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robertodr merged 4 commits into from
Aug 2, 2026
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fix: links to benchmarks in README.md #178

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86bd865
fix: links to benchmarks in README.md
robertodr Jul 31, 2026
a3e31d6
docs: use plots with scaling wrt lattice size
robertodr Jul 31, 2026
da591bf
style: resize images in readme
robertodr Jul 31, 2026
a492cd7
style: more tweaking of image sizes
robertodr Jul 31, 2026
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27 changes: 21 additions & 6 deletions README.md
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
# monoprop
> because your operators deserve to propagate at escape velocity

[![Documentation](https://github.com/Algorithmiq/monoprop/actions/workflows/docpages.yml/badge.svg)](https://docs.algorithmiq.fi/monoprop)
[![Documentation](https://github.com/Algorithmiq/monoprop/actions/workflows/docpages.yml/badge.svg)](https://docs.monoprop.algorithmiq.tech/)
[![Test monoprop](https://github.com/Algorithmiq/monoprop/actions/workflows/test.yml/badge.svg)](https://github.com/Algorithmiq/monoprop/actions/workflows/test.yml)
[![codecov](https://codecov.io/gh/Algorithmiq/monoprop/graph/badge.svg?token=1GgmPnNUxk)](https://codecov.io/gh/Algorithmiq/monoprop)

Expand All @@ -18,9 +18,20 @@ the operator across cores and across nodes with MPI.

## Benchmarks

Check out the comparison of `monoprop` against other open-source Pauli propagation engines in [benches/third_party]!
![Runtime Benchmark](benches/third_party/pauli_prop/runtime.png)
![Memory Benchmark](benches/third_party/pauli_prop/memory.png)
Check out the comparison of `monoprop` against other open-source Pauli propagation engines:

<p align="center">
<img src="docs/public/benchmarks/pauli_scaling_runtime.png" alt="PP Runtime Benchmark" width="49%" />
<img src="docs/public/benchmarks/pauli_scaling_memory.png" alt="PP Memory Benchmark" width="49%" />
</p>

and against [`MajoranaPropagation.jl`](https://github.com/SparqleSim/MajoranaPropagation.jl):

<p align="center">
<img src="docs/public/benchmarks/majorana_results.png" alt="MP Benchmark" width="50%" />
</p

Head to our [benchmarks page](https://docs.monoprop.algorithmiq.tech/benchmarks) for more details.

📖 **Full documentation:** <https://docs.algorithmiq.fi/monoprop>

Expand All @@ -43,7 +54,9 @@ from monoprop import MajoranaPropagator, ExpGate, Circuit, MajoranaOperator
# Observable m_0 m_1 m_2 m_4, evolved under one Majorana rotation exp(-i θ/2 · M_γ),
# generated by M_γ = i*m_4 m_5.
observable = MajoranaOperator({(0, 1, 2, 4): 1.0}, num_modes=8)
gate = ExpGate(MajoranaOperator({(4, 5): 1j}, num_modes=8)) # Hermitian generator: weight-2 => imaginary coeff
gate = ExpGate(
MajoranaOperator({(4, 5): 1j}, num_modes=8)
) # Hermitian generator: weight-2 => imaginary coeff
circuit = Circuit(gates=[gate], parameters=[0.5]) # one angle value per gate

mp = MajoranaPropagator.from_circuit(circuit, observable, cutoff=16)
Expand All @@ -56,7 +69,9 @@ Qubit (Pauli) operators are simulated with `PauliPropagator`. Here we back-propa
```python
from monoprop import PauliPropagator, ExpGate, Circuit, PauliOperator, Pauli

observable = PauliOperator({"ZZ": 1.0}, num_qubits=2) # num_qubits lives on the observable
observable = PauliOperator(
{"ZZ": 1.0}, num_qubits=2
) # num_qubits lives on the observable
gate = ExpGate(PauliOperator({Pauli("X", 0): 1.0}, num_qubits=2)) # exp(-i θ/2 · X_0)
circuit = Circuit(gates=[gate], parameters=[0.5]) # one angle value per gate

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