup", "downtime"}, "resource") + rid = _name(raw["id"], "resource.id") + cleanup = _integer(raw.get("cleanup", 0), "cleanup") + downtime = [] + for interval in _list(raw.get("downtime", []), "downtime"): + if not isinstance(interval, list) or len(interval) != 2: + raise ValueError("downtime must contain [start, end] pairs") + start, end = (_integer(v, "downtime endpoint") for v in interval) + if not 0 <= start < end <= horizon: + raise ValueError( + "downtime must satisfy 0 <= start < end <= horizon" + ) + downtime.append((start, end)) + resources.append(Resource(rid, cleanup, tuple(downtime))) + _unique([r.id for r in resources], "resource ids") + if not resources: + raise ValueError("at least one resource is required") + resource_ids = {r.id for r in resources} + tasks = [] + for raw in _list(data["tasks"], "tasks"): + _keys( + raw, + {"id", "group", "duration", "options"}, + {"original", "change_cost"}, + "task", + ) + tid = _name(raw["id"], "task.id") + group = _name(raw["group"], "group") + duration = _integer(raw["duration"], "duration", minimum=1) + options = [] + for option in _list(raw["options"], "options"): + _keys(option, {"resource", "start", "cost"}, set(), "option") + rid, start = _assignment(option, resource_ids) + options.append(Option(rid, start, _integer(option["cost"], "cost"))) + _unique([(o.resource, o.start) for o in options], "task options") + original = None + if "original" in raw: + _keys(raw["original"], {"resource", "start"}, set(), "original") + original = _assignment(raw["original"], resource_ids) + change_cost = _integer(raw.get("change_cost", 0), "change_cost") + if change_cost and original is None: + raise ValueError("change_cost requires original") + tasks.append( + Task(tid, group, duration, tuple(options), original, change_cost) + ) + _unique([t.id for t in tasks], "task ids") + task_ids = {t.id for t in tasks} + precedence = [] + for pair in _list(data.get("precedence", []), "precedence"): + if not isinstance(pair, list) or len(pair) != 2: + raise ValueError("precedence must contain [before, after] pairs") + before, after = (_name(v, "precedence task") for v in pair) + if before not in task_ids or after not in task_ids or before == after: + raise ValueError("precedence needs two distinct known task ids") + precedence.append((before, after)) + _unique(precedence, "precedence pairs") + return cls(name, horizon, tuple(resources), tuple(tasks), tuple(precedence)) + + +def load_problem(path: str | Path) -> Problem: + """Read UTF-8 JSON, rejecting duplicate keys and non-finite constants.""" + return parse_problem(Path(path).read_text(encoding="utf-8")) + + +def parse_problem(text: str) -> Problem: + """Parse a JSON snapshot with the same strict validation as file loading.""" + + def pairs_hook(pairs: list[tuple[str, Any]]) -> dict[str, Any]: + """Reject duplicate object keys instead of silently keeping the last.""" + result: dict[str, Any] = {} + for key, value in pairs: + if key in result: + raise ValueError(f"duplicate JSON key: {key}") + result[key] = value + return result + + def invalid_constant(value: str) -> None: + """Reject nonstandard JSON numeric constants.""" + raise ValueError(f"invalid JSON constant: {value}") + + return Problem.from_dict( + json.loads( + text, + object_pairs_hook=pairs_hook, + parse_constant=invalid_constant, + ) + ) + + +def _keys(value: Any, required: set[str], optional: set[str], label: str) -> None: + if not isinstance(value, dict): + raise ValueError(f"{label} must be an object") + if required - value.keys() or value.keys() - required - optional: + raise ValueError(f"{label}: missing or unknown fields") + + +def _integer(value: Any, label: str, minimum: int = 0) -> int: + if type(value) is not int or not minimum <= value <= 1_000_000: + raise ValueError(f"{label} must be an integer in [{minimum}, 1000000]") + return value + + +def _name(value: Any, label: str) -> str: + if not isinstance(value, str) or not value.strip(): + raise ValueError(f"{label} must be a nonempty string") + return value + + +def _list(value: Any, label: str) -> list[Any]: + if not isinstance(value, list): + raise ValueError(f"{label} must be an array") + return value + + +def _unique(values: list[Any], label: str) -> None: + if len(set(values)) != len(values): + raise ValueError(f"duplicate {label}") + + +def _assignment(raw: dict[str, Any], resource_ids: set[str]) -> tuple[str, int]: + rid = _name(raw["resource"], "resource reference") + if rid not in resource_ids: + raise ValueError(f"unknown resource: {rid}") + return rid, _integer(raw["start"], "start") diff --git a/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/quantum.py b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/quantum.py new file mode 100644 index 00000000..9063a171 --- /dev/null +++ b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/quantum.py @@ -0,0 +1,270 @@ +"""Seeded CPU statevector QAOA using upstream phase and XY circuit components.""" + +from __future__ import annotations + +import itertools +from dataclasses import replace +from time import perf_counter +from typing import Any + +import numpy as np +from pyqpanda3.core import CPUQVM, RX, H, QCircuit, QProg +from pyqpanda3.hamiltonian import PauliOperator +from scipy.optimize import minimize + +from pyqpanda_alg.QAOA.default_circuits import iswap, linear_w_state +from pyqpanda_alg.QAOA.qaoa import pauli_z_operator_to_circuit + +from .model import Model, evaluate + +MAX_QUBITS = 16 + + +def to_ising(model: Model, scale: float = 1.0) -> PauliOperator: + """Map x_i=(I-Z_i)/2 with explicit indices, preserving the constant offset.""" + if not np.isfinite(scale) or scale <= 0: + raise ValueError("scale must be positive and finite") + terms = {"": float(model.offset)} + for i, coefficient in enumerate(model.linear): + terms[""] += coefficient / 2 + terms[f"Z{i}"] = terms.get(f"Z{i}", 0.0) - coefficient / 2 + for i, j, coefficient in model.quadratic: + terms[""] += coefficient / 4 + terms[f"Z{i}"] = terms.get(f"Z{i}", 0.0) - coefficient / 4 + terms[f"Z{j}"] = terms.get(f"Z{j}", 0.0) - coefficient / 4 + terms[f"Z{i} Z{j}"] = coefficient / 4 + return PauliOperator({key: value / scale for key, value in terms.items()}) + + +def build_circuit( + model: Model, + parameters: list[float], + mixer: str = "xy", + *, + phase_mode: str = "auto", +) -> QProg: + """Build alternating cost/mixer layers; parameter order is gamma,beta per layer. + + XY starts each task in a W state and applies adjacent exchange rotations. + Singleton domains receive no mixer, preserving their required bit value. + X starts in |+> and applies RX(2*beta) to every qubit as an ablation. + auto omits the identically zero exact-one penalty ONLY for XY. full keeps + the original phase for equivalence/resource audits. Scaling stays unchanged. + """ + _check_model(model) + if mixer not in ("xy", "x"): + raise ValueError("mixer must be xy or x") + if not parameters or len(parameters) % 2 or not np.all(np.isfinite(parameters)): + raise ValueError("parameters must be finite gamma,beta pairs") + qubits = list(range(len(model.variables))) + circuit = QCircuit() + if mixer == "xy": + for domain in model.domains: + circuit << linear_w_state(list(domain)) + else: + for qubit in qubits: + circuit << H(qubit) + if phase_mode not in ("auto", "full"): + raise ValueError("phase_mode must be auto or full") + phase_model = model + if mixer == "xy" and phase_mode == "auto": + phase_model = replace( + model, + offset=0, + linear=tuple(v.cost for v in model.variables), + quadratic=tuple( + (i, j, c) + for i, j, c in model.quadratic + if model.variables[i].task != model.variables[j].task + ), + ) + operator = to_ising(phase_model, float(model.penalty)) + for gamma, beta in zip(parameters[::2], parameters[1::2], strict=True): + phase, _ = pauli_z_operator_to_circuit(operator, qubits, gamma) + circuit << phase + if mixer == "xy": + for domain in model.domains: + for left, right in itertools.pairwise(domain): + circuit << iswap(left, right, 2 * beta) + else: + for qubit in qubits: + circuit << RX(qubit, 2 * beta) + program = QProg(len(qubits)) + program << circuit + return program + + +def circuit_probabilities( + model: Model, + parameters: list[float], + mixer: str = "xy", + *, + phase_mode: str = "auto", +) -> np.ndarray: + """Simulate without measurement; qubit 0 is the least-significant bit.""" + machine = CPUQVM() + machine.run(build_circuit(model, parameters, mixer, phase_mode=phase_mode), shots=1) + probabilities = np.abs(np.asarray(machine.result().get_state_vector())) ** 2 + if len(probabilities) != 1 << len(model.variables): + raise RuntimeError("unexpected simulator statevector dimension") + return probabilities / probabilities.sum() + + +def solve_qaoa( + model: Model, + seed: int = 7, + layers: int = 1, + shots: int = 512, + maxiter: int = 60, + restarts: int = 2, + mixer: str = "xy", + *, + phase_mode: str = "auto", +) -> dict[str, Any]: + """Optimize exact expected QUBO energy, then sample the final quantum state. + + Uses independent NumPy generators for parameter initialization and final + Born-distribution sampling because CPUQVM does not expose a portable seed. + No exact optimum, feasibility-filtered state space or classical repair enters + training. Only sampled bitstrings compete for the returned schedule. + """ + started = perf_counter() + for name, value, lower, upper in ( + ("seed", seed, 0, 2**32 - 1), + ("layers", layers, 1, 8), + ("shots", shots, 1, 1_000_000), + ("maxiter", maxiter, 6, 100_000), + ("restarts", restarts, 1, 100), + ): + if type(value) is not int or not lower <= value <= upper: + raise ValueError(f"{name} must be an integer in [{lower}, {upper}]") + if maxiter < 2 * layers + 2: + raise ValueError("maxiter must be at least 2 * layers + 2 for COBYLA") + if mixer not in ("xy", "x"): + raise ValueError("mixer must be xy or x") + if phase_mode not in ("auto", "full"): + raise ValueError("phase_mode must be auto or full") + if any(not domain for domain in model.domains): + return { + "status": "infeasible_domain", + "best": None, + "seed": seed, + "mixer": mixer, + "runtime_seconds": perf_counter() - started, + "circuit_evaluations": 0, + } + if not model.variables: + return { + "status": "trivial", + "best": evaluate(model, []), + "seed": seed, + "mixer": mixer, + "runtime_seconds": perf_counter() - started, + "circuit_evaluations": 0, + } + _check_model(model) + init_seed, sample_seed = np.random.SeedSequence(seed).spawn(2) + initializer = np.random.default_rng(init_seed) + sampler = np.random.default_rng(sample_seed) + indices = np.arange(1 << len(model.variables), dtype=np.int64) + energies = np.full(len(indices), model.offset, dtype=float) + for i, coefficient in enumerate(model.linear): + energies += coefficient * ((indices >> i) & 1) + for i, j, coefficient in model.quadratic: + energies += coefficient * ((indices >> i) & 1) * ((indices >> j) & 1) + best_loss = float("inf") + best_parameters: list[float] = [] + best_probabilities = np.empty(0) + history = [] + + def loss(parameters: np.ndarray) -> float: + """Record a real simulator evaluation and retain the best parameters.""" + nonlocal best_loss, best_parameters, best_probabilities + probabilities = circuit_probabilities( + model, parameters.tolist(), mixer, phase_mode=phase_mode + ) + expected = float(probabilities @ energies) + history.append(expected) + if expected < best_loss: + best_loss = expected + best_parameters = parameters.tolist() + best_probabilities = probabilities + return expected / model.penalty + + optimizer_runs = [] + for _ in range(restarts): + initial = initializer.uniform(0.0, np.pi, size=2 * layers) + result = minimize( + loss, + initial, + method="COBYLA", + options={"maxiter": maxiter, "rhobeg": 0.5, "tol": 1e-5}, + ) + optimizer_runs.append( + { + "success": bool(result.success), + "message": str(result.message), + "evaluations": int(result.nfev), + } + ) + counts = sampler.multinomial(shots, best_probabilities) + observed = [ + (_bits(int(i), len(model.variables)), int(counts[i])) + for i in np.flatnonzero(counts) + ] + candidates = [evaluate(model, bits) for bits, _ in observed] + best = min( + candidates, + key=lambda row: (not row["feasible"], row["qubo_energy"], row["bits"]), + ) + # These diagnostics use the input validator, not a classical optimal solution. + feasible_probability = 0.0 + one_hot_probability = 0.0 + for state_index in np.flatnonzero(best_probabilities > 1e-15): + i = int(state_index) + bits = _bits(i, len(model.variables)) + if all(sum(bits[j] for j in domain) == 1 for domain in model.domains): + one_hot_probability += float(best_probabilities[i]) + if evaluate(model, bits)["feasible"]: + feasible_probability += float(best_probabilities[i]) + sampled_feasible = sum( + count + for row, (_, count) in zip(candidates, observed, strict=True) + if row["feasible"] + ) + return { + "status": "feasible" if best["feasible"] else "no_feasible_sample", + "best": best, + "seed": seed, + "mixer": mixer, + "phase_mode": phase_mode, + "layers": layers, + "shots": shots, + "maxiter": maxiter, + "restarts": restarts, + "parameters": best_parameters, + "expected_energy": best_loss, + "initial_expected_energy": history[0], + "history": history, + "feasible_probability": feasible_probability, + "one_hot_probability": one_hot_probability, + "sampled_feasible_fraction": sampled_feasible / shots, + "counts": {"".join(map(str, bits[::-1])): count for bits, count in observed}, + "circuit_evaluations": len(history), + "optimizer_runs": optimizer_runs, + "sampling": "seeded NumPy multinomial from CPUQVM Born probabilities", + "runtime_seconds": perf_counter() - started, + } + + +def _check_model(model: Model) -> None: + if not 1 <= len(model.variables) <= MAX_QUBITS: + raise ValueError( + f"CPU simulation requires 1..{MAX_QUBITS} surviving candidates" + ) + if any(not domain for domain in model.domains): + raise ValueError("cannot prepare an empty task domain") + + +def _bits(index: int, size: int) -> list[int]: + return [(index >> i) & 1 for i in range(size)] diff --git a/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/reduction.py b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/reduction.py new file mode 100644 index 00000000..0cc2b6f9 --- /dev/null +++ b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/reduction.py @@ -0,0 +1,272 @@ +"""Lossless constraint propagation, components and auditable restoration.""" + +from __future__ import annotations + +from collections import deque +from collections.abc import Sequence +from dataclasses import dataclass, replace +from time import perf_counter +from typing import Any + +import numpy as np + +from .model import Model, compile_qubo, evaluate +from .quantum import MAX_QUBITS, solve_qaoa + + +@dataclass(frozen=True) +class Reduction: + """Lossless feasible-schedule map; indices always refer to the original model.""" + + source: Model + fixed: tuple[int, ...] + components: tuple[Model, ...] + mappings: tuple[tuple[int, ...], ...] + removals: tuple[dict[str, Any], ...] + infeasible_task: str | None + pruning: str = "singleton" + + def audit(self) -> dict[str, Any]: + """Explain every deletion, fixed cost and component-to-original mapping.""" + return { + "method": ( + "arc consistency + singleton propagation" + " + disconnected conflict components" + if self.pruning == "arc" + else "singleton propagation + disconnected conflict components" + ), + "original_qubits": len(self.source.variables), + "fixed_variables": self.fixed, + "fixed_cost": sum(self.source.variables[i].cost for i in self.fixed), + "removals": self.removals, + "infeasible_task": self.infeasible_task, + "component_qubits": [len(m.variables) for m in self.components], + "max_component_qubits": max(map(len, self.mappings), default=0), + "component_to_original": self.mappings, + "components": [m.audit() for m in self.components], + } + + def restore(self, assignments: Sequence[Sequence[int]]) -> dict[str, Any]: + """Reinsert fixed choices and validate all original task constraints.""" + if self.infeasible_task is not None: + raise ValueError("cannot restore a proven infeasible reduction") + if len(assignments) != len(self.components): + raise ValueError("one bit vector per component is required") + bits = [0] * len(self.source.variables) + for i in self.fixed: + bits[i] = 1 + for model, mapping, local in zip( + self.components, self.mappings, assignments, strict=True + ): + evaluate(model, local) # Validate dimensions and binary values. + for i, bit in zip(mapping, local, strict=True): + bits[i] = bit + return evaluate(self.source, bits) + + +def _enforce_arc_consistency( + model: Model, + active: list[set[int]], + neighbours: dict[int, dict[int, tuple[str, ...]]], + removals: list[dict[str, Any]], +) -> None: + """Run deterministic AC-3 and retain the full opposing domain as a witness.""" + adjacent: dict[int, set[int]] = {t: set() for t in range(len(active))} + for i, j, _ in model.conflicts: + a, b = model.variables[i].task, model.variables[j].task + adjacent[a].add(b) + adjacent[b].add(a) + queue = deque((a, b) for a in adjacent for b in sorted(adjacent[a])) + queued = set(queue) + while queue: + a, b = queue.popleft() + queued.remove((a, b)) + revised = False + for i in sorted(active[a]): + if any(j not in neighbours[i] for j in active[b]): + continue + removals.append( + { + "variable": i, + "rule": "no_support", + "against_task": model.problem.tasks[b].id, + "blockers": [ + {"variable": j, "reasons": neighbours[i][j]} + for j in sorted(active[b]) + ], + } + ) + active[a].remove(i) + revised = True + if not active[a]: + return + if revised: + for other in sorted(adjacent[a] - {b}): + arc = (other, a) + if arc not in queued: + queue.append(arc) + queued.add(arc) + + +def reduce_model(model: Model, *, pruning: str = "singleton") -> Reduction: + """Preserve every feasible schedule by propagating logically forced choices. + + A singleton must be selected, hence all its conflicting candidates are + impossible. Optional pruning="arc" first removes choices incompatible with + every surviving choice of another task, using AC-3. Neither mode proves + global feasibility from nonempty domains. Then partition by surviving + forbidden pairs. No dominance rules or heuristic freezing occur. + """ + if pruning not in ("singleton", "arc"): + raise ValueError("pruning must be singleton or arc") + active = [set(domain) for domain in model.domains] + neighbours: dict[int, dict[int, tuple[str, ...]]] = { + i: {} for i in range(len(model.variables)) + } + for i, j, reasons in model.conflicts: + neighbours[i][j] = reasons + neighbours[j][i] = reasons + fixed: list[int] = [] + removals: list[dict[str, Any]] = [] + if pruning == "arc" and all(active): + _enforce_arc_consistency(model, active, neighbours, removals) + processed: set[int] = set() + while True: + empty = next((t for t, domain in enumerate(active) if not domain), None) + if empty is not None: + return Reduction( + model, + tuple(fixed), + (), + (), + tuple(removals), + model.problem.tasks[empty].id, + pruning, + ) + task = next( + (t for t, d in enumerate(active) if len(d) == 1 and t not in processed), + None, + ) + if task is None: + break + processed.add(task) + forced = min(active[task]) + fixed.append(forced) + for other, reasons in sorted(neighbours[forced].items()): + ti = model.variables[other].task + if other in active[ti]: + active[ti].remove(other) + removals.append( + { + "variable": other, + "forced_by": forced, + "reasons": reasons, + } + ) + remaining = set(range(len(active))) - processed + adjacency: dict[int, set[int]] = {t: set() for t in remaining} + for i, j, _ in model.conflicts: + a, b = model.variables[i].task, model.variables[j].task + if a in remaining and b in remaining and i in active[a] and j in active[b]: + adjacency[a].add(b) + adjacency[b].add(a) + components = [] + mappings = [] + while remaining: + pending = [min(remaining)] + tasks: set[int] = set() + while pending: + task = pending.pop() + if task in tasks: + continue + tasks.add(task) + pending.extend(sorted(adjacency[task] - tasks, reverse=True)) + remaining -= tasks + ordered = sorted(tasks) + ids = {model.problem.tasks[t].id for t in ordered} + problem = replace( + model.problem, + tasks=tuple( + replace( + model.problem.tasks[t], + options=tuple(model.variables[i].option for i in sorted(active[t])), + ) + for t in ordered + ), + precedence=tuple( + (a, b) for a, b in model.problem.precedence if a in ids and b in ids + ), + ) + components.append(compile_qubo(problem)) + mappings.append(tuple(i for t in ordered for i in sorted(active[t]))) + return Reduction( + model, + tuple(fixed), + tuple(components), + tuple(mappings), + tuple(removals), + None, + pruning, + ) + + +def solve_reduced( + model: Model, + seed: int = 7, + layers: int = 1, + shots: int = 512, + maxiter: int = 60, + restarts: int = 2, + mixer: str = "xy", + *, + pruning: str = "singleton", +) -> dict[str, Any]: + """Solve each exact component on CPU and restore; never fall back silently. + + Each component receives its own deterministic training/sampling seed and + the stated shot budget. Independent component draws give product feasibility. + A component exceeding MAX_QUBITS is reported before any quantum execution. + Use pruning="arc" for lossless unsupported-candidate deletion before fixing. + """ + started = perf_counter() + # Validate solver arguments even when propagation solves the entire problem. + empty = compile_qubo(replace(model.problem, tasks=(), precedence=())) + solve_qaoa(empty, seed, layers, shots, maxiter, restarts, mixer) + reduction = reduce_model(model, pruning=pruning) + result: dict[str, Any] = { + "seed": seed, + "mixer": mixer, + "reduction": reduction.audit(), + "best": None, + "components": [], + "circuit_evaluations": 0, + } + if reduction.infeasible_task is not None: + result["status"] = "infeasible_propagation" + elif any(len(m.variables) > MAX_QUBITS for m in reduction.components): + result["status"] = "resource_limit" + else: + seeds = np.random.SeedSequence(seed).spawn(len(reduction.components)) + runs = [ + solve_qaoa( + m, int(s.generate_state(1)[0]), layers, shots, maxiter, restarts, mixer + ) + for m, s in zip(reduction.components, seeds, strict=True) + ] + best = reduction.restore([run["best"]["bits"] for run in runs]) + result.update( + { + "status": ("feasible" if runs else "classical_propagation") + if best["feasible"] + else "no_feasible_sample", + "best": best, + "components": runs, + "circuit_evaluations": sum(r["circuit_evaluations"] for r in runs), + "feasible_probability": float( + np.prod([r["feasible_probability"] for r in runs]) + ), + "total_shots": shots * len(runs), + } + ) + result["runtime_seconds"] = perf_counter() - started + return result diff --git a/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/report.py b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/report.py new file mode 100644 index 00000000..34abcc26 --- /dev/null +++ b/pyqpanda-algorithm/pyqpanda_alg/LabScheduling/report.py @@ -0,0 +1,184 @@ +"""End-to-end reports and compact, machine-readable CLI output.""" + +from __future__ import annotations + +import hashlib +import platform +from importlib.metadata import version +from math import prod +from pathlib import Path +from time import perf_counter +from typing import Any + +import numpy as np + +from .classical import solve_exact +from .metrics import circuit_resources, distribution_metrics +from .milp import MAX_RAW_CANDIDATES, solve_milp, validate_assignment +from .model import compile_qubo, evaluate +from .problem import parse_problem +from .quantum import circuit_probabilities, solve_qaoa +from .reduction import solve_reduced + + +def run_experiment( + path: str | Path, + seed: int = 7, + layers: int = 1, + shots: int = 512, + maxiter: int = 60, + restarts: int = 2, + mixer: str = "xy", + *, + reduce: bool = False, + pruning: str = "singleton", +) -> dict[str, Any]: + """Solve a snapshot; report unavailable baseline budgets without losing results. + + Optional arc pruning requires reduce=True. A skipped MILP contributes no + certificate; absolute_gap is unknown unless enumeration proves optimality. + """ + if pruning not in ("singleton", "arc"): + raise ValueError("pruning must be singleton or arc") + if pruning != "singleton" and not reduce: + raise ValueError("arc pruning requires reduce=True (--reduce)") + started = perf_counter() + path = Path(path) + snapshot = path.read_bytes() + problem = parse_problem(snapshot.decode("utf-8")) + model = compile_qubo(problem) + quantum = ( + solve_reduced( + model, seed, layers, shots, maxiter, restarts, mixer, pruning=pruning + ) + if reduce + else solve_qaoa(model, seed, layers, shots, maxiter, restarts, mixer) + ) + exact: dict[str, Any] = ( + solve_exact(model) + if prod(map(len, model.domains)) <= 1_000_000 + else { + "status": "budget_exceeded", + "best": None, + "combinations": prod(map(len, model.domains)), + "feasible_count": None, + } + ) + raw_candidates = sum(len(task.options) for task in problem.tasks) + milp: dict[str, Any] = ( + solve_milp(problem) + if raw_candidates <= MAX_RAW_CANDIDATES + else { + "status": "budget_exceeded", + "variables": raw_candidates, + "max_variables": MAX_RAW_CANDIDATES, + "reason": "raw candidate count exceeds the independent MILP budget", + "best": None, + "dual_bound": None, + "gap": None, + } + ) + if exact["best"] and milp["status"] == "optimal": + if exact["best"]["objective"] != milp["best"]["objective"]: + raise RuntimeError("enumeration and raw-input MILP disagree") + if milp["status"] == "infeasible" and ( + exact["best"] or quantum["best"] and quantum["best"]["feasible"] + ): + raise RuntimeError("raw-input MILP rejected a feasible witness") + if exact["status"] == "infeasible" and milp["best"]: + raise RuntimeError("enumeration rejected a raw-input MILP witness") + if quantum["best"] and quantum["best"]["feasible"]: + choices = [] + for ti, task in enumerate(problem.tasks): + chosen = next( + v.option + for v, bit in zip(model.variables, quantum["best"]["bits"], strict=True) + if bit and v.task == ti + ) + choices.append(task.options.index(chosen)) + independent = validate_assignment(problem, choices) + if ( + not independent["feasible"] + or independent["objective"] != quantum["best"]["objective"] + ): + raise RuntimeError("quantum schedule failed raw-input validation") + quantum["business_metrics"] = independent + if "parameters" in quantum: + quantum["resources"] = circuit_resources(model, quantum["parameters"], mixer) + quantum["distribution"] = distribution_metrics( + model, circuit_probabilities(model, quantum["parameters"], mixer) + ) + uniform_started = perf_counter() + random = np.random.default_rng(seed) + uniform_best = None + feasible_samples = 0 + if all(model.domains): + for _ in range(shots): + bits = [0] * len(model.variables) + for domain in model.domains: + bits[int(random.choice(domain))] = 1 + candidate = evaluate(model, bits) + feasible_samples += candidate["feasible"] + if uniform_best is None or ( + not candidate["feasible"], + candidate["qubo_energy"], + ) < (not uniform_best["feasible"], uniform_best["qubo_energy"]): + uniform_best = candidate + uniform = { + "best": uniform_best, + "shots": shots, + "seed": seed, + "feasible_probability": ( + exact["feasible_count"] / exact["combinations"] + if exact["combinations"] and exact["feasible_count"] is not None + else 0.0 + if exact["combinations"] == 0 + else None + ), + "sampled_feasible_fraction": feasible_samples / shots, + "runtime_seconds": perf_counter() - uniform_started, + } + quantum_best = quantum["best"] + exact_best = exact["best"] or ( + milp["best"] if milp["status"] == "optimal" else None + ) + gap = ( + quantum_best["objective"] - exact_best["objective"] + if quantum_best and quantum_best["feasible"] and exact_best + else None + ) + if gap is not None and gap < 0: + raise RuntimeError( + "quantum result contradicts the independent exact certificate" + ) + return { + "schema_version": 2, + "problem": problem.name, + "input_sha256": hashlib.sha256(snapshot).hexdigest(), + "environment": { + "python": platform.python_version(), + "platform": platform.system(), + **{ + name: version(name) + for name in ("pyqpanda3", "pyqpanda_alg", "numpy", "scipy") + }, + }, + "model": model.audit(), + "quantum": quantum, + "exact": exact, + "milp": milp, + "uniform_one_hot": uniform, + "absolute_gap": gap, + "status": ( + "infeasible" + if exact["status"] == "infeasible" + or milp["status"] == "infeasible" + or quantum["status"] == "infeasible_propagation" + else "resource_limit" + if quantum["status"] == "resource_limit" + else "feasible" + if quantum_best and quantum_best["feasible"] + else "no_feasible_sample" + ), + "runtime_seconds": perf_counter() - started, + } diff --git a/test/LabScheduling/budget.test.py b/test/LabScheduling/budget.test.py new file mode 100644 index 00000000..241c4306 --- /dev/null +++ b/test/LabScheduling/budget.test.py @@ -0,0 +1,117 @@ +"""Budget conservation, sample provenance and fair-control regression checks.""" + +import importlib +import json +import sys +from pathlib import Path + +import pytest +from pyqpanda_alg.LabScheduling import compile_qubo, load_problem, reduce_model + +EXAMPLES = ( + Path(__file__).resolve().parents[2] / "pyqpanda-algorithm/example/LabScheduling" +) +sys.path.insert(0, str(EXAMPLES)) +try: + budget = importlib.import_module("matched_budget") + summary = importlib.import_module("summarize_matched") +finally: + sys.path.pop(0) + + +without_time = summary.without_timing + + +def test_allocation_conserves_budget_and_rejects_unfunded_components(): + for total in (8, 16, 60, 128, 512): + for count in (1, 2, 3): + parts = budget.allocate(total, count) + assert sum(parts) == total + assert max(parts) - min(parts) <= 1 + assert budget.allocate(512, 0) == [] + assert budget.allocate(512, 3) == [171, 171, 170] + for args in ((0, 1), (2, 3), (60, 11, 6), (True, 1), (8, -1)): + with pytest.raises(ValueError): + budget.allocate(*args) + + +def test_component_hit_probability_is_product_of_best_of_samples(): + assert budget.joint_hit([0.2, 0.4], 5) == pytest.approx((1 - 0.8**3) * (1 - 0.6**2)) + assert budget.joint_hit([], 8) == 1 + assert budget.joint_hit([0.0, 1.0], 8) == 0 + + +def test_single_unchanged_component_has_identical_controls_and_replays(): + model = compile_qubo(load_problem(EXAMPLES / "data/tradeoff.json")) + reduction = reduce_model(model) + first = budget.compare(model, reduction, 7) + second = budget.compare(model, reduction, 7) + assert without_time(first) == without_time(second) + paths = first["paths"] + assert without_time(paths["direct"]) == without_time( + paths["reduced"]["components"][0] + ) + assert without_time(paths["uniform_direct"]) == without_time( + paths["uniform_reduced"]["components"][0] + ) + assert first["exact_objective"] == 12 + + +def test_three_components_share_caps_and_all_selected_results_are_observed(): + model = compile_qubo(load_problem(EXAMPLES / "data/corpus-v2/n5-s0-r0.json")) + reduction = reduce_model(model) + assert len(reduction.components) == 3 + row = budget.compare(model, reduction, 7) + for name in ("reduced", "uniform_reduced"): + run = row["paths"][name] + assert run["shots"] == 512 + assert run["circuit_evaluations"] <= 120 + for component, allocation in zip( + run["components"], run["allocations"], strict=True + ): + assert sum(component["counts"].values()) == allocation["shots"] + key = "".join(map(str, component["best"]["bits"][::-1])) + assert component["counts"][key] > 0 + restored = reduction.restore([r["best"]["bits"] for r in run["components"]]) + assert restored == run["best"] + assert ( + sum( + a["maxiter_per_restart"] * 2 for a in row["paths"]["reduced"]["allocations"] + ) + == 120 + ) + limited = budget.components(reduction, 7, quantum=True, per_restart=10) + assert limited["status"] == "budget_insufficient" + assert limited["circuit_evaluations"] == limited["shots"] == 0 + + +def test_infeasible_domains_do_not_produce_fictitious_samples(): + corpus = EXAMPLES / "data/corpus-v2" + model = next( + m + for m in (compile_qubo(load_problem(p)) for p in sorted(corpus.glob("n*.json"))) + if any(not d for d in m.domains) + ) + row = budget.compare(model, reduce_model(model), 0) + assert row["exact_objective"] is None + assert row["conditional_hit_curve"] == [] + assert row["paths"]["uniform_direct"]["best"] is None + assert row["paths"]["uniform_direct"]["shots"] == 0 + assert row["paths"]["reduced"]["status"] == "infeasible_propagation" + + +def test_replay_ignores_only_timing_and_rejects_changed_samples(tmp_path): + original, replay = tmp_path / "original", tmp_path / "replay" + for directory, elapsed in ((original, 1), (replay, 2)): + directory.mkdir() + (directory / "provenance.json").write_text('{"seed": 7}') + (directory / "execution.json").write_text('{"paired_records": 1}') + (directory / "runs.jsonl").write_text( + json.dumps({"run": {"counts": {"01": 512}, "runtime_seconds": elapsed}}) + + "\n" + ) + summary.verify_replay(original, replay) + assert json.loads((original / "replay.json").read_text())["identical_except_timing"] + (replay / "runs.jsonl").write_text('{"run":{"counts":{"10":512}}}\n') + with pytest.raises(ValueError, match="beyond timing"): + summary.verify_replay(original, replay) diff --git a/test/LabScheduling/budget_report.test.py b/test/LabScheduling/budget_report.test.py new file mode 100644 index 00000000..24b1104a --- /dev/null +++ b/test/LabScheduling/budget_report.test.py @@ -0,0 +1,106 @@ +"""Keep quantum evidence when the independent raw MILP exceeds its budget.""" + +import json + +import pytest +from pyqpanda_alg.LabScheduling.__main__ import main +from pyqpanda_alg.LabScheduling.report import run_experiment + + +def _problem(raw_count, tasks=1, available=True): + raw = { + "name": "raw options versus surviving quantum candidates", + "horizon": 3 * tasks, + "resources": [{"id": "r"}], + "tasks": [ + { + "id": f"t{i}", + "group": f"g{i}", + "duration": 1, + "options": [ + {"resource": "r", "start": 3 * i + j, "cost": j} for j in range(2) + ], + } + for i in range(tasks) + ], + } + raw["tasks"][0]["options"].extend( + {"resource": "r", "start": 100 + i, "cost": 0} + for i in range(raw_count - 2 * tasks) + ) + if not available: + raw["resources"][0]["downtime"] = [[0, raw["horizon"]]] + return raw + + +@pytest.mark.parametrize("raw_count", [2048, 2049]) +def test_raw_candidate_boundary_retains_quantum_and_exact_certificate( + tmp_path, raw_count +): + path = tmp_path / "input.json" + path.write_text(json.dumps(_problem(raw_count)), encoding="utf-8") + result = run_experiment(path, shots=32, maxiter=6, restarts=1) + assert result["model"]["qubits"] == 2 + assert len(result["model"]["pruned"]) == raw_count - 2 + assert result["status"] == "feasible" + assert result["quantum"]["circuit_evaluations"] > 0 + assert result["quantum"]["business_metrics"]["feasible"] + assert result["quantum"]["best"]["objective"] == 0 + assert result["exact"]["best"]["objective"] == 0 + assert result["absolute_gap"] == 0 + if raw_count == 2048: + assert result["milp"]["status"] == "optimal" + assert result["milp"]["best"]["objective"] == 0 + else: + assert result["milp"]["status"] == "budget_exceeded" + assert result["milp"]["variables"] == 2049 + assert result["milp"]["max_variables"] == 2048 + assert result["milp"]["best"] is None + + +def test_no_exact_certificate_means_unknown_gap_not_zero(tmp_path, monkeypatch): + import pyqpanda_alg.LabScheduling.report as module + + def forbidden_call(*args, **kwargs): + raise AssertionError("raw MILP must not be constructed above its budget") + + monkeypatch.setattr(module, "solve_milp", forbidden_call) + path = tmp_path / "input.json" + path.write_text(json.dumps(_problem(2049, tasks=21)), encoding="utf-8") + result = run_experiment(path, reduce=True, shots=2, maxiter=6, restarts=1) + assert result["status"] == "feasible" + assert result["quantum"]["business_metrics"]["feasible"] + assert len(result["quantum"]["components"]) == 21 + assert all(c["circuit_evaluations"] > 0 for c in result["quantum"]["components"]) + assert result["exact"]["status"] == "budget_exceeded" + assert result["milp"]["status"] == "budget_exceeded" + assert all(result["milp"][key] is None for key in ("best", "dual_bound", "gap")) + assert result["absolute_gap"] is None + assert result["uniform_one_hot"]["feasible_probability"] is None + + +def test_empty_domain_remains_infeasible_when_milp_is_skipped(tmp_path): + path = tmp_path / "input.json" + path.write_text(json.dumps(_problem(2049, available=False)), encoding="utf-8") + result = run_experiment(path, reduce=True, shots=2, maxiter=6, restarts=1) + assert result["status"] == "infeasible" + assert result["exact"]["status"] == "infeasible" + assert result["milp"]["status"] == "budget_exceeded" + assert result["quantum"]["circuit_evaluations"] == 0 + assert result["absolute_gap"] is None + + +def test_cli_saves_report_instead_of_treating_budget_as_bad_input(tmp_path, capsys): + source = tmp_path / "input.json" + output = tmp_path / "report.json" + source.write_text(json.dumps(_problem(2049)), encoding="utf-8") + before = source.read_bytes() + status = main( + [str(source), "--maxiter", "6", "--restarts", "1", "--output", str(output)] + ) + assert status == 0 + result = json.loads(capsys.readouterr().out) + assert result == json.loads(output.read_text(encoding="utf-8")) + assert result["status"] == "feasible" + assert result["milp"]["status"] == "budget_exceeded" + assert source.read_bytes() == before diff --git a/test/LabScheduling/calendar_budget.test.py b/test/LabScheduling/calendar_budget.test.py new file mode 100644 index 00000000..da32664d --- /dev/null +++ b/test/LabScheduling/calendar_budget.test.py @@ -0,0 +1,164 @@ +"""Reject calendar-empty domains before quadratic MILP model construction.""" + +import importlib +import json +from pathlib import Path + +import pytest +from pyqpanda_alg.LabScheduling import Problem, solve_milp, validate_assignment + +milp_module = importlib.import_module("pyqpanda_alg.LabScheduling.milp") + + +def _problem(start: int, cleanup: int, downtime: list[list[int]]) -> Problem: + return Problem.from_dict( + { + "name": "calendar-domain", + "horizon": 6, + "resources": [{"id": "r", "cleanup": cleanup, "downtime": downtime}], + "tasks": [ + { + "id": "blocked", + "group": "g0", + "duration": 2, + "options": [{"resource": "r", "start": start, "cost": 0}], + }, + { + "id": "other", + "group": "g1", + "duration": 1, + "options": [{"resource": "r", "start": 0, "cost": 0}], + }, + ], + } + ) + + +@pytest.mark.parametrize( + "start, cleanup, downtime", + [(1, 0, [[2, 3]]), (4, 1, []), (0, 1, [[2, 3]])], + ids=["downtime-overlap", "cleanup-exceeds-horizon", "cleanup-overlaps-downtime"], +) +def test_calendar_infeasibility_skips_pairs_and_solver( + monkeypatch: pytest.MonkeyPatch, + start: int, + cleanup: int, + downtime: list[list[int]], +) -> None: + """A raw calendar witness needs neither pair rows nor a solver invocation.""" + problem = _problem(start, cleanup, downtime) + assert any( + row["kind"] == "calendar" and row["task"] == "blocked" + for row in validate_assignment(problem, [0, 0])["violations"] + ) + + def unnecessary(*args: object, **kwargs: object) -> None: + pytest.fail("calendar infeasibility must be checked before pairs and MILP") + + monkeypatch.setattr(milp_module, "_incompatible", unnecessary) + monkeypatch.setattr(milp_module, "milp", unnecessary) + result = solve_milp(problem) + assert result["status"] == "infeasible" + assert result["infeasible_task"] == "blocked" + assert result["best"] is None and result["dual_bound"] is None + assert result["gap"] is None and result["constraints"] == 0 + assert "solver_status" not in result + + +def test_touching_calendar_boundary_still_runs_real_milp() -> None: + """Half-open downtime and cleanup boundaries must not remove a valid choice.""" + problem = _problem(3, 1, [[2, 3]]) + result = solve_milp(problem) + assert result["status"] == "optimal" and result["solver_status"] == 0 + assert result["best"]["feasible"] and result["best"]["objective"] == 0 + + +def test_one_remaining_raw_option_preserves_actual_optimization() -> None: + """An unavailable cheap option must not hide a remaining feasible option.""" + problem = Problem.from_dict( + { + "name": "mixed-calendar", + "horizon": 6, + "resources": [{"id": "r", "downtime": [[0, 2]]}], + "tasks": [ + { + "id": "task", + "group": "group", + "duration": 1, + "options": [ + {"resource": "r", "start": 0, "cost": 0}, + {"resource": "r", "start": 2, "cost": 3}, + ], + } + ], + } + ) + result = solve_milp(problem) + assert result["status"] == "optimal" and result["solver_status"] == 0 + assert result["best"]["choices"] == [1] + assert result["best"]["objective"] == 3 + + +def test_cli_keeps_large_calendar_infeasibility_report( + tmp_path: Path, capsys: pytest.CaptureFixture[str] +) -> None: + """Many raw options with empty calendar domains still produce exit 3 and JSON.""" + from pyqpanda_alg.LabScheduling.__main__ import main + + data = { + "name": "large-calendar-empty", + "horizon": 2, + "resources": [{"id": "r", "downtime": [[0, 2]]}], + "tasks": [ + { + "id": f"t{i}", + "group": f"g{i}", + "duration": 1, + "options": [{"resource": "r", "start": 0, "cost": 0}], + } + for i in range(512) + ], + } + source, output = tmp_path / "input.json", tmp_path / "report.json" + source.write_text(json.dumps(data), encoding="utf-8") + assert main([str(source), "--output", str(output)]) == 3 + result = json.loads(output.read_text(encoding="utf-8")) + assert json.loads(capsys.readouterr().out) == result + assert result["status"] == "infeasible" + assert result["quantum"]["circuit_evaluations"] == 0 + assert result["milp"]["variables"] == 512 + assert result["milp"]["constraints"] == 0 + assert result["milp"]["infeasible_task"] == "t0" + assert result["absolute_gap"] is None + + +@pytest.mark.parametrize("blocked_start", [0, 1]) +def test_fixed_zero_candidates_need_no_pair_rows(blocked_start: int) -> None: + """Retain raw indices and the optimum while omitting bound-redundant rows.""" + problem = Problem.from_dict( + { + "name": "fixed-zero-pairs", + "horizon": 6, + "resources": [{"id": "r", "downtime": [[0, 2]]}], + "tasks": [ + { + "id": f"t{i}", + "group": f"g{i}", + "duration": 2, + "options": [ + {"resource": "r", "start": blocked_start, "cost": 0}, + {"resource": "r", "start": 2, "cost": 3 if i == 0 else 1}, + {"resource": "r", "start": 4, "cost": 1 if i == 0 else 3}, + ], + } + for i in range(2) + ], + } + ) + result = solve_milp(problem) + assert result["status"] == "optimal" and result["variables"] == 6 + assert result["best"]["choices"] == [2, 1] + assert result["best"]["objective"] == 2 + assert validate_assignment(problem, [2, 1])["feasible"] + # Two exact-one rows and two incompatible pairs of available options. + assert result["constraints"] == 4 diff --git a/test/LabScheduling/cli.test.py b/test/LabScheduling/cli.test.py new file mode 100644 index 00000000..9dbcaad4 --- /dev/null +++ b/test/LabScheduling/cli.test.py @@ -0,0 +1,90 @@ +"""CLI contract: reports, exit status, actual samples and no secret configuration.""" + +import json +import subprocess +import sys +from pathlib import Path + +import pytest +from pyqpanda_alg.LabScheduling.__main__ import main + +DATA = ( + Path(__file__).resolve().parents[2] + / "pyqpanda-algorithm/example/LabScheduling/data" +) + + +def test_one_command_cli_and_saved_report(tmp_path): + output = tmp_path / "nested" / "report.json" + process = subprocess.run( + [ + sys.executable, + "-m", + "pyqpanda_alg.LabScheduling", + str(DATA / "simple.json"), + "--maxiter", + "25", + "--restarts", + "1", + "--output", + str(output), + ], + capture_output=True, + text=True, + timeout=30, + check=True, + ) + report = json.loads(process.stdout) + assert report == json.loads(output.read_text(encoding="utf-8")) + assert report["status"] == "feasible" + assert report["absolute_gap"] == 0 + assert ( + report["quantum"]["best"]["objective"] + == report["exact"]["best"]["objective"] + == 5 + ) + assert len(report["input_sha256"]) == 64 + assert report["environment"]["pyqpanda3"] + assert report["uniform_one_hot"]["feasible_probability"] == pytest.approx(4 / 9) + + +def test_infeasible_exit_and_report(capsys): + status = main([str(DATA / "infeasible.json"), "--maxiter", "6", "--restarts", "1"]) + assert status == 3 + report = json.loads(capsys.readouterr().out) + assert report["status"] == "infeasible" + assert report["exact"]["best"] is None + assert report["quantum"]["status"] == "no_feasible_sample" + assert report["absolute_gap"] is None + + +def test_missing_or_invalid_input_exit(tmp_path, capsys): + for path in [tmp_path / "missing.json", tmp_path / "bad.json"]: + if path.name == "bad.json": + path.write_text('{"name": "only"}', encoding="utf-8") + with pytest.raises(SystemExit) as exc: + main([str(path)]) + assert exc.value.code == 2 + assert "error:" in capsys.readouterr().err + + +def test_finite_shots_miss_is_distinct_from_infeasibility(capsys): + status = main( + [ + str(DATA / "simple.json"), + "--shots", + "1", + "--mixer", + "x", + "--maxiter", + "6", + "--restarts", + "1", + ] + ) + report = json.loads(capsys.readouterr().out) + assert status == 4 + assert report["status"] == "no_feasible_sample" + assert report["exact"]["status"] == "optimal" + assert report["quantum"]["best"]["objective"] is None + assert report["absolute_gap"] is None diff --git a/test/LabScheduling/enhancement.test.py b/test/LabScheduling/enhancement.test.py new file mode 100644 index 00000000..89be933d --- /dev/null +++ b/test/LabScheduling/enhancement.test.py @@ -0,0 +1,429 @@ +"""Lossless reduction, independent raw MILP and real circuit equivalence.""" + +from dataclasses import replace +from itertools import product +from pathlib import Path +from random import Random +from types import SimpleNamespace + +import numpy as np +import pytest +from pyqpanda_alg.LabScheduling import ( + Problem, + circuit_probabilities, + compile_qubo, + evaluate, + load_problem, + reduce_model, + solve_exact, + solve_milp, + solve_reduced, + validate_assignment, +) +from pyqpanda_alg.LabScheduling.metrics import ( + circuit_resources, + distribution_metrics, + hit_probability, +) + +DATA = ( + Path(__file__).resolve().parents[2] + / "pyqpanda-algorithm/example/LabScheduling/data" +) + + +def random_problem(seed): + rng = Random(seed) + tasks = [] + for ti in range(rng.randint(0, 5)): + assignments = rng.sample( + [(r, s) for r in ("r0", "r1") for s in range(6)], + rng.randint(0 if seed % 9 == 0 else 1, 3), + ) + tasks.append( + { + "id": f"t{ti}", + "group": f"g{rng.randrange(2)}", + "duration": rng.randint(1, 2), + "change_cost": rng.randrange(4), + "original": {"resource": "r0", "start": ti}, + "options": [ + {"resource": r, "start": s, "cost": rng.randrange(5)} + for r, s in assignments + ], + } + ) + edges = [ + [a["id"], b["id"]] + for a in tasks + for b in tasks + if a != b and rng.random() < 0.07 + ] + return Problem.from_dict( + { + "name": f"case-{seed}", + "horizon": 7, + "resources": [ + {"id": "r0", "cleanup": seed % 2, "downtime": [[0, 1]]}, + {"id": "r1"}, + ], + "tasks": tasks, + "precedence": edges, + } + ) + + +def feasible_vectors(model): + result = {} + for choice in product(*model.domains): + bits = [int(i in choice) for i in range(len(model.variables))] + row = evaluate(model, bits) + if row["feasible"]: + result[tuple(bits)] = row["objective"] + return result + + +def test_one_hundred_instances_preserve_every_feasible_schedule_and_raw_milp_optimum(): + for seed in range(100): + problem = random_problem(seed) + model = compile_qubo(problem) + original = feasible_vectors(model) + reduced = reduce_model(model) + restored = {} + if reduced.infeasible_task is None: + options = [list(feasible_vectors(m)) for m in reduced.components] + for choices in product(*options): + row = reduced.restore(choices) + assert row["feasible"], seed + restored[tuple(row["bits"])] = row["objective"] + local_cost = sum( + evaluate(m, b)["objective"] + for m, b in zip(reduced.components, choices, strict=True) + ) + assert row["objective"] == local_cost + reduced.audit()["fixed_cost"] + assert restored == original, seed + milp = solve_milp(problem) + assert milp["status"] == ("optimal" if original else "infeasible"), seed + if original: + assert milp["best"]["objective"] == min(original.values()), seed + + +@pytest.mark.parametrize("dataset", ["simple", "medium"]) +def test_full_and_reduced_xy_phase_match_for_thirty_real_cpu_parameter_sets(dataset): + model = compile_qubo(load_problem(DATA / f"{dataset}.json")) + rng = np.random.default_rng(721) + for layers in (1, 2, 3): + for _ in range(10): + parameters = rng.uniform(-3, 3, 2 * layers).tolist() + assert circuit_probabilities(model, parameters) == pytest.approx( + circuit_probabilities(model, parameters, phase_mode="full"), abs=1e-10 + ) + resources = circuit_resources(model, [0.3, 0.7]) + assert resources["auto"]["cnot"] < resources["full"]["cnot"] + assert resources["auto"]["depth"] < resources["full"]["depth"] + assert ( + circuit_resources(model, [0.3, 0.7], "x")["auto"] + == circuit_resources(model, [0.3, 0.7], "x")["full"] + ) + + +def test_mixed_singleton_domains_and_x_phase_unmodified(): + p = load_problem(DATA / "simple.json") + p = replace( + p, tasks=(replace(p.tasks[0], options=(p.tasks[0].options[-1],)), p.tasks[1]) + ) + model = compile_qubo(p) + for mixer in ("xy", "x"): + assert circuit_probabilities( + model, [0.3, -0.9, 1.1, 0.7], mixer + ) == pytest.approx( + circuit_probabilities( + model, [0.3, -0.9, 1.1, 0.7], mixer, phase_mode="full" + ), + abs=1e-10, + ) + with pytest.raises(ValueError, match="phase_mode"): + circuit_probabilities(model, [0.3, 0.7], phase_mode="bad") + from pyqpanda_alg.LabScheduling import solve_qaoa + + with pytest.raises(ValueError, match="phase_mode"): + solve_qaoa(model, phase_mode="bad") + + +def chain_problem(): + return Problem.from_dict( + { + "name": "chain", + "horizon": 5, + "resources": [{"id": "r", "downtime": [[0, 1]]}], + "tasks": [ + { + "id": f"t{i}", + "group": f"g{i}", + "duration": 1, + "original": {"resource": "r", "start": i}, + "change_cost": i + 1, + "options": [ + {"resource": "r", "start": s, "cost": 0} for s in (i, i + 1) + ], + } + for i in range(3) + ], + } + ) + + +def test_outage_cascades_through_unaffected_bookings_and_restores_cost(): + model = compile_qubo(chain_problem()) + reduced = reduce_model(model) + assert len(reduced.fixed) == 3 + assert len(reduced.removals) == 2 + assert reduced.components == () + assert reduced.restore([])["objective"] == 6 + result = solve_reduced(model) + assert result["status"] == "classical_propagation" + assert result["circuit_evaluations"] == 0 + assert result["best"]["objective"] == 6 + assert all(row["changed"] for row in result["best"]["schedule"]) + with pytest.raises(ValueError, match="per component"): + reduced.restore([[0]]) + with pytest.raises(ValueError, match="seed"): + solve_reduced(model, seed=-1) + + +def test_reduction_infeasible_and_actual_component_sampling(): + infeasible = compile_qubo(load_problem(DATA / "infeasible.json")) + assert solve_reduced(infeasible)["status"] == "infeasible_propagation" + with pytest.raises(ValueError, match="infeasible"): + reduce_model(infeasible).restore([]) + model = compile_qubo(load_problem(DATA / "simple.json")) + result = solve_reduced(model, maxiter=20, restarts=1) + assert result["status"] == "feasible" + assert result["best"]["objective"] >= solve_exact(model)["best"]["objective"] + for run in result["components"]: + key = "".join(map(str, run["best"]["bits"][::-1])) + assert run["counts"][key] > 0 + + +def test_disconnected_components_respect_largest_component_budget(): + p = chain_problem() + tasks = tuple( + replace( + p.tasks[1], + id=f"t{i}", + group=f"g{i}", + options=tuple( + replace(o, start=3 * i + j + 1) + for j, o in enumerate(p.tasks[1].options) + ), + ) + for i in range(9) + ) + model = compile_qubo(replace(p, horizon=40, tasks=tasks)) + reduced = reduce_model(model) + assert reduced.audit()["original_qubits"] == 18 + assert reduced.audit()["component_qubits"] == [2] * 9 + result = solve_reduced(model, maxiter=6, restarts=1) + assert result["best"]["feasible"] + assert result["total_shots"] == 512 * 9 + task = replace( + tasks[0], + options=tuple(replace(tasks[0].options[0], start=i + 1) for i in range(17)), + ) + big = compile_qubo(replace(p, horizon=40, tasks=(task,))) + assert solve_reduced(big)["status"] == "resource_limit" + + +def test_probability_diagnostics_ties_infeasibility_and_validation(): + p = chain_problem() + model = compile_qubo(replace(p, tasks=(), precedence=())) + metrics = distribution_metrics(model, np.array([1.0])) + assert metrics["optimal_count"] == 1 + assert metrics["shots_for_95_percent"] == 1 + model = compile_qubo(load_problem(DATA / "infeasible.json")) + metrics = distribution_metrics(model, circuit_probabilities(model, [0.0, 0.0])) + assert metrics["optimal_probability"] == 0 + assert metrics["shots_for_95_percent"] is None + model = compile_qubo(load_problem(DATA / "simple.json")) + metrics = distribution_metrics(model, circuit_probabilities(model, [0.0, 0.0])) + assert metrics["optimal_probability"] == pytest.approx(1 / 9) + assert metrics["uniform_optimal_probability"] == pytest.approx(1 / 9) + assert hit_probability(1 / 81, 512) == pytest.approx(0.998271204298638) + for p in (-1, 2, float("nan")): + with pytest.raises(ValueError): + hit_probability(p, 8) + with pytest.raises(ValueError): + hit_probability(0.2, 0) + for array in (np.array([1.0]), np.full(64, -1), np.full(64, np.nan), np.ones(64)): + with pytest.raises(ValueError): + distribution_metrics(model, array) + + +def test_raw_validator_catches_unary_error_and_choice_shape(): + problem = chain_problem() + assert not validate_assignment(problem, [0, 0, 0])["feasible"] + assert not validate_assignment(problem, [1, 0, 0])["feasible"] + assert validate_assignment(problem, [1, 1, 1])["changed_tasks"] == 3 + for choices in ([], [99, 0, 0], [True, 0, 0]): + with pytest.raises(ValueError): + validate_assignment(problem, choices) + for limit in (0, -1, True, float("nan")): + with pytest.raises(ValueError): + solve_milp(problem, time_limit=limit) + + +def test_milp_limit_keeps_status_separate_from_optimality(monkeypatch): + import importlib + + module = importlib.import_module("pyqpanda_alg.LabScheduling.milp") + monkeypatch.setattr( + module, + "milp", + lambda *a, **kw: SimpleNamespace( + status=1, + x=None, + message="time limit", + mip_dual_bound=0, + mip_gap=None, + ), + ) + result = solve_milp(chain_problem()) + assert result["status"] == "limit" + assert result["best"] is None and result["dual_bound"] == 0 + assert result["gap"] is None + + +def test_tied_optima_and_greedy_dead_end(): + from pyqpanda_alg.LabScheduling.classical import solve_greedy + + p = chain_problem() + task = replace(p.tasks[1], original=None, change_cost=0) + model = compile_qubo(replace(p, tasks=(task,))) + metrics = distribution_metrics(model, np.array([0, 0.5, 0.5, 0])) + assert metrics["optimal_count"] == 2 + assert metrics["optimal_probability"] == 1 + assert solve_greedy(model)["best"]["objective"] == 0 + infeasible = compile_qubo(load_problem(DATA / "infeasible.json")) + assert solve_greedy(infeasible)["status"] == "dead_end" + + +def test_booking_csv_expands_windows_costs_and_preserves_invalid_candidates(tmp_path): + from pyqpanda_alg.LabScheduling.bookings import COLUMNS, import_bookings + + path = tmp_path / "bookings.csv" + header = ",".join(COLUMNS) + "\n" + path.write_text(header + "a,g,1,r,0,r,0,2,1,2,3\n") + calendar = { + "name": "csv", + "horizon": 3, + "resources": [{"id": "r", "downtime": [[0, 1]]}], + } + raw = import_bookings(path, calendar) + assert [o["cost"] for o in raw["tasks"][0]["options"]] == [0, 3, 6] + assert len(compile_qubo(Problem.from_dict(raw)).pruned) == 1 + invalid = [ + ("bad-header\n", "headers"), + (header + "a,g\n", "missing"), + (header + "a,g,1,r,0,r,0,2,1,2,3,extra\n", "extra"), + (header + "a,g,1,r,0,r,0,2,0,2,3\n", "step"), + (header + "a,g,1,r,0,r,3,2,1,2,3\n", "earliest"), + (header + "a,g,1,r,0,r,0,2,1,-2,3\n", "integer"), + (header + "a,g,1,r,0,r,0,2,1,1000001,3\n", "1000000"), + (header + "a,g,1,r,0,r,0,256,1,2,3\n", "256"), + (header + "a,g,1,r,0,r|r,0,2,1,2,3\n", "duplicate"), + ] + for content, match in invalid: + path.write_text(content) + with pytest.raises(ValueError, match=match): + import_bookings(path, calendar) + with pytest.raises(ValueError, match="calendar"): + import_bookings(path, {"tasks": []}) + + +def test_reduced_report_and_independent_business_audit(tmp_path): + import json + + from pyqpanda_alg.LabScheduling.bookings import import_bookings + from pyqpanda_alg.LabScheduling.report import run_experiment + + calendar = json.loads((DATA / "bookings/outage.json").read_text()) + raw = import_bookings(DATA / "bookings/chain.csv", calendar) + path = tmp_path / "chain.json" + path.write_text(json.dumps(raw)) + report = run_experiment(path, reduce=True, maxiter=20, restarts=1) + assert report["status"] == "feasible" + assert report["quantum"]["business_metrics"]["changed_tasks"] == 3 + assert report["milp"]["best"]["objective"] == 6 + assert report["quantum"]["reduction"]["max_component_qubits"] == 4 + + +def test_large_exact_budget_is_reported_and_milp_certifies(tmp_path): + import json + + from pyqpanda_alg.LabScheduling.report import run_experiment + + raw = { + "name": "20 independent bookings", + "horizon": 61, + "resources": [{"id": "r"}], + "tasks": [ + { + "id": f"t{i}", + "group": f"g{i}", + "duration": 1, + "options": [ + {"resource": "r", "start": 3 * i + j, "cost": j} for j in range(2) + ], + } + for i in range(20) + ], + } + path = tmp_path / "large.json" + path.write_text(json.dumps(raw)) + report = run_experiment(path, reduce=True, shots=2, maxiter=6, restarts=1) + assert report["exact"]["status"] == "budget_exceeded" + assert report["milp"]["status"] == "optimal" + assert report["uniform_one_hot"]["feasible_probability"] is None + assert report["status"] == "feasible" + + +def test_milp_resource_budget_and_rejects_corrupted_incumbents(monkeypatch): + import importlib + + module = importlib.import_module("pyqpanda_alg.LabScheduling.milp") + p = chain_problem() + one = replace(p, tasks=(replace(p.tasks[1], options=(p.tasks[1].options[0],)),)) + oversized = replace( + one, tasks=tuple(replace(one.tasks[0], id=f"t{i}") for i in range(2049)) + ) + with pytest.raises(ValueError, match="2048"): + solve_milp(oversized) + for x, value, message in ( + (np.array([0.5]), 1, "integral"), + (np.array([0]), 0, "exact-one"), + (np.array([1]), 999, "semantic"), + (None, None, "without an incumbent"), + ): + monkeypatch.setattr( + module, + "milp", + lambda *a, x=x, value=value, **kw: SimpleNamespace( + status=0, x=x, fun=value, message="test" + ), + ) + with pytest.raises(RuntimeError, match=message): + solve_milp(one) + monkeypatch.setattr( + module, + "milp", + lambda *a, **kw: SimpleNamespace( + status=1, + x=np.array([1]), + fun=0, + message="limit with incumbent", + mip_dual_bound=0, + mip_gap=0, + ), + ) + result = solve_milp(one) + assert result["status"] == "limit" and result["best"]["feasible"] diff --git a/test/LabScheduling/evidence.test.py b/test/LabScheduling/evidence.test.py new file mode 100644 index 00000000..4877deb9 --- /dev/null +++ b/test/LabScheduling/evidence.test.py @@ -0,0 +1,110 @@ +"""Independent business and corpus invariants for the second evaluation.""" + +import hashlib +import importlib.util +import json +from dataclasses import replace +from pathlib import Path + +import pytest +from pyqpanda_alg.LabScheduling import ( + compile_qubo, + evaluate, + load_problem, + reduce_model, + solve_milp, + solve_reduced, + validate_assignment, +) + +EXAMPLES = ( + Path(__file__).resolve().parents[2] / "pyqpanda-algorithm/example/LabScheduling" +) +spec = importlib.util.spec_from_file_location("ablation", EXAMPLES / "ablation.py") +ablation = importlib.util.module_from_spec(spec) +spec.loader.exec_module(ablation) + + +def test_frozen_heterogeneous_inputs_preserve_controls_and_all_feasible_schedules(): + corpus = EXAMPLES / "data/corpus-v2" + manifest = json.loads((corpus / "manifest.json").read_text()) + assert len(manifest["instances"]) == 36 + assert ( + hashlib.sha256((EXAMPLES / "make_corpus_v2.py").read_bytes()).hexdigest() + == manifest["generator_sha256"] + ) + durations, cleanups, domains = set(), set(), set() + for record in manifest["instances"]: + path = corpus / record["file"] + assert hashlib.sha256(path.read_bytes()).hexdigest() == record["sha256"] + problem = load_problem(path) + control = replace( + problem, resources=tuple(replace(r, downtime=()) for r in problem.resources) + ) + witness = validate_assignment(control, [0] * len(problem.tasks)) + assert witness["feasible"] and witness["objective"] == 0 + stages = ablation.audit_stages(problem) + oracle = solve_milp(problem) + assert (oracle["status"] == "optimal") == bool(stages["feasible_count"]) + if oracle["best"]: + assert oracle["best"]["objective"] == stages["exact_objective"] + durations.update(t.duration for t in problem.tasks) + cleanups.update(r.cleanup for r in problem.resources) + domains.update(len(t.options) for t in problem.tasks) + assert durations == {1, 2, 3} + assert cleanups == {0, 1, 2} + assert domains == {2, 3, 4} + + +def test_tradeoff_retains_all_three_decisions_and_nine_feasible_alternatives(): + model = compile_qubo(load_problem(EXAMPLES / "data/tradeoff.json")) + reduction = reduce_model(model) + assert reduction.fixed == () + assert [len(m.variables) for m in reduction.components] == [8] + choices = [evaluate(model, bits) for bits in ablation.feasible_vectors(model)] + assert len(choices) == 9 + assert min(row["objective"] for row in choices) == 12 + optimum = next(row for row in choices if row["objective"] == 12) + assert sum(row["changed"] for row in optimum["schedule"]) == 3 + backup = min( + ( + row + for row in choices + if any(r["resource"] == "backup" for r in row["schedule"]) + ), + key=lambda r: r["objective"], + ) + assert backup["objective"] == 14 + assert sum(row["changed"] for row in backup["schedule"]) == 1 + stages = ablation.audit_stages(model.problem)["stages"] + assert stages["raw_input"]["candidate_count"] == 9 + assert ( + stages["components_auto_phase"]["cnot"] + < stages["components_full_phase"]["cnot"] + ) + + +@pytest.mark.parametrize("seed", [0, 7, 19]) +def test_tradeoff_quantum_choices_are_actual_samples_and_raw_valid(seed): + model = compile_qubo(load_problem(EXAMPLES / "data/tradeoff.json")) + result = solve_reduced(model, seed=seed) + component = result["components"][0] + assert component["counts"]["".join(map(str, component["best"]["bits"][::-1]))] > 0 + assert result["circuit_evaluations"] > 0 + assert result["best"]["feasible"] + assert result["best"]["objective"] >= 12 + choices = [ + task.options.index( + next( + variable.option + for variable, bit in zip( + model.variables, result["best"]["bits"], strict=True + ) + if bit and variable.task == ti + ) + ) + for ti, task in enumerate(model.problem.tasks) + ] + raw = validate_assignment(model.problem, choices) + assert raw["feasible"] + assert raw["objective"] == result["best"]["objective"] diff --git a/test/LabScheduling/io.test.py b/test/LabScheduling/io.test.py new file mode 100644 index 00000000..5a0d086d --- /dev/null +++ b/test/LabScheduling/io.test.py @@ -0,0 +1,128 @@ +"""Input snapshot provenance and non-destructive report publication regressions.""" + +import hashlib +import json +from pathlib import Path + +import pyqpanda_alg.LabScheduling.__main__ as cli +import pyqpanda_alg.LabScheduling.report as reports +import pytest +from pyqpanda_alg.LabScheduling import load_problem, parse_problem + +DATA = ( + Path(__file__).resolve().parents[2] + / "pyqpanda-algorithm/example/LabScheduling/data" +) + + +@pytest.mark.parametrize("alias", ["same_path", "symlink", "hardlink", "directory"]) +def test_output_aliases_rejected_before_training_without_changing_input( + tmp_path, monkeypatch, capsys, alias +): + source = tmp_path / "input.json" + original = (DATA / "simple.json").read_bytes() + source.write_bytes(original) + output = source if alias == "same_path" else tmp_path / "output.json" + if alias == "symlink": + output.symlink_to(source) + elif alias == "hardlink": + output.hardlink_to(source) + elif alias == "directory": + output.mkdir() + + def forbidden(*args, **kwargs): + pytest.fail("invalid output must be rejected before running the simulator") + + monkeypatch.setattr(cli, "run_experiment", forbidden) + with pytest.raises(SystemExit) as error: + cli.main([str(source), "--output", str(output)]) + assert error.value.code == 2 + assert "output" in capsys.readouterr().err + assert source.read_bytes() == original + + +@pytest.mark.parametrize("mutation", ["modify", "delete"]) +def test_report_hash_describes_the_solved_snapshot_even_if_input_changes( + tmp_path, monkeypatch, mutation +): + source = tmp_path / "input.json" + original = (DATA / "simple.json").read_bytes().replace(b"\n", b"\r\n") + source.write_bytes(original) + real_solver = reports.solve_qaoa + + def solve_then_change_file(*args, **kwargs): + result = real_solver(*args, **kwargs) + if mutation == "modify": + changed = json.loads(original) + changed["name"] = "modified after loading" + source.write_text(json.dumps(changed), encoding="utf-8") + else: + source.unlink() + return result + + monkeypatch.setattr(reports, "solve_qaoa", solve_then_change_file) + report = reports.run_experiment(source, shots=2, maxiter=6, restarts=1) + assert report["problem"] == json.loads(original)["name"] + assert report["input_sha256"] == hashlib.sha256(original).hexdigest() + assert report["exact"]["best"]["objective"] == 5 + + +@pytest.mark.parametrize("failure", ["write", "replace"]) +def test_failed_report_write_preserves_previous_report_and_removes_temporary( + tmp_path, monkeypatch, capsys, failure +): + source = tmp_path / "input.json" + source.write_text("input must survive", encoding="utf-8") + output = tmp_path / "report.json" + output.write_text("previous complete report", encoding="utf-8") + monkeypatch.setattr(cli, "run_experiment", lambda *a, **k: {"status": "feasible"}) + if failure == "replace": + + def reject_replace(*args, **kwargs): + raise OSError("simulated replacement failure") + + monkeypatch.setattr(Path, "replace", reject_replace) + else: + real_temporary = cli.NamedTemporaryFile + + def failing_temporary(*args, **kwargs): + stream = real_temporary(*args, **kwargs) + write = stream.write + + def partial_write(text): + write(text[:5]) + raise OSError("simulated full filesystem during write") + + stream.write = partial_write + return stream + + monkeypatch.setattr(cli, "NamedTemporaryFile", failing_temporary) + with pytest.raises(SystemExit) as error: + cli.main([str(source), "--output", str(output)]) + assert error.value.code == 2 + assert "simulated" in capsys.readouterr().err + assert source.read_text() == "input must survive" + assert output.read_text() == "previous complete report" + assert sorted(p.name for p in tmp_path.iterdir()) == ["input.json", "report.json"] + + +def test_atomic_report_replacement_preserves_a_valid_output_symlink(tmp_path): + source = tmp_path / "input.json" + source.write_text("original input", encoding="utf-8") + target = tmp_path / "report.json" + target.write_text("old report", encoding="utf-8") + link = tmp_path / "report-link.json" + link.symlink_to(target) + cli._save_report(source, link, '{"status": "feasible"}') + assert link.is_symlink() + assert target.read_text() == '{"status": "feasible"}\n' + assert source.read_text() == "original input" + assert len(list(tmp_path.iterdir())) == 3 + + +def test_text_parser_preserves_file_loader_validation(): + text = (DATA / "simple.json").read_text(encoding="utf-8") + assert parse_problem(text) == load_problem(DATA / "simple.json") + for invalid in ('{"name":"a","name":"b"}', '{"horizon":NaN}', "[]"): + with pytest.raises(ValueError): + parse_problem(invalid) diff --git a/test/LabScheduling/model.test.py b/test/LabScheduling/model.test.py new file mode 100644 index 00000000..db04890b --- /dev/null +++ b/test/LabScheduling/model.test.py @@ -0,0 +1,206 @@ +"""Exhaustive semantic/QUBO equality and input-boundary regression checks.""" + +import copy +import json +from itertools import product +from pathlib import Path + +import pytest +from pyqpanda_alg.LabScheduling import ( + Problem, + compile_qubo, + evaluate, + load_problem, + solve_exact, +) + +DATA = ( + Path(__file__).resolve().parents[2] + / "pyqpanda-algorithm/example/LabScheduling/data" +) + + +def fixture_data(): + return json.loads((DATA / "simple.json").read_text(encoding="utf-8")) + + +def test_all_binary_energies_equal_independent_constraint_formula(): + model = compile_qubo(load_problem(DATA / "simple.json")) + feasible_energies, infeasible_energies = [], [] + for bits in product((0, 1), repeat=len(model.variables)): + checked = evaluate(model, bits) + assignment_penalty = sum( + (sum(bits[i] for i in domain) - 1) ** 2 for domain in model.domains + ) + pair_count = sum(v["kind"] == "conflict" for v in checked["violations"]) + assert model.energy(bits) == checked["raw_cost"] + model.penalty * ( + assignment_penalty + pair_count + ) + (feasible_energies if checked["feasible"] else infeasible_energies).append( + model.energy(bits) + ) + assert min(infeasible_energies) > max(feasible_energies) + exact = solve_exact(model) + assert exact["best"]["objective"] == min(feasible_energies) == 5 + assert exact["feasible_count"] == 4 + + +def test_medium_all_4096_states_and_exact_optimum(): + model = compile_qubo(load_problem(DATA / "medium.json")) + assert len(model.variables) == 12 + assert len(model.pruned) == 2 + for bits in product((0, 1), repeat=12): + checked = evaluate(model, bits) + one_hot = sum( + (sum(bits[i] for i in domain) - 1) ** 2 for domain in model.domains + ) + pairs = sum(v["kind"] == "conflict" for v in checked["violations"]) + assert model.energy(bits) == checked["raw_cost"] + model.penalty * ( + one_hot + pairs + ) + exact = solve_exact(model) + assert exact["best"]["objective"] == 4 + assert exact["best"]["violation_count"] == 0 + + +def test_cleanup_downtime_horizon_and_touching_intervals(): + data = fixture_data() + # At start=1, occupation [1,4) includes cleanup and touches downtime [0,1). + data["tasks"] = [data["tasks"][0]] + data["tasks"][0]["options"].append( + {"resource": "spectrometer", "start": 7, "cost": 0} + ) + model = compile_qubo(Problem.from_dict(data)) + assert [v.option.start for v in model.variables] == [1, 3, 6] + assert [row["reasons"] for row in model.pruned] == [["downtime"], ["horizon"]] + data["resources"][0]["downtime"] = [[3, 4]] + model = compile_qubo(Problem.from_dict(data)) + assert 1 not in [v.option.start for v in model.variables] # cleanup also blocked + + +def test_pair_multiple_reasons_penalized_once_and_precedence_end_boundary(): + data = fixture_data() + for task in data["tasks"]: + task["group"] = "same" + data["precedence"] = [["sample-A", "sample-B"]] + model = compile_qubo(Problem.from_dict(data)) + bits = [1, 0, 0, 1, 0, 0] + result = evaluate(model, bits) + assert result["violation_count"] == 1 + assert result["violations"][0]["reasons"] == ["resource", "group", "precedence"] + assert model.energy(bits) == result["raw_cost"] + model.penalty + # Separate resources: group/precedence allow immediate start at predecessor end. + data["resources"].append({"id": "other"}) + data["tasks"][1]["options"] = [{"resource": "other", "start": 3, "cost": 0}] + model = compile_qubo(Problem.from_dict(data)) + assert evaluate(model, [1, 0, 0, 1])["feasible"] + + +def test_valid_but_infeasible_and_empty_domain_and_cycle(): + model = compile_qubo(load_problem(DATA / "infeasible.json")) + assert solve_exact(model)["status"] == "infeasible" + data = fixture_data() + data["tasks"][0]["options"] = [] + model = compile_qubo(Problem.from_dict(data)) + assert solve_exact(model)["combinations"] == 0 + assert solve_exact(model)["best"] is None + data = fixture_data() + data["precedence"] = [["sample-A", "sample-B"], ["sample-B", "sample-A"]] + assert solve_exact(compile_qubo(Problem.from_dict(data)))["status"] == "infeasible" + + +def test_empty_problem_and_singleton_zero_cost(): + data = {"name": "empty", "horizon": 1, "resources": [{"id": "r"}], "tasks": []} + model = compile_qubo(Problem.from_dict(data)) + assert model.energy([]) == 0 + assert solve_exact(model)["best"]["objective"] == 0 + data["tasks"] = [ + { + "id": "t", + "group": "g", + "duration": 1, + "options": [{"resource": "r", "start": 0, "cost": 0}], + } + ] + model = compile_qubo(Problem.from_dict(data)) + assert model.penalty == 1 + assert model.energy([1]) == 0 + assert model.energy([0]) == 1 + + +def test_limits_penalty_and_decode_errors(): + model = compile_qubo(load_problem(DATA / "simple.json")) + for penalty in [model.objective_bound, 0, -1, True, 2.5]: + with pytest.raises(ValueError, match="penalty"): + compile_qubo(model.problem, penalty=penalty) + for bits in [[], [0] * 7, [2] * 6]: + with pytest.raises(ValueError, match="binary"): + evaluate(model, bits) + with pytest.raises(ValueError, match="limit"): + solve_exact(model, max_combinations=1) + with pytest.raises(ValueError): + solve_exact(model, max_combinations=False) + assert compile_qubo(model.problem, penalty=20).penalty == 20 + + +@pytest.mark.parametrize( + "mutation", + [ + lambda d: d.update(horizon=True), + lambda d: d.update(horizon=0), + lambda d: d.update(typo=1), + lambda d: d.update(resources=[]), + lambda d: d.update(tasks="bad"), + lambda d: d["tasks"][0].update(duration=-1), + lambda d: d["tasks"][0].update(duration=1.5), + lambda d: d["tasks"][0].update(group=" "), + lambda d: d["tasks"][0]["options"][0].update(cost=-1), + lambda d: d["tasks"][0]["options"][0].update(cost=float("nan")), + lambda d: d["tasks"][0]["options"][0].update(resource="missing"), + lambda d: d["tasks"][0]["options"].append( + copy.deepcopy(d["tasks"][0]["options"][0]) + ), + lambda d: d["tasks"].append(copy.deepcopy(d["tasks"][0])), + lambda d: d["resources"].append(copy.deepcopy(d["resources"][0])), + lambda d: d["resources"][0].update(downtime=[[2, 2]]), + lambda d: d["resources"][0].update(downtime=[[0, 20]]), + lambda d: d["resources"][0].update(downtime=[[1]]), + lambda d: d.update(precedence=[["missing", "sample-B"]]), + lambda d: d.update(precedence=[["sample-A", "sample-A"]]), + lambda d: d.update(precedence=[["sample-A"]]), + lambda d: d["tasks"][0].pop("original"), + ], +) +def test_invalid_input(mutation): + data = fixture_data() + mutation(data) + with pytest.raises(ValueError): + Problem.from_dict(data) + + +@pytest.mark.parametrize("text", ['{"name":"a","name":"b"}', '{"name":NaN}', "{bad"]) +def test_invalid_json(tmp_path, text): + path = tmp_path / "bad.json" + path.write_text(text, encoding="utf-8") + with pytest.raises(ValueError): + load_problem(path) + + +def test_model_compilation_rejects_excessive_candidates_before_pair_expansion(): + data = { + "name": "large", + "horizon": 300, + "resources": [{"id": "r"}], + "tasks": [ + { + "id": "t", + "group": "g", + "duration": 1, + "options": [ + {"resource": "r", "start": i, "cost": 0} for i in range(257) + ], + } + ], + } + with pytest.raises(ValueError, match="256"): + compile_qubo(Problem.from_dict(data)) diff --git a/test/LabScheduling/mypy.ini b/test/LabScheduling/mypy.ini new file mode 100644 index 00000000..78d2bab1 --- /dev/null +++ b/test/LabScheduling/mypy.ini @@ -0,0 +1,9 @@ +[mypy] +python_version = 3.12 +follow_imports = silent +ignore_missing_imports = True + +# The binary bindings do not publish complete static type exports. +[mypy-pyqpanda3.*] +follow_imports = skip +follow_imports_for_stubs = True diff --git a/test/LabScheduling/pytest.ini b/test/LabScheduling/pytest.ini new file mode 100644 index 00000000..caae6f69 --- /dev/null +++ b/test/LabScheduling/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +python_files = *.test.py +addopts = -ra --import-mode=importlib diff --git a/test/LabScheduling/quantum.test.py b/test/LabScheduling/quantum.test.py new file mode 100644 index 00000000..f8b527ce --- /dev/null +++ b/test/LabScheduling/quantum.test.py @@ -0,0 +1,179 @@ +"""Real CPUQVM tests for bit ordering, Hamiltonians, invariants and sampling.""" + +from pathlib import Path + +import numpy as np +import pytest +from pyqpanda3.core import CPUQVM, QProg, X +from pyqpanda_alg.LabScheduling import ( + Problem, + build_circuit, + circuit_probabilities, + compile_qubo, + load_problem, + solve_qaoa, + to_ising, +) + +DATA = ( + Path(__file__).resolve().parents[2] + / "pyqpanda-algorithm/example/LabScheduling/data" +) + + +def small_model(): + return compile_qubo(load_problem(DATA / "simple.json")) + + +def test_simulator_little_endian_contract(): + machine = CPUQVM() + machine.run(QProg(3) << X(0) << X(2), shots=1) + probabilities = np.abs(machine.result().get_state_vector()) ** 2 + assert probabilities.argmax() == 5 + assert machine.result().get_prob_dict()["101"] == pytest.approx(1) + machine.run(QProg(3) << X(0), shots=1) + assert np.abs(machine.result().get_state_vector()).argmax() == 1 + + +def test_ising_diagonal_matches_qubo_all_states_including_indices_above_9(): + model = compile_qubo(load_problem(DATA / "medium.json")) + terms = to_ising(model).terms() + for index in range(1 << len(model.variables)): + bits = [(index >> i) & 1 for i in range(len(model.variables))] + energy = 0.0 + for term in terms: + sign = 1 + for pauli in term.paulis(): + if pauli.is_Z(): + sign *= 1 - 2 * bits[pauli.qbit()] + energy += sign * term.coef().real + assert energy == pytest.approx(model.energy(bits), abs=1e-10) + + +@pytest.mark.parametrize("parameters", [[0.0, 0.0], [0.4, 0.7], [0.2, -0.5, 0.9, 1.1]]) +def test_xy_preserves_each_domain_and_probability_normalization(parameters): + model = small_model() + probabilities = circuit_probabilities(model, parameters) + assert probabilities.sum() == pytest.approx(1) + for index, probability in enumerate(probabilities): + if probability > 1e-12: + assert all( + sum((index >> i) & 1 for i in domain) == 1 for domain in model.domains + ) + if parameters == [0.0, 0.0]: + assert np.count_nonzero(probabilities > 1e-12) == 9 + assert probabilities[probabilities > 1e-12] == pytest.approx(np.full(9, 1 / 9)) + + +def test_x_initial_state_is_uniform_and_not_one_hot(): + probabilities = circuit_probabilities(small_model(), [0.0, 0.0], "x") + assert probabilities == pytest.approx(np.full(64, 1 / 64)) + + +def test_phase_and_x_mixer_against_independent_dense_numpy_simulation(): + model = small_model() + gamma, beta = 0.61, -0.32 + state = np.full(64, 1 / 8, dtype=complex) + energies = np.array( + [model.energy([(i >> j) & 1 for j in range(6)]) for i in range(64)] + ) + state *= np.exp(-1j * gamma * energies / model.penalty) + for qubit in range(6): + previous = state.copy() + for index in range(64): + state[index] = ( + np.cos(beta) * previous[index] + - 1j * np.sin(beta) * previous[index ^ (1 << qubit)] + ) + assert circuit_probabilities(model, [gamma, beta], "x") == pytest.approx( + np.abs(state) ** 2 + ) + + +def test_fixed_seed_reproducibility_and_best_is_actually_sampled(): + model = small_model() + first = solve_qaoa(model, seed=7, maxiter=25, restarts=1) + second = solve_qaoa(model, seed=7, maxiter=25, restarts=1) + assert first["counts"] == second["counts"] + assert first["parameters"] == pytest.approx(second["parameters"]) + assert first["best"] == second["best"] + assert sum(first["counts"].values()) == first["shots"] + bitstring = "".join(map(str, first["best"]["bits"][::-1])) + assert first["counts"][bitstring] > 0 + assert first["expected_energy"] <= first["initial_expected_energy"] + assert first["best"]["objective"] == 5 + + +def test_infeasible_not_mislabeled_as_quantum_certificate(): + model = compile_qubo(load_problem(DATA / "infeasible.json")) + result = solve_qaoa(model, maxiter=6, restarts=1) + assert result["status"] == "no_feasible_sample" + assert result["best"]["objective"] is None + assert result["best"]["violation_count"] == 1 + assert result["one_hot_probability"] == pytest.approx(1) + + +def test_singleton_empty_domain_and_zero_task_boundaries(): + data = {"name": "boundary", "horizon": 1, "resources": [{"id": "r"}], "tasks": []} + assert solve_qaoa(compile_qubo(Problem.from_dict(data)))["status"] == "trivial" + data["tasks"] = [{"id": "t", "group": "g", "duration": 1, "options": []}] + assert ( + solve_qaoa(compile_qubo(Problem.from_dict(data)))["status"] + == "infeasible_domain" + ) + data["tasks"][0]["options"] = [{"resource": "r", "start": 0, "cost": 0}] + model = compile_qubo(Problem.from_dict(data)) + assert circuit_probabilities(model, [0.4, 0.7]) == pytest.approx([0, 1]) + assert solve_qaoa(model, maxiter=6, restarts=1)["best"]["objective"] == 0 + + +@pytest.mark.parametrize( + "kwargs", + [ + {"seed": -1}, + {"seed": True}, + {"shots": 0}, + {"layers": 0}, + {"layers": 9}, + {"restarts": 0}, + {"maxiter": 0}, + {"mixer": "bad"}, + ], +) +def test_invalid_solver_options(kwargs): + with pytest.raises(ValueError): + solve_qaoa(small_model(), **kwargs) + + +def test_invalid_circuit_and_scale_and_qubit_budget(): + model = small_model() + for params in [[], [1], [float("nan"), 0]]: + with pytest.raises(ValueError): + build_circuit(model, params) + with pytest.raises(ValueError): + build_circuit(model, [0, 0], "bad") + for scale in [0, -1, float("inf")]: + with pytest.raises(ValueError): + to_ising(model, scale) + data = { + "name": "large", + "horizon": 20, + "resources": [{"id": "r"}], + "tasks": [ + { + "id": "t", + "group": "g", + "duration": 1, + "options": [ + {"resource": "r", "start": i, "cost": 0} for i in range(17) + ], + } + ], + } + with pytest.raises(ValueError, match="1..16"): + solve_qaoa(compile_qubo(Problem.from_dict(data))) + + +def test_cobyla_budget_is_not_silently_increased(): + with pytest.raises(ValueError, match="COBYLA"): + solve_qaoa(small_model(), layers=4, maxiter=6) diff --git a/test/LabScheduling/ruff.toml b/test/LabScheduling/ruff.toml new file mode 100644 index 00000000..0df68154 --- /dev/null +++ b/test/LabScheduling/ruff.toml @@ -0,0 +1,5 @@ +target-version = "py311" +line-length = 88 + +[lint] +select = ["E", "F", "I", "UP", "B"] diff --git a/test/LabScheduling/sensitivity.test.py b/test/LabScheduling/sensitivity.test.py new file mode 100644 index 00000000..bb1786ef --- /dev/null +++ b/test/LabScheduling/sensitivity.test.py @@ -0,0 +1,176 @@ +"""Weight invariants, tied optima, retained misses and real CPU replay.""" + +import copy +import hashlib +import importlib +import json +import sys +from pathlib import Path + +import pytest +from pyqpanda_alg.LabScheduling import Problem + +EXAMPLES = ( + Path(__file__).resolve().parents[2] / "pyqpanda-algorithm/example/LabScheduling" +) +sys.path.insert(0, str(EXAMPLES)) +try: + sensitivity = importlib.import_module("sensitivity") + demo = importlib.import_module("tradeoff_demo") +finally: + sys.path.pop(0) + + +def base_input(): + return json.loads((EXAMPLES / "data/tradeoff.json").read_text()) + + +def test_frozen_inputs_preserve_every_feasible_schedule_and_certify_switch(): + base = base_input() + untouched = copy.deepcopy(base) + config = json.loads((EXAMPLES / "data/sensitivity.json").read_text()) + assert config["weights"] == list(range(7)) + assert config["seeds"] == [7, 19, 42] + assert ( + hashlib.sha256((EXAMPLES / "data/tradeoff.json").read_bytes()).hexdigest() + == config["base_sha256"] + ) + reference = None + for weight in config["weights"]: + raw = sensitivity.weighted_input(base, weight) + assert all(t["change_cost"] == weight for t in raw["tasks"]) + reverted = copy.deepcopy(raw) + for task in reverted["tasks"]: + task["change_cost"] = 2 + assert reverted == base + rows = sensitivity.feasible_schedules(Problem.from_dict(raw)) + choices = [r["choices"] for r in rows] + assert len(choices) == 9 + assert reference is None or choices == reference + reference = choices + for row in rows: + assert ( + row["objective"] + == row["preference_cost"] + weight * row["changed_tasks"] + ) + optimum = min(r["objective"] for r in rows) + assert optimum == min(6 + 3 * weight, 12 + weight) + optimal = [r["choices"] for r in rows if r["objective"] == optimum] + assert optimal == ( + [[1, 1, 1]] + if weight < 3 + else [[2, 0, 0]] + if weight > 3 + else [[1, 1, 1], [2, 0, 0]] + ) + assert base == untouched + for value in (-1, True, 1.5, 1_000_001): + with pytest.raises(ValueError, match="weight"): + sensitivity.weighted_input(base, value) + + +def diagnostic_report(row, optimum): + # Deliberately synthetic diagnostic inputs; no quantum run is mocked. + return { + "status": "feasible", + "runtime_seconds": 1, + "absolute_gap": row["objective"] - optimum, + "quantum": { + "seed": 7, + "business_metrics": row, + "best": { + "feasible": True, + "objective": row["objective"], + "violation_count": 0, + }, + }, + } + + +def test_accepts_both_tied_optima_and_retains_nonoptimal_sample(): + rows = sensitivity.feasible_schedules( + Problem.from_dict(sensitivity.weighted_input(base_input(), 3)) + ) + optimal = [r for r in rows if r["objective"] == 15] + assert len(optimal) == 2 + for row in optimal: + metrics = sensitivity.sample_metrics(diagnostic_report(row, 15), rows) + assert metrics["optimal"] and metrics["absolute_gap"] == 0 + assert metrics["choices"] == row["choices"] + suboptimal = next(r for r in rows if r["objective"] > 15) + metrics = sensitivity.sample_metrics(diagnostic_report(suboptimal, 15), rows) + assert not metrics["optimal"] and metrics["absolute_gap"] > 0 + assert metrics["objective"] == suboptimal["objective"] + altered = diagnostic_report(optimal[0], 15) + altered["quantum"]["business_metrics"] = {**optimal[0], "change_cost": 999} + with pytest.raises(ValueError, match="business audit"): + sensitivity.sample_metrics(altered, rows) + + +@pytest.mark.parametrize( + "best", [None, {"feasible": False, "violation_count": 2, "objective": None}] +) +def test_sampling_failure_keeps_empty_business_fields(best): + rows = sensitivity.feasible_schedules(Problem.from_dict(base_input())) + report = { + "status": "no_feasible_sample", + "absolute_gap": None, + "runtime_seconds": 1, + "quantum": {"seed": 42, "best": best}, + } + result = sensitivity.sample_metrics(report, rows) + assert not result["optimal"] + assert all( + result[k] is None + for k in ( + "objective", + "choices", + "change_cost", + "changed_tasks", + "absolute_gap", + ) + ) + + +def test_actual_twenty_one_cpu_runs_replay_and_reject_tampering(tmp_path, capsys): + first = sensitivity.run_sensitivity(tmp_path / "first") + second = sensitivity.run_sensitivity( + tmp_path / "second", verify_against=tmp_path / "first/results.json" + ) + capsys.readouterr() + assert len(first["runs"]) == 21 + assert json.loads((tmp_path / "second/replay.json").read_text())[ + "identical_except_timing" + ] + for row in second["weights"]: + assert len(row["alternatives"]) == 9 + assert len(row["samples"]) == 3 + for run in first["runs"]: + for component in run["report"]["quantum"]["components"]: + key = "".join(map(str, component["best"]["bits"][::-1])) + assert component["counts"][key] > 0 + # A different tied optimum is accepted by scoring, but is not an exact replay. + corrupted = copy.deepcopy(first) + corrupted["weights"][3]["samples"][0]["choices"] = next( + choice + for choice in first["weights"][3]["optimal_choices"] + if choice != first["weights"][3]["samples"][0]["choices"] + ) + with pytest.raises(ValueError, match="beyond timing"): + sensitivity.verify_replay(tmp_path / "first/results.json", corrupted) + corrupted = copy.deepcopy(first) + corrupted["source_sha256"]["protocol"] = "changed" + with pytest.raises(ValueError, match="beyond timing"): + sensitivity.verify_replay(tmp_path / "first/results.json", corrupted) + assert (tmp_path / "first/summary.csv").read_text().count("\n") == 8 + assert (tmp_path / "first/sensitivity.svg").is_file() + + +def test_demo_rejects_replay_flag_without_sensitivity(monkeypatch, capsys): + monkeypatch.setattr( + sys, "argv", ["tradeoff_demo", "--verify-sensitivity", "missing.json"] + ) + with pytest.raises(SystemExit) as error: + demo.main() + assert error.value.code == 2 + assert "requires --sensitivity" in capsys.readouterr().err diff --git a/test/LabScheduling/support.test.py b/test/LabScheduling/support.test.py new file mode 100644 index 00000000..024a59a1 --- /dev/null +++ b/test/LabScheduling/support.test.py @@ -0,0 +1,288 @@ +"""Raw semantic equivalence, replayable deletion evidence and arc limitations.""" + +import importlib +import json +import sys +from dataclasses import replace +from pathlib import Path +from random import Random + +import pytest +from pyqpanda_alg.LabScheduling import ( + Problem, + compile_qubo, + load_problem, + reduce_model, + solve_exact, + solve_reduced, +) +from pyqpanda_alg.LabScheduling.__main__ import main +from pyqpanda_alg.LabScheduling.report import run_experiment + +EXAMPLES = ( + Path(__file__).resolve().parents[2] / "pyqpanda-algorithm/example/LabScheduling" +) +sys.path.insert(0, str(EXAMPLES)) +try: + support = importlib.import_module("support_pruning") +finally: + sys.path.pop(0) + + +def random_problem(seed): + rng = Random(seed) + tasks = [] + for ti in range(rng.randrange(6)): + tasks.append( + { + "id": f"t{ti}", + "group": f"g{rng.randrange(2)}", + "duration": rng.randint(1, 2), + "change_cost": rng.randrange(5), + "original": {"resource": "r0", "start": ti}, + "options": [ + {"resource": r, "start": s, "cost": rng.randrange(5)} + for r, s in rng.sample( + [(r, s) for r in ("r0", "r1") for s in range(6)], + rng.randint(0 if seed % 9 == 0 else 1, 4), + ) + ], + } + ) + return Problem.from_dict( + { + "name": f"arc-{seed}", + "horizon": 7, + "resources": [ + {"id": "r0", "cleanup": seed % 2, "downtime": [[0, 1]]}, + {"id": "r1"}, + ], + "tasks": tasks, + "precedence": [ + [a["id"], b["id"]] + for a in tasks + for b in tasks + if a != b and rng.random() < 0.08 + ], + } + ) + + +def slot_problem(tasks, starts, duration=1): + return Problem.from_dict( + { + "name": "shared slots", + "horizon": 10, + "resources": [{"id": "r"}], + "tasks": [ + { + "id": f"t{i}", + "group": f"g{i}", + "duration": duration, + "options": [ + {"resource": "r", "start": s, "cost": 0} for s in starts + ], + } + for i in range(tasks) + ], + } + ) + + +def test_two_hundred_raw_feasible_sets_costs_and_deletion_witnesses(): + for seed in range(200): + support.audit_problem(random_problem(seed)) + + +def test_existing_v2_corpus_preserves_every_raw_feasible_schedule(): + paths = sorted((EXAMPLES / "data/corpus-v2").glob("n*.json")) + assert len(paths) == 36 + for path in paths: + support.audit_problem(load_problem(path)) + + +def test_support_cascade_crosses_resource_boundary_without_fixing_decisions(): + model = compile_qubo(load_problem(EXAMPLES / "data/support-chain.json")) + old = reduce_model(model) + new = reduce_model(model, pruning="arc") + assert old.audit()["component_qubits"] == [18] + assert new.audit()["component_qubits"] == [12] + assert old.fixed == new.fixed == () + assert len(new.removals) == 6 + assert all(row["rule"] == "no_support" for row in new.removals) + # Some witnesses depend on earlier deletions, requiring backward re-queuing. + assert any( + len(row["blockers"]) + < len( + model.domains[ + next( + i + for i, t in enumerate(model.problem.tasks) + if t.id == row["against_task"] + ) + ] + ) + for row in new.removals + ) + support.verify_removals(model, new) + audit = support.audit_problem(model.problem) + assert audit["feasible_count"] == 15 + assert audit["exact_objective"] == 3 + assert solve_reduced(model)["status"] == "resource_limit" + run = solve_reduced(model, pruning="arc", maxiter=10, restarts=1) + assert run["best"]["feasible"] + assert run["best"]["objective"] >= 3 + for component in run["components"]: + key = "".join(map(str, component["best"]["bits"][::-1])) + assert component["counts"][key] > 0 + + +def test_detects_infeasibility_without_any_initial_singleton(): + model = compile_qubo(slot_problem(2, [0, 1], duration=3)) + assert reduce_model(model).infeasible_task is None + reduced = reduce_model(model, pruning="arc") + assert reduced.infeasible_task == "t0" + support.verify_removals(model, reduced) + result = solve_reduced(model, pruning="arc") + assert result["status"] == "infeasible_propagation" + assert result["circuit_evaluations"] == 0 + assert result["components"] == [] and result["best"] is None + assert not support.audit_problem(model.problem)["feasible_count"] + + +def test_pairwise_support_does_not_certify_global_feasibility(): + problem = slot_problem(3, [0, 1]) + model = compile_qubo(problem) + reduction = reduce_model(model, pruning="arc") + assert reduction.infeasible_task is None + assert not reduction.removals and reduction.audit()["component_qubits"] == [6] + assert solve_exact(model)["status"] == "infeasible" + run = solve_reduced(model, pruning="arc", maxiter=6, restarts=1) + assert run["status"] == "no_feasible_sample" + assert not run["best"]["feasible"] + support.audit_problem(problem) + + +def test_empty_independent_and_forced_cases(): + empty = compile_qubo(slot_problem(0, [])) + assert solve_reduced(empty, pruning="arc")["best"]["objective"] == 0 + no_options = compile_qubo(slot_problem(1, [])) + assert ( + solve_reduced(no_options, pruning="arc")["status"] == "infeasible_propagation" + ) + p = slot_problem(2, [0, 1]) + independent = replace( + p, + tasks=( + p.tasks[0], + replace( + p.tasks[1], + options=tuple( + replace(o, start=o.start + 5) for o in p.tasks[1].options + ), + ), + ), + ) + assert support.audit_problem(independent)["modes"]["arc"]["component_qubits"] == [ + 2, + 2, + ] + forced = replace( + p, tasks=(replace(p.tasks[0], options=(p.tasks[0].options[0],)), p.tasks[1]) + ) + assert len(support.audit_problem(forced)["modes"]["arc"]["fixed_variables"]) == 2 + assert ( + solve_reduced(compile_qubo(forced), pruning="arc")["circuit_evaluations"] == 0 + ) + + +def test_rejects_invalid_modes_and_ignored_cli_option(capsys): + path = EXAMPLES / "data/simple.json" + model = compile_qubo(load_problem(path)) + with pytest.raises(ValueError, match="pruning"): + reduce_model(model, pruning="unknown") + with pytest.raises(ValueError, match="pruning"): + run_experiment(path, reduce=True, pruning="unknown") + with pytest.raises(ValueError, match="requires"): + run_experiment(path, pruning="arc") + with pytest.raises(SystemExit) as error: + main([str(path), "--pruning", "arc"]) + assert error.value.code == 2 + assert "requires" in capsys.readouterr().err + + +def test_arc_cli_report_replay_and_default_backward_compatibility(tmp_path, capsys): + path = EXAMPLES / "data/simple.json" + default = run_experiment(path, reduce=True, maxiter=6, restarts=1) + explicit = run_experiment( + path, reduce=True, pruning="singleton", maxiter=6, restarts=1 + ) + assert support.without_timing(default) == support.without_timing(explicit) + out = tmp_path / "report.json" + assert ( + main( + [ + str(path), + "--reduce", + "--pruning", + "arc", + "--maxiter", + "6", + "--restarts", + "1", + "--output", + str(out), + ] + ) + == 0 + ) + report = json.loads(out.read_text()) + assert json.loads(capsys.readouterr().out) == report + replay = run_experiment(path, reduce=True, pruning="arc", maxiter=6, restarts=1) + assert support.without_timing(report) == support.without_timing( + json.loads(json.dumps(replay)) + ) + assert report["quantum"]["business_metrics"]["feasible"] + + +def test_deletion_verifier_rejects_partial_or_fabricated_support_witness(): + model = compile_qubo(load_problem(EXAMPLES / "data/support-chain.json")) + reduction = reduce_model(model, pruning="arc") + row = reduction.removals[0] + for blockers, match in ( + (row["blockers"][:-1], "entire live domain"), + ([{**b, "reasons": ["invented"]} for b in row["blockers"]], "forbidden pair"), + ): + changed = replace( + reduction, removals=({**row, "blockers": blockers}, *reduction.removals[1:]) + ) + with pytest.raises(ValueError, match=match): + support.verify_removals(model, changed) + + +def test_evidence_command_creates_directories_and_replays_real_cpu_runs( + tmp_path, monkeypatch, capsys +): + first, replay = tmp_path / "first", tmp_path / "replay" + monkeypatch.setattr(sys, "argv", ["support_pruning", "--output", str(first)]) + support.main() + monkeypatch.setattr( + sys, + "argv", + [ + "support_pruning", + "--output", + str(replay), + "--verify-against", + str(first / "results.json"), + ], + ) + support.main() + capsys.readouterr() + assert json.loads((replay / "replay.json").read_text())["identical_except_timing"] + result = json.loads((first / "results.json").read_text()) + assert len(result["instances"]) == 37 + assert result["boundary_original"]["before_outage"]["objective"] == 0 + assert not result["boundary_original"]["after_outage"]["feasible"] + assert result["boundary_default"]["status"] == "resource_limit" + assert [r["absolute_gap"] for r in result["boundary_arc"]] == [0, 0, 0]