diff --git a/.gitignore b/.gitignore index 1fd0376..0661491 100644 --- a/.gitignore +++ b/.gitignore @@ -13,7 +13,6 @@ src/RSI_tk.jl RhombGraph.ipynb logo_tensorBinding.svg examples/nontracked/ -GPU_tk.jl Conductivity_tk.jl TensorBinding_JOSS_AI.txt docs/src/joss.txt diff --git a/examples/dynamics/time_evolution.ipynb b/examples/dynamics/time_evolution.ipynb index 1bbae8e..0dd4011 100644 --- a/examples/dynamics/time_evolution.ipynb +++ b/examples/dynamics/time_evolution.ipynb @@ -1,17 +1,4 @@ { - "nbformat": 4, - "nbformat_minor": 5, - "metadata": { - "kernelspec": { - "display_name": "Julia 1.10", - "language": "julia", - "name": "julia-1.10" - }, - "language_info": { - "name": "julia", - "version": "1.10.0" - } - }, "cells": [ { "cell_type": "markdown", @@ -29,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "te_imports", "metadata": {}, "outputs": [], @@ -38,7 +25,7 @@ "using Plots\n", "using ITensors\n", "using ITensorMPS\n", - "include(\"../src/TensorBinding.jl\")\n", + "include(\"../../src/TensorBinding.jl\")\n", "using .TensorBinding" ] }, @@ -5015,5 +5002,20 @@ ")" ] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.12", + "language": "julia", + "name": "julia-1.12" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/manybody/many_body.ipynb b/examples/manybody/many_body.ipynb index ed1fed2..1af9397 100644 --- a/examples/manybody/many_body.ipynb +++ b/examples/manybody/many_body.ipynb @@ -1,17 +1,4 @@ { - "nbformat": 4, - "nbformat_minor": 5, - "metadata": { - "kernelspec": { - "display_name": "Julia 1.10", - "language": "julia", - "name": "julia-1.10" - }, - "language_info": { - "name": "julia", - "version": "1.10.0" - } - }, "cells": [ { "cell_type": "markdown", @@ -30,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "mb_imports", "metadata": {}, "outputs": [], @@ -39,7 +26,7 @@ "using Plots\n", "using ITensors\n", "using ITensorMPS\n", - "include(\"../src/TensorBinding.jl\")\n", + "include(\"../../src/TensorBinding.jl\")\n", "using .TensorBinding" ] }, @@ -53,11 +40,11 @@ "\n", "An exciton is a correlated electron-hole pair. In the quantics representation\n", "the two-particle Hilbert space is encoded on a `2L`-qubit chain:\n", - "sites `1..L` for the electron, sites `L+1..2L` for the hole.\n", + "odd sites for the electron position qubits and even sites for the hole position qubits.\n", "\n", "The exciton Hamiltonian is\n", "$$H_{\\rm exc} = H_e \\otimes I_h + I_e \\otimes H_h + V_{eh}$$\n", - "where $V_{eh}(r_e, r_h) = -U/|r_e - r_h|$ is the Coulomb attraction.\n", + "where $V_{eh}(r_e, r_h) = -U(r,r)$ is a Hubbard-type interaction that can be modulated in space.\n", "\n", "The `2L`-site MPO makes the standard MPO-mode Chebyshev list expensive;\n", "the **MPS mode** (`KPM_Tn(H, Ncheb, X; ...)`) propagates a single reference\n", @@ -65,54 +52,752 @@ ] }, { - "cell_type": "markdown", - "id": "sp_exc_intro", + "cell_type": "code", + "execution_count": 18, + "id": "sp_exc_build", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TBHamiltonian | L=5, N=32 [exciton, D=1024], scale=10.0, maxlinkdim=11 | geometry: 32 sites, 1D | no Tn cache\n" + ] + } + ], "source": [ - "---\n", - "## 2. Exciton Hamiltonians\n", + "# 1D electron + hole on a chain; APSOS-style modulated type-I confinement\n", + "L_exc = 5 # 2^5 = 32 sites per particle\n", + "t_exc = -1.0\n", + "U_exc = 6.0 # contact attraction strength\n", + "V0_exc = 1.5 # confinement scale\n", + "scale_exc = 10.0\n", + "maxdim_exc = 100\n", "\n", - "An exciton is a correlated electron-hole pair. In the quantics representation\n", - "the two-particle Hilbert space is encoded on a `2L`-qubit chain:\n", - "sites `1..L` for the electron, sites `L+1..2L` for the hole.\n", + "N_sites_exc = 2^L_exc\n", "\n", - "The exciton Hamiltonian is\n", - "$$H_{\\rm exc} = H_e \\otimes I_h + I_e \\otimes H_h + V_{eh}$$\n", - "where $V_{eh}(r_e, r_h) = -U/|r_e - r_h|$ is the Coulomb attraction.\n", + "function Vx_exc(x; V0=V0_exc, N=N_sites_exc)\n", "\n", - "The `2L`-site MPO makes the standard MPO-mode Chebyshev list expensive;\n", - "the **MPS mode** (`KPM_Tn(H, Ncheb, X; ...)`) propagates a single reference\n", - "state $|X, X\\rangle$ and is the recommended approach." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "sp_exc_build", - "metadata": {}, - "outputs": [], - "source": [ - "# 1D electron + hole on a chain; Coulomb attraction\n", - "L_exc = 5 # 2^5 = 32 sites per particle\n", - "t_exc = 1.0\n", - "U_exc = 2.0 # Coulomb strength\n", + " b = sqrt(3)*N/10\n", + " k = 2*pi/b\n", + "\n", + " return V0*(1 + 0.2*(cos(k*x)))\n", + "end\n", "\n", - "H_exc = TensorBinding.get_Hamiltonian(\"exciton_1d\", (t=t_exc, U=U_exc); L=L_exc)\n", + "H_exc = TensorBinding.exciton_hamiltonian(\"chain_1d\", t_exc, x -> U_exc;\n", + " L = L_exc,\n", + " on_site = Vx_exc,\n", + " scale = scale_exc,\n", + " maxdim = maxdim_exc)\n", "println(H_exc)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "sp_exc_kpm", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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XGgoWO+sItQLPLGMm17juuuvMH83whscee8wv8PrrrxPRJZdc4ncKPZMKXqM+ny88PDwtLa2Co+O2bduIKDEx8dSpU/6L+/btMzOg+gM2/HGEFT5l7NixRPTbb79Vczu19hotLS19+eWXzbWqgo/onDlziCg1NdUfBGLi8XjM9a9Tp07+jKOHDx8+M9WLYRjr1q0zl8D9+/dX1THzBhcuXHjmr3w+X4sWLYho9+7d5hXThyUwfYxJRkaGw+FITk7Ozc31X3Q6nWlpaTabrUIWlcWLFy9YsKD6yIrnnnuOiK644orA2TSfir59+/qvmF6jFZKpfv3110R0xx13VGgzMTExISHB/+NTTz1FRNdee+2ZwZeBieLMMPyXX37Zf+XQoUNmoju/12h5eXlYWFjz5s0DnZw3bdpknquf6R163nnn2Wy2kydPVjMCgiALYb3EXAjj4uLuqQy/M/2qVas0TevQoYPfud/pdFaIoHC73eY54UUXXbR8+fIdO3asWbPmlVdeGTRokP/jKiyEhmH079+fiK6//vpXX311wYIF/u9fc+U4//zzP/vssx07dixdutQ8hn3yySf9f3v2C2HXrl3PvOsHH3zQlDEXwilTpjzyyCOPPPLInXfeOWrUKNNpIjw8/MyIPZ/PN3HiRCKKjo7+05/+9Pnnn//www+vv/666UCUmpr666+/+oVNzaNjx46PPfbYihUrfvjhh+XLlz/44IPmAWBV8e+GYeTm5jocjvDw8Kq+lB988EEievzxx80fnU5nTEzMeeedFyhTXl5uKnBnZmX7/PPPiaht27aBicVN+1lGRkY1Q1pYWGjGel5//fVr16795ZdfZs2aFRERERYWFhjJcDYLYXFxsTmYgwcP/uijjzIyMn766aePPvpo0qRJaWlpfjHT90fTtAkTJrzyyiuPPPJIkyZNTMNnYPiEmSbmmmuuWb9+/e7du998883k5GTTo6fCQpibm2u32y3JBSjUL2QhrJeYC2FVmAUNjhw5kpycHBERUeF78MyY+sLCwkmTJvkTqpn07t3b/ydnLoQ7duzo27ev/0/8Aekul2vq1KmBx4MREREzZswI1DbOfiGslPDwcFMmMNmb/1c9e/Z89NFHq1LXdF2fP39+hXwlNpttwoQJ2dnZgZLHjh0bP378mZaz6OjoadOmVaNVv/jii0R09dVXVyVgHiAH5rgx1x4zH56JmVm7QhpuP+Zy/tBDD5k/lpeXh4eHh4eHHzt2rKoPNcnOzg6MfyCi5s2bf/HFF4EyZ7MQGoaRn58/adKkCufGkZGR/rMKk59//vniiy82fxsfHz9t2jTzzPaqq67yy+zfv79Dhw7+RjRNmzp1qqnCVlgITT/VJUuWVH/7gqAZUqG+HuJ2u03X80pp3LhxcnJyQUHBqVOnoqKimjVrVkHg2LFj5eXlSUlJpsZgcuTIkZ9++snpdDZu3LhLly6mjcokOzvb7XZX8JInIo/Hk5ub6/F44uLiAv0Rjh49unbtWqfTmZycPHjw4AquCiUlJcePH09ISKgQJpGbm1taWpqWlhYeHl7VrTmdzhMnTlT6K5vNZmo2hw4dCrRfJiQkJCQkIFlMfT7fhg0bMjMzy8rKUlNTBwwYUJWThcfj2b59+5EjR44fP+5wONq2bXvRRRfFxMRU0/jx48dLSkoSExNN3bRSzKC9Vq1amb3dvn17z54977vvPtNOSUTZ2dk+ny81NfVMR00iKisry8nJsdvt5lZgzZo1Q4YMufvuu81FonoMw9i+ffsvv/zicrnat28/cODAwHhBIjpx4kRRUVGzZs0CHUfNT4yPj09OTg4UPnjwIBG1adOmwqfk5uauX78+Ly8vNjY2LS2td+/elXr3OJ3O4uLilJQUh8OxYsWK8ePH33vvva+99ppfwO12r1+/PjMzMzIycsCAAWaquZMnTwYOr2EYPXr0OH78+O+//17hXgShIhYvxIIgVM1NN90UGRnp191ZPP3005GRkYcPHw56r+oMXdcvv/xy+m9eVhZmAodFixap6JhwjiEaoSCELocPHx42bNjtt98+ffp07t++//77drv9pptuUtExFbjd7lGjRt12221msMq+ffveeOONf/3rXxdccMGWLVu4mY+uvfbaU6dOffvtt9X48QqCiSyEgiCEBB6PJyEhITBTDBENGjRo6dKlpkutIChCFkJBEEKF8vLyDRs2HDp0KD8/PyEhoXfv3oG2akFQhCyEgiAIQoNGMssIgiAIDRpZCAVBEIQGjSyEgiAIQoNGFkJBEAShQSMLoSAIgtCgkYVQEARBaNCEdGHe9evXH/n9+MUXD6hZNFTRdb1CMmsTjdNICAa4VH1f6J0ZoXhbZBiGWXW91uB/XGf3b4ZInfV9hdzMVvUQKqLOZrYu7yutbdOahchN5K5ZKgCfz/fXv7725JNP1q5XdU9IxxG+8MIL/3j+N5/X4h7iXwEcSQY6/Gbh30E2XhdQYTt8xuAjHf94/L4MuFmNcxyi4hlgLRj4feGThXeViGzwcOlwV1kjoGtoszYD7SpzBFBhzgvLegtgSQ2V/bV4aY0yupFh0B74w4mICgpK27WZ7nQ6WX9lISGtEQqCIAjWYpCuG96a5QLQDZ+izigi1BdCjac8heC5VBA+Xs0IKAHfCyvD4hFQ9mgpOWzAsfwoWzNU3BlLKVfRAUabGt7b4I6VoRvMhY0rbzmhvhAKgiAIFlIrjZAnbzmhvhBqZLdxzsZhSYZ9iGOgUrJrxi0Z+F2xOorfl07oTlCRiQ43ZTEnCx8BVJJloGIJg/BMdLA1C7elKTJScu6LMQI+xrONP65KTPUqnpZzm1BfCAVBEAQLMQzd4Gp4ohEGF5aNED/ED4HoBUVWQoutpLhtAvZr46FoJ4z79uG3pUQXUGaixB0Rg2yg+i8cLQc/wmGhaG5xrNEIDcNn6LyFjStvOaG+EAqCIAiWwtYIxVkmyOikq3BEtNixjytrvZESxW7YYVmLTXQsOCq5EkOtipllPYJqZpZlpLT4u9VGwR8BRYbq4KpjtTgaNYLcBeVIijVBEAShQRPqGqHB0wiVWFLwPf65mllGhdOmoswyKvKPkKrMMgxUZJZhPQMqZjYEMssw4GSWQcF9cVngYwVh6GIjFARBEBowho/tBSpeo8FGieJiefqT+pVZhmPzOSfzj7CoN5llWB21PGeQ5TOr4v6Z7p3weU9Qx8oQZxlBEAShQWPoxD3qlKPR4GIjm5qtmGSWUeKy6FWQf4TqWWYZFGYcoWSWsTizDMdtVckIqDBUCyahvhAKgiAIVqL7SPcw/4QpbzWhvhBqxCgnyvEuZeyY1Oh5lmeWYRF8p02NEZfGun8lNjJOZhlFuUbxDijBwB0ROTOLo6YcIAO8WUU6sZpgVgRdnGUEQRCEBkxtbITiLBNUdDLUZJaxOP8Is7aZih0uK5ISBc8/oiwpqJLArHrkuIvL8qLo1Oh5OKzAUxXgRkrO9wDLVI+iWZ2Fp94R6guhIAiCYCWGT7xGLYaVWUaRHUWFsxbrCJ9TDlCJ0yY+tHb4zliqG8dxFzZSqiqIiAezKnHcVaS8qphZ5ggEf2aZmWWC77bKSRjE8YUNbm0XQ2yEgiAIQgNGM3SNqeFx5S0n9BdCg2MhU7IZtzwox/I8OJY7reJYXhBRGcHPuKuoIKIiLJ9ZFQdOqlx8g9uwobOdXwyLDbpcpPqEIAiC0KAJdY1QI5tNUZI/JQRfeSVVUXQM8A0mx7VPkduqor2d5Y67FmeWUTGzzIKIFu/aOe7rSorA4ATZxVeORgVBEIQGTS2ORiWOMLiwco2qqFvIalZRkkMVqQsVuSwacAATp943A45rnxqHPVWZZYI/s4qcNlXE27GaVaG6sZpVlALGspMhXZJuC4IgCA0YzdA1pobHlbccWQgR6pl/oXAu0tAfQkXGV0EgWQgFQRCEaqlFQL1ohEFF52SWwVG2Y1QUcVdvCiJqaix/nHKAit5AFfUElOSZxGHGEQbfaZNppLT4u1WN464SQ3WQkaNRQRAEoUFjGOI1ajE66T41bng4HC84JUk98E0jJ9coY4OPRzvZYY3Qy9ngqwhkZI2AnTGzuHMjYwQ4LovBT4tKamaWF8gIF0S0wRGHrBHA/ZxxPQ//ciNWzmG8eKRARKG/EAqCIAgWoulyNGo1WggkORTqERZnDCLSrHdZVBJPq8L8bPlIMVFS+L4eUJtco7IQCoIgCOcKmmGwNTzRCEEKCwv379+fmJjYtm3basSU5RplEXynTUW3xLF5KMmpoahAPA7L8ofDsfooQYWmy5osFTNrea5RZrbV4M8t03EXtv4GOdeoQTpz9nVoYJ1O54IFCwYMGDBw4EDzSmZm5ltvvZWfnz9u3Lgrr7yS29NaY00e21deeaVVq1Z//OMfhw4dOnfuXEv6IAiCIFjIn//85zlz5nz33XfmjydPnuzfv7/D4Rg4cOCUKVOWL19eZz2xQCP8+uuvX3jhhS1btqSnpxNRSUlJNcLKco2yrCMqKm5bXH2CFZ2pwl3Nztm04iOgJiOlkgSe526uUXRmWSOAn3aoiDwmjkaI35ddjSoS5KBPNXGE33//fU5OzrBhw/xXFi9e3Lt37+eff56INE2bM2fOhAkTuJ2tHRZohG+99dY999zTtm3b48ePE1FMTEzd90EQBEGAMJ1luP+qpaSk5MEHH/zb3/6maf+3aVi/fv2QIUPM/w8ZMmTr1q1ut1vdbQVigUa4b9++2NjYjh072u328PDw5cuXd+nSpVLJsrIywyA1paEFQRBYKCpvE3yC/PEKyjBNnz59ypQp7dq1C7yYm5ubnJxs/r9JkyaGYeTm5rZq1Yr30bXCgoUwPz9/165dO3bsiI6Onj59+tSpU1evXl2p5JEjR3RdV5HbSRAEQfB4PA6Ho3oZzTA0prOM4fWVlJQMHz488GKLFi3ee+89IlqzZk1GRsZrr71W4a8iIiL8KqD5n8jISNbn1hoLFsLU1NSrr746OjqaiCZOnPjaa6/pum6zVbLapaenb7Pv1L3Bd1djBkWpUDSV5JlUVh4dBU/qoWgEVBRSZ8HZjCsxUuIw9YbgzyzTadPikyF8R453VJE5E09aVOMqWDtsNi0yMvKRRx4JvNi0aVPzP5999llmZmbHjh2JKC8v75tvvsnMzPzwww/T0tIOHz5syhw6dCgiIsKvIKrGgoWwb9++J0+eNP9/8uTJ+Pj4SldBQRAEwXr02uQatdvtgY4wgTzxxBP33nuv+f/777+/ffv2jz76KBGNGzfuySeffOqppyIjI5cuXXr11VfX2dJgwUJ4//33Dx48uGfPni1atJg2bdqdd95ZjbBBPk6WRbw6OWMjhjsi4j5grA7gI4C79rEOnHGHPXysOKobx22V4dzIctoMfvpKljaA35eiXKMqZpZVewHXclRMFhGFwd6w+AiwojMtyzVaizhCo7quJiUlJSUlmf+PjY1t3Lhxs2bNiGjs2LHvvPNOnz59WrVqtW3btu+//762PWZjwULYtWvXf/3rX2+88UZ5efl9991322231X0fBEEQBAhD59oIcfn58+f7j2fDwsK++OKLrVu35ufnDxgwIDY2ltfPs8CazDL9+/fv378/ImloZMCbNq3abUgdwAn2YqEikpIBJ6UFrA+pMaYZGizLyVSiIpQzBJycOToxPLMa3CrPUM/4HsBb5ejE8ANjefGHIFuUa1OGCR0Bv+HQxGaz9e3bl/dZwUCMc4IgCEKDJtSTbtsMm6FAz2P6y6EdYNXYU9EBReoIw+YB74UV6cSK3FY5JSEtTzesJDGsoWpmUTgzi8LSnDyaB25WCfjbHWYE9Yu9NrlG61lBxFBfCAVBEAQrkYXQchRVn2BphCqSN7JuCXfY4+iDSiIp8YrbrDIRKjwhmfdvca5RfLg4M8s6FQj+zFo+AsxARtwhGXdbVWKoDm5uGa02uUbr2UIoNkJBEAShQRPqGiEz2EtJij9826gorQlnEPAgNlYAEywJW6k0h+sAACAASURBVJJwL0Ti7G/tcLAXsxofijKNEJ9ZFKbTZvBnljUCeJAuPrO4mstCUSgn3myQvwnlaFQQBEFo0MhCaDk6GZwEHPjos+oRqkn2CaOiOLgip01cG2AWw8MlcWOGkg5w2lSUXYjTBRgVM8t8BlS8hqyQw+CPADPXKK4RBhVdV1ShPnQI9YVQEARBsJJgp1gLQUJ9ITRI5+QaxZtVk3yCYRtgoMIZlblnDH7FbUVuqyqeFpa4IrdVNfelxESn6ARFRaEGppkWhXOEpeQtUFGr5Nwm1BdCQRAEwVIMMkQjtBRd03U4daDdQD0GWWnvOXFRqGQYJ02GR/OCkspcFtGBDYOnwAvfFHGspJwaBWhXiaMPKaq/oab2AuMhxJ9Yr4Z2lTcCcLP49wBeUII4rwz+uCrKReWDxwqiNs4yshAKgiAI5wy6LIRWoxkaJ5e8ktFX4Ybns/qumKYJODCLUXlA0QhYHEuKawOKDDkcizLj2w2fWUVV1zmBp0qcNrX6Y6JjhSfWTC01wvqUraU+9VUQBEEQgk7Ia4SkqVirmTum4O8EvZzNePDzyhApyjOJW32UKG68mVUyAjiKRkBFYliqZzOL4lNjolPzwjLAraQQhsE+6pSjUUEQBOHcoZZHo0FdjBUT6guhnez40byK1A8sYU78lCK3VdyxTVEYH7rFxj1RiZe8Ec9IqeQZwG2EuHsnqXq2WWF8eO0FdGaVZVtVYiXlPDDBf1xJjU4MYfA1vPqmEYqNUBAEQWjQhLpGKAiCIFiJUYtco2p6ooxQXwg10jiWZ4uODv4Pi6u6cCQZMA6HGa4qrC6oGAGLI1iU3L8yVIQE8EYAr+4ExyYZapzmFDnLKKpvVTO1OBqtZyejIb8QCoIgCFZi8DU80QiDi5d8iuJz1aCkq6xMVCrAtQEbo1gPqzJw8Ju1fIvLrK4VfJ2QpeThbh2M9Aucm9LhysCKClFxGlVRypt8eG1kvFEEw2DnDq1H39lEJM4ygiAIQgMn1DVCczcCiioJpQ4JEx0uqSjpNn5fSvKeq6hrwzTRBf8ZUFSISkXFKFJzX4pKE3OebVYhKssL86IEuVk5GhUEQRAaNLIQWo7B0whxMwYjtZINHiW8qouivTCeMoqlDeD2IbwMk4dTKUZFGSZWRD8ubIfLFbFq5eDGJHxmmWWYgj+z+GQRZwTw15B1LhJuoN8DnBFgFCPDe8uqcVYjRi3KEcpCKAiCIJw7iEZoOZqqCCpGq2p8wJTk98JhWhHgQqOMYj0sG2HwLWTM+4d7CwexKcpyx7GnMqZAxcwqMtUrSjKHFxxm+gPjwM7b4gXJJNQXQkEQBMFKdNEIrYeRWYYR7qZKI1RS1QVHxViRmjJMisBnVlHidUX3j88sR9FUUoZJESpmlnkqEEzDm4ki73HcUI19sEY682BOFkJBEAThXMLgJUSsd/H0Ib8QhlEYp6gKiqJMm7hGyHJX4wSxBT/9B6lxWw3jPHucMkxKNEJ8BDjlipSU4FEUxIbflx122lTkuqzIVO+Fn21FI8B4C5jrVg00AGcZsakKgiAIDZpQ1whthg1Pc2fADnt4HnfiJDlUl3celOMUL2VtReGNM9wqPFesVskGW0c408orVQG3yRIOvvc0r/4G4+XCO8B5COF31gb7FfDcdhWMAOtUQFGzNVMbG2E9OxwN9YVQEARBsBBD1wydt7IashAGF4/m0RnVxVAYrqhqUn0y4whxPU/J2byK6hOKai+oqVHAFwdgjYCKpJS86hMKZpZ1UxocxqfoO5hx2KBqBBiyuGjNNACvUbERCoIgCA2aUNcIDTLU7IUZcOx+FhcSV1R9Qo3bqpK8KirMuSxxfGupTCVVUsdczcyy3FZRODOrxG3V8hEIvkbGdUOtZyejIb8QCoIgCFZiaAbzaFSSbp+D4DtBPH5IUVoTTvUJxqm4XUn1CUaeDtzoggd7MatPwM+AmuoT+Ajgqhs+rcS5L07tBZaVNPgzywrjC4OrT+Azy8pFhcf+4rXsIXSNmM4y4jUqCIIgnDsYfI2QdKpfx6OyENYMJ1WHxU6bitrE78vHcHBl6cQqUJNrlOG2qmgE6tHjqui7Uk2uUQUzy7x/JdZfgWQhFARBEGqgNs4yohGeW3DyTFpcfQKHmWsUBbeOKHpL8L0w0xsZt/pYjKJCGZaH8amZ2fpUfwMHD3lEMAx+QL1Ri/ykViILoSAIglA1DSCgPtQXQjvZ8YyIiqLoOOUvlGxFcT9ANVW8lVhJeVkWGWF8SupvqCjpoKjygJpESKzqE8F/XElNZRVFFUgYqXE5rss4rEerZnQ658MnJLOMIAiCUHdkZmbecccdQ4YMGTZs2MyZM4uLi83rhmHMmzdv8ODBY8aMWbNmTV12KdQ1QocRpsPVJ/DoGVYFZzwuCt+KsjZM+P7ODofxsSKNcNsn7lmHRxwSR8/Dm/Vy9rgcB0slxxL4zOK5eVkmbc7Mot8qzDC+4M8s03UZjXzFlVfWW4ATFlRF0yDNYBodjWq/tMvKynr16nXzzTeXl5fPnDlz//79H330EREtWLDgjTfeePfddw8cODBmzJhffvmlTZs2Z9NznFBfCAVBEAQrCbaNsFu3bt26dTP/73a777rrLvP/8+fP/8tf/jJgwIABAwZ88803ixYteu6552rTYT4WLIQHDhx4//33s7KyIiIihg0b9oc//MFmq3K74da8nOoTwdcdiWdy4LQKg6sjeMk0RTZCToZ+VgdwldTiEcDhjQAjDY2SEVBRewEvq0KcZ1uNUZ+lwaPNejn5lXBYCWtqpjYp1moYAcMwnE7n0aNHFy5cOG7cOCJyuVx79uy56KKLTIF+/fp9+eWXtetvLbBgIczOzo6IiBg7dmxBQcETTzyRmZn51FNP1X03BEEQBEsoLCzs3bt3fn5+s2bN5s6dS0R5eXmGYTRu3NgUSExMPH78eJ31x4KF8LLLLrvsssvM/2ua9vbbb1ezEOrk89WrwEwVhECqDhTc6qMsr4rFgXwcrcFilZS1yVcxs0ynTYtRkaylfsQmGhoxbYQ+nYqKihITEwMvnn/++X4XmISEhMzMTF3XX3zxxREjRuzduzc+Pp6ISktLzf+UlJQkJCQE6QZqxkob4cmTJ7/44otBgwZZ2AdBEAShGgyDHT5hM7TY2NjMzMzAi+Hh4RXFbLZbb7310UcfPXnyZNOmTc3VMTU1lYgyMzNbt259lj3HsWYh3L17d9euXYmoX79+H374YVVimZmZPp8Xj/FQlFUErymBOzeyHit824ibM3HHNuLcl0NV9Yl6U38Dt6UxHXfxmcVjLpXUXsBnlhXKib8FnHIxrPobuEs2+hDixVJY6HAHPB6Pw+GoQag2NkJN0zT/OWcF9uzZ06ZNm8jISF3X33vvvZYtWzZt2pSIbrzxxjfffHPAgAGnT5/++OOPFy1axPrQs8GaOMIuXboYhnHq1KkOHTr84Q9/qEosNTW1Gj8aQRAE4WwICwP2N4Z5Osr8VzVffPFFSkpK586dU1JSPvjgg2XLlpnXn3766T179nTo0KFTp05XXXXV8OHDg3KPCFYejSYmJk6fPr1Xr166rle64MXExJCmcfLF4LC8xVDwLbbGSWLLSXuvqDo5CkcnZnXAYguZCjWLl+qTIankGbDDwl4FSXBqIw7AGgHOzCoq/qAgx5ZmQZ2KadOm3XvvvTk5OXFxccnJyf7rKSkpW7duPXToUExMTFJSUl12yYKFMCcnp1mzZub/v/rqq44dO4raJwiCEJoYho2fdLsGgcjIyLZt21b6q1atWrE+KyhYsBA++eSTq1evbtOmTU5OTllZ2dKlS6sR1oixY8F3jCwLGW6eYZgemUfuQZbjZhWBu4vbvZhukMFP16IokFFR/Q3OuQheIJ7xFKqZWSUZd/EOsEx0lldWwb81bEFNWGMQGcwyTFx5y7FgIXznnXf2799/5MiRJk2adOrUqWZTrSAIgmAVem2cZRT1RRHW2AjT09PT09MxWSvOsGuLimAv4ll9cJQEsXHcVplGuuDKsQl+kT9FRkpFmWXUzKwSQzUHJYGMKgy6LFge0TVjaMQ8Gq0X4ZGBiHFOEARBaNCEetLtSCMcrz7BibVi7ABw2wAexMbajOO55DkWMlYQG9pbt+YCJSOMCLwDnHT+cEpMOHclcWYWNzuFcd4+FZUmWVZSt+YGJfGZZQUycupvoM82KycnHh/pgGMu8ZsizjPgCHL1CbERCoIgCA2ZWgXUK+qLIkJ9ITTIYBioGGaU+pS/VIXpUVkIl5IYMjyOUFF9dhWPC3MEVDRrce0FluFPUQpTi+FZ9WFdP6jfb4YhGqEgCILQkKlFHGF9c5YJ9YXQpXk4thxFYXxB/3geuNlJjd7AAM9IyTJS4r3lJPBUohHimhPLQKXkrjgwMm0y4m4535cKIopZY8XIIqtoBOBHi5XIV6DQXwgFQRAEC6nV0aiivqgi1BdCg3Tct01RUI6KXKOWW0cszzWqSCNUk5mWk0VWTWobFbl1mIGMKmbW4kBGZblGleTWURHOizUnzjKCIAhCA0bCJ6xH13TcRogf4itKa4LHD7G24144kBHPIMqrxgcPbAShCfPK4bg04igZHniswjghXHhNSrxZvKukps4iPq1EFA5/V7g0DyjJqs/ug+1e+H3hEbpEFA6/3S64q6xzEXwEvIROgWAS6guhIAiCYCWGRtyjTtEIg4tmaLjTqCIbIQ7ussjqqpo4Qk6WRfi+PAa+F1ZiHcELPbLGFO8tp0qDEh9zRrQZw8OWPAZ+LKEk4I9VwhOEpZDhztv4zCoaAbsW1MwyhmZwDg/MvwliB+qAUF8IBUEQBCuRgHrLsZHN8jwRisqbWQtLHcFFLa/ZpiInJ3FMdKzoQBVwco0yOCdn1sd6BjgKtArwEbAznBVqxmgAXqNSfUIQBEFo0IS+RmhXscVkbVc4W2zcNsCAE8SGV/FmhfHhFjJUb2BtWvG9MD5WPCupgmeA5bir4r54OjGsD+E5aBSF8Smqv6FiZln1NxTFRyLtydGoIAiC0HAxDNKZCxtX3nJCfSEMN8LwTOpKkt5z9sLMzIEoNkYQG7oV9TIyNzLsXh44gCkMjjgkIju8xcZDOVkGXQOPDIOfFjwvK3Fm1ge7WbPMmXhomgP+VsGjM4nIgQdo4vfF8puFI1/tcEVGVj1C/C3AJREMQ+Mn3RavUUEQBOFcwZCjUcvxaF5WJXEVWO62ysmroqhmGyqM63lMp014564mqQfeVzy9EctM62XMLA5DNgz+ruAY3lhKuQpTPWMEcO1NhU2dOPflEy9IJqG+EAqCIAhWInGEluMjHydRxbmJItOjClTsmlnClo+V5dUnFGFToQ8pOhVQA8clW8kI4GhBDWZtCHGEob4QCoIgCFZinPs2QjlKFgRBEBo0oa4R6poPdwfHT29Y6Ybxwjr44Qlrv6QmmpsVyQvXtYHHygt7tRBvBJQUouKESMNxDjxvHRVlaRkjoGJmWYfDeLAHHpXBCjzHazbhr6GXGG8B/rXhgSthIUg9QkEQBKFhw68+YRj1xq3BpPKFcNeuXV27dq3jrlSKl3y4PTmcsWVjGJM1OCQAj2Pl1ZSBfazxsdI4scx4pdNIeGtVqkYfwnV9/PyAOHoeHtGPB+kTR33E9TxWYd4IRvgEHr3ACR5gZCpQohGqyNXgtjFUNzyOzM2pel0jhqHpTOeXepdZpvI3YejQob179164cGFxcXEdd0gQBEEIHQzDLEnI+2d1r3lUvsd57bXX3njjjbvuumvatGk33njjnXfe2aNHjzrumYnDCLMpqPHIyvisIj6Xk+CMYUrh+O5zFDJ4L1rKcBznBFPDkhw9T4nvvg6bZxQV5mU8rpxUFWUKZlZZPDsKawpcsJqFv7B4+gUWkUakimbPYSqfhmuvvfaHH37Yu3fv3Xff/emnn/bs2VMUREEQhAaImWKtIWqEJunp6bNnz37mmWc+/fTThQsX3nXXXdOnT7/pppseeOCBjh071k3/NLLZFATUs3wmFdW1sRaJZ2dGs9cj47+Sx7VezayS11BF2W1mPTiU4BbmrUVmGapvC2HNirnL5XI6nU6n0zCMlJSUf/zjH126dPnzn/9cB50TBEEQrMUgTTfY/6zuNY/qNg5btmxZuHDhxx9/7HK5xo4d++KLLw4dOrS8vPz111+fPn36uHHjBg4cqLp/DnIwM1EFH9w+pMo6Ant44m6rrCA2XBvA3dUi4FI1xNHgcY9BvLoWcWYWT6PMKsOEu61y8nuxihChts9wIxyUZFULwm2EXngKWIo+PgK4QsYqRIUbFFkxsjVSi6POenc0Wvl4vfPOOz179uzbt+9XX301ffr0gwcPLlu27LLLLtM0LSoqatq0ae3atdu3b18d91UQBEEQgk7lO5cnn3yye/fun3766ejRo8PCKpG55557zj//fMV9IyKyGzZNgdcoC9y5jmNDYNwUvrnCI8NYHoP4ttmHp7TgaISM2Dh4i816qnCNkGNLY+yacW2AlTUJxwdHPWqwRmh5Yhed0wFcgca1PJbXKD4Cdp7xsWbOeY2w8oVw8+bNLVu2rObPHnroITX9EQRBEEIIozaZZZRnsfZ4PGvWrDn//PNTUlLMK+vXr585c+bhw4e7du06Y8aMCy64AG+t8oWw+lWwLnFrHrwwryLNUYUTGivNI24ldcPbdkWedRFGFCyrJM+kC9aHFI0Abh9idQBP4KnoLQjXUQ2eY6RknKGUwzOryKvAYaAZpnBtiJVxlzNcwVTIapNZRn0Zpk2bNl1++eUHDx40f8zKyhoxYkRcXFy/fv02btw4cuTIrKys6OhosLXKX9rrrrsuPz//zOvJyclt27YdN25cnz59atV5QRAEoT5hhGRh3o0bN/bp0yctLc38ccGCBfTfs8wTJ0506tRp2bJlN998M9halbvXn3/+2el0durUKSkpKTc398CBAy1atGjZsuXXX389e/bsl19++U9/+tPZ30yN6KQrql2pAkWTb3kgI8dIqSSxSwhUOkXhhKgqMdPisB5Xjn8jHnVar0oTM05xzs0RCCmOHTuWnp7u//Hrr78eMWKEeZbZpEmT4cOHs9w5K5/awYMHN2/efOfOnbt27Vq7du3+/fs3bdpks9kee+yxI0eO3HLLLY899lheXt5Z3okgCIIQ8oRiZpno6OiSkhLz/4WFhTt37hwwYID/tzExMYWFhXhrlWiEXq/3qaeeWr58eefOnf0X+/bt+/TTTz/11FNjxox57bXXPvroo/Xr11999dW1vQsUj+bG6xGGwYf4LJ9J3LMrDM7Qz/KXw20DeHkzO6cCFx7vFQFPQTknQT7usOdVEOxFHH0ID2T0qKk+gSsurDC+cHi4yuEpYBWBwYcLN+axMkyFw83ipnrWCOCRr26tHG+2RgyDXU2iDgLqzz///Pnz5x86dKhVq1Yff/yx1+sdOXKk/7f79++/4oor8NYqeRNOnDiRn5+fmppa4XqzZs327t1LRDExMa1atWKtt4IgCEJ9JDRzjY4fP75NmzbnnXdez54977777ssvv7xbt27mr3JycjZv3nzRRRfhrVWyy0tMTIyOjv7oo4+ee+65wOtLlizxe5OeOnUqKSmptrfAQCcdP0bn6HlKDFSM4uC8eoRwlkW4VY5KzLgvRkVGzmYcF8WfFkZKD05vcYWMNQIqYHWAM7NKUJPutX6NgBJ33HpKWFjY2rVr586du3v37rFjxwZG9G3btu2aa65hJT6rZCGMiIh44IEH/vrXv+7atWvMmDHJyck5OTkff/zxDz/8sHDhQiJat26d0+ns2bPn2d+MIAiCENpoBnNx58rXjoSEhGeeeebM61dccQXrXJSq8hqdNWtWfHz8Sy+9tHLlSvNKWlrau+++e+uttxJRt27dsrKymjVrxux2bQg3InxwZhnLt9i4iY7VU041Pjj/CC/XKLwVZQR7sTb4aAfwMuIs8MoD+AgocvHFNScPK5QTTuCpKIwP9wDgOG0yHsIyzQU3qyT0GXfJjjZiOA3XgF4bG2F1v12/fv2cOXMyMjIcDseoUaNmz54dFxdHRF6vd9q0acuWLYuJiXn44Ydvv/125LNOnTq1evXq7OxsImrduvXgwYOTk5NZvaVKF0Jd1w8ePHj33Xc/8sgjR48ezc3NTUtLCzQZNmrUqFGjRtxPEgRBEOohQU66nZ2dfc0118yfP9/lct16660PPfTQokWLiGjevHnr1q3LyMg4evToiBEjunfv3rdv32ra8Xq9M2bMeOWVV8rL/885KDIy8v777581a5bDge+cKlsIc3Nz27dv//333w8dOjQtLc0fsWgJrHqElqNoL4w/gxydWFEYH+4JqcRGyEnZo2QEOBZlBipmlqWSckpVwJZy/OPVhPEx0xupyDClRNzGs4DXQC28Rqs/Gr3hhhv8/7/nnnv8ziiLFi167rnnUlNTU1NTJ02a9Pbbb1e/EE6dOvW9996bOnXqNddc07ZtW03TsrKyPv3001deeSUvL2/x4sV4hyt3lgkPD9f1elSJVBAEQah/rF692nQ38Xq9+/fv9ycIveCCC6pfyTIyMt55552VK1eOGTPGf7FVq1ZDhgwZMWLEVVddNXXq1OrX0UAqWQgjIyMnTpy4cOFCs+4SekNqiDDCddhGiOfdx4O9iBPA5IJj41gucHhcFC7JSnKI2z5LbaWgZIwei3cA3+Hi4W541Toi8hAaG8ep28eov6Gi9gKr5HoZPLPROmqgYlXj44TxoVPAGoFyODgv0ogEJfHajSwcwdUIa3U0ahhGhTydERERFZJ/rly5csWKFdu2bSOi/Px8Xdfj4+PNXyUkJJw4caKaj/jyyy8vueSSwFXQz5VXXjlkyJAvv/zyrBZCIurXr99TTz3Vu3fvq666qlmzZoHL4R133AE2XT2nTp3y+XxNmzYNSmuCIAiCCmpxNOrVqbi4uH379oEXO3fuvH79ev+P33zzzR133LFq1SrT+ta4cWObzVZUVGQuCoWFhdX7vOTl5XXo0KGq36anp7Nyn1W+EM6YMSMvLy8vL+/nn3+u8KuzXwi9Xu/kyZO//fbbsLCwnj17Llu2rJoc4RrLPgSf4SvTcxUVylBidFEBR9NVUpFRkecwx+ikyE6s4r6UWEk5KLETKwzkCzaKOhrkp4UfIK+RLS4u7vTp01UJrFu3btKkScuWLfPXbwgLC2vduvXOnTvN5XPXrl3VrHNE1KRJk19//bWq32ZmZrLiCCu3P+/du/d0FeBNV8WHH364Y8eO7Ozsw4cPl5SUzJ8//+zbFARBEFQQ9MwyP/3005VXXjl79uyWLVtmZWWZkQ9EdOutt7700kslJSVZWVkffPDBLbfcUk0jl19++Y8//vjll1+e+atvvvnmhx9+GDVqFH6PlWuECQkJeBNcli5dOmXKFFMLvOuuu55//vlHH320KmGX5lZRj9DDcG5khfGhR/OsspW4muXCC8TzFDK0vzF6HKNVGNxlsRwO9mJt7/EtNsfqw+gC54nF3VYZjyFu+cNhJUApg226irzMI2DLHw7nhWW8MG7O91vds3r16uTk5FmzZs2aNYuIGjVqlJGRQUTTp08/cOBA8+bNIyIipk+fPnjw4Goa6dOnz6RJk8aNG3f//fePGzeuXbt2fq/RefPm3XDDDWebYs3E7Xb/+9//3rVrV1FR0ezZs4lo8+bN8fHxgZm4a8fvv//uL5+Rnp7++++/VyXp9XoN2FNGEARBCDoGkc48a61e/oknnnjiiSfOvB4ZGfn+++/jn7Jo0aKkpKR58+a9+OKL/osOh2Pq1Klz5szB26GqFsIjR46MGDFi9+7dMTExjRo1MhfCZcuWrVu3bsOGDawPOJPi4uKoqP+tYx4TE1NSUuLz+ez2SnSp/fv3+3QvR32Cc3Jy9QFUzmLjhOVBbHg+RkWV2DjNsgxUwX8GmPcf/JllPa54GJ+i8pkcYSUvbAiMAC4J16lwu8PDazjDqEUS7TpIuk1E4eHhc+fOffjhh//zn/8cPHjQMIw2bdoMHTq0FlnPKl8Ib7/9dl3Xf/nll9OnT990003mxeuuu+7ll192Op1nmVamadOmTqfT/P/p06eTk5MrXQWJqEuXLr/Zf/N5RSkUBEEIPjWugmRqhKFXhslPamrqjTfeeJaNVLIQFhcXf/vtt1988UX37t3XrFnjv96hQwfDMI4cOXKWC2H37t03b948btw4Itq8ebM/grJSymxlPhueZxKNNGKpI7jVB090yUmTQW44PBGPDsTHijhBbDGExsYVEaNkGm5MwseKNQIOuHyjirp9xAnlxGMu8Wklomj4LSiG4+1YYXwu2PobAQdosgIZo+Bm8VqbuEmbOG9BuYYGfdZ3vF7vJ5988tVXXx05coSIWrZsOWrUqAkTJoSF8XIOVyJdWFio63rr1q0rXHe73UTk8TDe3kq5++67R48ePWjQoOjo6Dlz5rz55ptn2aAgCIKgCoO4rhp149rhdDovv/zyTZs2RUZGtm/f3jCMDRs2fPDBB/Pnz1+1ahVLYatkIWzSpElsbOzGjRu7du0aeP2bb74JCwurPrYD4eKLL160aNFLL73k9Xqfe+65sWPHViOsGRqnch5uG1AzUYx8kPXpvJdh88Dvi1UREcbyCDLOXSl6BpQYqBRVVMCxvLaMikhKRVU5bUEdK4O00Dwa/fOf/7x3795//vOf48aNM7O+6Lr+6aef3n777dOnTzcTeYNUshA6HI5JkyY9+uijKSkpZpCD1+v9/PPPH3rooeuvv96sl3GWjB8/fvz48WffjiAIgqAUg19fsA7qERYWFn700UeLFy++5ppr/BdtNtv48eNdLteUKVNefvllf8K2Gqn8IPXFF1/cv3//6NGjHQ6HYRgJCQmlpaV9+vSp++D3SCMKr0eoCHxKGcFe3OcKA69Yxkp26oU7UKiVwR1Q+Sm/rgAAIABJREFU4jXqUJO8kWOewcPdWGcdwfeIZpnocMufotw6rNSsIJySGlTKGAEUvIAoEdkIfbY5tRtrxjDYGmEdeI1mZGT4fL4JEyac+asJEyZMnjz5559/HjJkCNha5QthTEzM119//dVXX3333XdHjx5NTEwcMmTI+PHjuRZIQRAEQQg6x44dS0tLq7ToYHh4eFpa2tGjR/HWqlzYbDZbLQreBx0b2dTYPBgbFk5UEF6dnAF+4o9LKgrh8sFuq3gWHuLoQ5aPAG5LY9qHrK5HCGu6uJajaAQUhfH54OI2lo9AkG2EBlvDqwONsLS0NCkpqarfJicnl5SU4K3VoOG5XK7S0v/nidu4cWO8dUEQBKFeY5AW3MwyQcEwDJfLlZWVVelvXS4XKytZ5QvhiRMnZsyY8dlnn+Xk5Jz58XjrZ0+UEYHXI/TANg/WjskNazl4/BCnkjtFG1VW56gAHhuH12wjIi8sXKihadkbUXU1ViqAB2hGwjZCfFqJE+/lgq2k0ZyKjHjMH27OxEsnElGx5gQlE4xEUDKMGKYsfGbxBJ54AVEiKtEKQcloQj0Kozj5S+2wohmlBdNGSHwn97pZJXbs2FGh0lOtqfz75brrrtu4cePEiRO7detWTY0kQRAE4dxG5zvL1EH4xLBhwz755JNqBHr16oW3VslCWFZWtnbt2rfeeuv2229n9y7Y6GQwApjg0DSdt2EJvtUHT//B+HjOCLDA7ysMdmxj9RSX9amxDzFmlmP7xOHYvVBYBiqO9qYk2yru4cnIy8rpgV1DjyXwgWWluNLgEfAaDG/Yekq7du3atWtX1W+zsrISE9GTCao0n3VpaanP5+vZs2dteicIgiCcWwS3HmEdMGbMmH/84x+4fCV7nKSkpN69e2/YsCEU1sJyzaWiHqEiU24YbMpigltJg1+1jjjhiXEGWsmSpY5wKjIyrD44uB9gJEWBkqxnkBPxFvyoUyKKMdDYZI4+xFBccAM8jrKKjOgIsCoy+uAnBveWQKiVs0w9o/Iv7vnz5990000Oh+Pyyy+vkEpGvEYFQRAaDoYR0tUngkLlC+G4ceOOHz9+5513nvmrOvYa9WleH6wRavC2nWUfwitF4JIsfUiH93f4FpvlN4vfF+7ciJdTII4pRVcQ7ka8UE60WVZaE1wn5hiqGSOgYmZZFjJdQRgf6y2wwxVIcD2P9Qzguj4e8iiYVJlirawM9QIXBEEQzlWMUA2fCCKVL4STJk2q435URbFWhO9uImDzDK43EFGEgTYbpeOF0Bib8WI4NA2vxhfJydyIh/El2tCxOmkw8j74CI0MK4MrsUUQI4QrWkfvKxr2my2Cp5WIXPATixuq8dKJRNQYntlT8Mx6OdX4SjW02SjC424Zz0AC/PVSQOjMskzaeDhvMeXjzdZIaAbUL1myZNq0aVX99tSpU6zWqnsTfD7foUOHioqKunfvzmpUEARBODcIzRRrHTp0qL4wPWvZqnwh1HV95syZr7zySnFxcYsWLczivw888EB5efmCBQtY3T1LwikCjwzDD/FxayIRafCk4sqrIlMrq+I2Dm6eKTNQ+xAzjA8Fz63DspDhI+A2lFSf4JifUfCbIqIyHbb8KTDqE5EDD1GFx4plpHThzzY8AjwzLfxsR2qMpEU1YvC9QOvgZLRfv379+vULVmuVT8PTTz89Z86cu++++/nnn/dfHDZs2NKlS8069YIgCIJwblCJCuX1eufPn//Xv/71T3/605o1a15//XXzeo8ePYqLiw8fPhys9G4IkXqUj5kGJuhYXo8Ql1UUyIgXriuErSOKErvgVesUhfGVMexerFBOhuoAwqpHWAQbXxnV+DiTEAHb8xg6MUfVKVFQjzCME8ppwOc9DjjoE4MdIF8HhXmDSyVvV15eXmFh4ciRIytcT0hIIKLTp9GsyoIgCEJ9xyzMy/1nda95VKJAxMXF2Wy2nJycrl27Bl7fsWMHETVr1qyOukZERA7DYYfNabiJTlEYnwcuaMDSh3C7F+6Mit8UcWrRueBdcyTsh0kcU4qDUaWBoQ3gsXF4RUYHXE6BOCOARxyy0pq4YU0XV91YvtOc+huKRgC1CoXDM4vfFHG+tXA3bwSdbyOsd2GMlTyIcXFxAwcOfPbZZ4uLizXtf4e+oKDgkUceueCCC9LS0uq2h4IgCIJ1GPUv1yiXyjcO8+bNGzx4cOfOnc8777zCwsJbb731q6++cjqd3377bR33L06LxJ3Qygw0zsbO0QjL4A7gtei8cGAccQq84ZFhZZxISg+he+GTejYomaoxLM2RsJIRBW/GyznV+PDwRLxuX2OjKd6BcLj4gwfPTMsJYjtFh0HJplpbUNIBR+gSUQxs/S2F7ws/wCAip5YLSiYSemwWCUceE+e0o5Gd0axAVXmNXnjhhVu3bh0yZMi2bduKior++c9/9unT56effho4cGAd908QBEGwEOO/p6Osf/WLKhWI9PT0JUuW1GVXKqXM8DAq1MPmGR9Hc8eDjRzwphUPeWThhfU81slFGNzbeFsKKMkqfYCbVF24MY/ztuLV+KIJddhj+UyqqD7BKhAfpzUBJfGZ5VWfgA9R8LFivYYxhB7MMLKScg5m8ODjQrxQBfK5tfAaPTeORgVBEASBGnL1idChjFOPkAE3KgYDd+8kzmYc7wArMgzHDpseEwy8SherHiE6Am6G5U9JRcYoA010yXoGWV6+ICylPN5oBMviFeoZGqFLwcyyKpDEGnE1CxERZ2ZZxxK4VzyrtAsC9yu4vuXc5rgvC4IgCMK5R8hrhLZiny34JjpWGB+eriUK7gArx2AZ7Nvmha2krBw0uLtavIIM/cTZ4eLhbrjhkzjFOnDH3VJO7QV8BPB8s/i0ElEsXKyjCJ5ZRiYmIg9jZtHjlghOKGc0/AzgOWjw2ETipTdilHapEZ3YAfJyNCoIgiCcO9Qi6fa54zUqCIIgCMQvw0SiEQaXQjqNHwjEaah/Myu1UjShNU2i4Eox4RrjaLTUwE9a4OMjztFoBOwElBqJjkBZOSOe3QM7SpRpxaBktMEoVRMBCzeywxH9OudYDPazx11gWIV5UyPQg8FyFzpZLhurMG8RKBkDp5x2wJ5NRJTkQEfA7UGPfEvgRA1E5IFPXPN9h/BmBQr9hVAQBEGwEDkatZ54SsIL8+K5bnk1UWEf61ID3eOXc44ONFgWL0LEAvdcz3Ghe3w37NdDnHTD0bCPO6sCDj4C+T700WLFBbF6C8Kaglw3OrN4Xgsb56bwTAX4aQdrBE56UIUMHwEHJ4wKL8WVaG+NN1sjElAvCIIgNGgMg7g1Ya2uIcsm1BfCKD0aL8yrptgt4eGhnGhuJXAsf4xHFY/TdxLqt816U/DZwk8FWF3AHf1xbYAFJ95GSfqFAgUzyyzMi592wIXbOCNQDKfUx0eAVW8Zr1rlMBLwZmukIRyNSkC9IAiC0KAJdY0wieLwnNfFsImOtRV1wUGvp2x5oKQXLmxERMl6c1AyAtYIOQmrqMyGagM53t2gZPOwrjUL/ZdoHfXuw3MauDiqm9N2GpQsNk6Bkk2oFd4BPPQb1/PKOS6Lufo+UDLV1hGUjOE47qoosFVoK8A7cFrHC1G1AyVjOG6reLndRDua/QDB4Nv8xEYoCIIgnDs0hKPRUF8I48IcjITLHnQvbMMdMYncsPpYYuSjbeqMHEjJGqoRhsFJs8o5KqkLTppVXJ6JthmLlm8lomiKASUjNPSR9hqMt7WMCkHJAs9RUDLJkYZ3ADcm4Wn23JyytIXwzDaOaQlKxsEuvkQUCc+sx0C/B1zE0IkLXGjR6cZR6MyG6YwRCNfQtzsxPKg+xgbBBaD++xfiLCMIgiCcM+ik6cx6PVx5ywn1hTDPU6p74aKs8F6YNUt4apvGWjO0TTunqg6suuDWETwyjzipOprFXgxKshK74KcCRYRqOXhRGyKKM5JAyfBwNO14mM7YtnsYFVzxmEvGFDSP6Rf0ZllOm4XwzOLlqaPhB5uImkX1ACUjDfQZYPmZ49bfg2X1TSOzmlBfCAVBEAQLkThC6ynRynxwAg6OlqMkjC1WRzeYzKKsqO6Cb7FZHcBdFpsZbUBJljaAz5eH8C02YwyiYe++GAM1Z+Lu0MR5BtTEXFKqgVr+8BMU1gi4YKs2/j0QaTC8K3EPT3wEeIV54VfGZWN4ACDUWWFeXdcLCgoaNWqkcdw4zh6JIxQEQRCqxCBNN3j/qg+fWLFixbBhw1JSUm6++ebA619++WWLFi26du3avn37LVu2KL6t/0eoa4T7veu9XnQf1Dy8u4o+NNLRohaXJ6EJHeLCGDvBlbmo06bT5gQlYzkOe63DUE339o4nQMkPDjTFO7DXjYbx4QF/8XpjvAOXxKM2wk7xaE7Ofxxm7JtPwxFveAaWFnaGhey2DqhH9PJsdKx+Kz+Jd8BpQ4UTYINuv+gmeAcuaYoWNllyEJ2Cw8QYgTK4/sbh0o14s3VPbGzsXXfdtXbt2qNH/8/Fury8fNKkSe+9997o0aPfeuutm2++eefOnXWmF4pGKAiCIFSJYdTmXzWMGDFiwoQJTZv+v63wqlWrkpKSRo8eTURTpkzJycnZunWr0vsKJNQ1Qo+v2OtDNUIdTgjJAt+T4HpeQjjDW4yRZRL2hORUPqBwGzoGTeNgxcXO0AjxSVD0DMSEoeOVGIFqhHaNUSqEUy8FHSsHZ7vdJBZVRyLtqELGsiT5GDOLNhzFCbdLjkKDDsM01GvU4ATc4SOg66jyilA3AfVZWVmdO3c2/+9wONq1a5eZmdmnTx9+S7Uh1BdCQRAEwUJql2LN5/NlZGQEXoyPj09PT6/qTwoKCqKj/88dKS4uzulEDT1nT6gvhPERrX12dHuBRwVxcvkzYn2W5KEn/qwwPjwPTqSObkVZlQf2uNH8mXdsSoY7gFoTiUiD5ysetg8ZHKX4X/loFtl/n8a1DIZrH15XBPctzPSiZj8iujcDtX97NXSs8GklosYGbs9D35fvi47jHVizC50CjdBv8HBiOO7aCX22W8QOxputkVpohD4yysvL77zzzsCL6enpf//736v6kyZNmgQunPn5+RXOTpUS6guhIAiCUL/QSIuJiWEZ+bp16/b888/rum6z2YqKivbt29etWzd1PaxAqC+EoyIGGLB5Zn8ZejKO555gEWEw6k3j4PlicEsSK4zPDqcwxVNi4mlRiZNXBc/QzwKfWTw2jpVVBG8WjzjE69sRRyXFx4pVIN4ORz3ib0E452lRMbOsMjiROjoCbeyd8WZrJOgB9UePHt29e3dmZmZeXt53332XlpbWuXPnIUOGNG7ceObMmbfddtvs2bMHDBjQsSNaxuTssWAhPHLkyIoVK7KysqKioi677LJhw4bVfR8EQRAEkOAG1P/6669z584looiIiBdeeOGqq67q3LmzzWb7/PPPp02bNmrUqJ49e3700Ue17m0tsGAh3L59e2ZmZnp6ekFBwcSJEx9//PEHHnigKuGFx3qHhaGbprtTNoOSR0sZ+lCkDd0433seWnnAwck1+upvaOG6kx7U7ISXkieirpGNQMnbOqIj8P1RtKQGEa0+iUZSNrKjrpgR8LQS0RQ4PrIR7Fv48i+t8Q7keNBmS+GaEu0c6LQS0b3nHQElf85LBSX/fYxhIYuxo19W+Lf2jW1QV1giapmAWv5e/AXNwuP0MezEKeHosz1r8Da82RrR+SnTqpcfNWrUqFGjzrzeoUOHlStX8j4pSFiwEI4ePdoMFiGi5OTk9957r5qFUBAEQbCSWpRhUtMRdVhpI3S73T/99NOFF15YndAHdxFsSCjy3FmzEBERuXSGGxT+EGQ60Rw0YRyXxVIf2lsvXImN5TNZ7EE7kFWApms56WJYR7yw3csNz6zOebl/L4RzBpWhGSlL4QBZ4lj+cEq8jDaz4Gc7pwy2ERqook9EYTqqweMze6gETQxLRC64WkiZjs4sqygm/j2w+wh6hlRlNEMDQ9VC6KvsJbfb//dJ2rdvX79+/UpLS3v06PHdd99V1cihQ4d8ib6wUHfoEQRBqJd4vd6wmr5hjQZQj1BjpTYA2bBhw5AhQ868vmXLlu7duxORz+crLCw8fvz49OnTY2Njqwouefzxx5e8vhnfMyXpKaAkK4DJBRtd3BqaVcTG8Zm0G6hwOJxnEq9jTkQlGpovJtZANacijRHEFmug1ix8BPDJIqIyDTXR4R3A0wARUQRc4g7XHUvh3JVEFAVXGSzW8IS36NNCnByqLnhmFY0A/mjF6IySkDp8inPU2ANKnir+ucZ8np/O+HT1otVggyZuw/338r/XZUT8WaJE2+rfv7/LVd2jYLfbGzdu3Lhx41mzZvXp08eMHTlTLCEhwWaz+TjHmIIgCAIIktXaqMMyTFZhwbFjcXFxbOz/7oM2bdrUqlWrSldBkza+troPHdVTcIZ+g2NxaQGntMDtbmUGQyGLtaFGlzxCIymbcjbjbaNRP8B1pcdAyZFRXfAO/F6CBmYd09DkPikGavQiolgbetiQ60OnoHkYQxvI9aEqaYyGumK2jkRviog2l+eCkkMj0CC2w2UMn8kjGuq429xA0xs1caAPNhHtg0ugXOBAPaIzXYV4B2xwnrNHml2MN1sjRrC9RkMQCxbCu+66KzMzs23btseOHduxY0cdx4sIgiAIQiAWLITvvvtuRkbGkSNHkpKS+vTpExdXXWG8UakOgjXCxcdRNQuv7EVEFySg+7tG4ai3WG4ZavIhouRIVH/9Jg/VHbsmMJLgTO31Kyi5+YcWoOT0/oxQpze29AAlDxShClnn2GZ4B3onogrZNzloocdLmjL0oX8fRWupt4pBX+rbumbiHdi5CT0XebAHaqB6ewcjAcqeYjThbacY9CEc0wpNi0pEr+5GPaKvb4vqjq/vZZwKRNhQX4H7P9+NN1sjcjSqhPDw8P79+9f95wqCIAhcgp5iLQQJ9dCEbaftBqwR4q6YYcQoBbejAFU0o+2oM2qJj5FnMrsEbRbPKrKviDEC727vDkp6NNSS9O7P1caP/n/2FqJTEKahmu6+IoZCVuRBowOPudDYuDXHUSWPiE7BFea8xWizS/a0xztQZkNNdB/uQvW8/UUMS3m4hk7BgWL05frsEKO+Qb6B2vM+O4San52EujUQUbgP/br+5ib0tGPszzXLNAQboVSoFwRBEBo0oa4R7vLm+rzo7oJR0MBgbMb3a4dASTuce0LTOAURCdXzIjU0U8bvhKpuRLSjAN23xhFqR1nqRC1JRBRFqOHNAdcoyLahDq5EdNANP4Swi+8hHyPda5QNNSYVEKq47C5mhHnFwjO7vHAvKBkBK3lEFGmgwtm2HFAys5RxMBOhoV8a37vQjLsxGsN5O0xDv67v3o++3WMxMbERCoIgCA0XsRFaT4lW5IMTcAyN6ARK9klGdSwi8sBJDn8+jaojUYzEMtQjETU7HStDLX97ChgqafPoNFCySQTqN/trPqP6RBjc2T5JqOWv1MuYg+35aA+aR6HBXp0TGJk29xehbsa5cKsd4hh+szY4Z9b202izyRGMRFwXNEbTtRwpRV/DvYwoPuoUj0qmRKIetjnlDOftradR/XWvazXebI2I16ggCILQoGkIzjKhvhC6qdwHZ4FJiUKH/8IU1IpARB5YdcgpQ+uQxTkYqW16Nz8MSu47geYKOeViGCfS41AFunk0avc64UrCO4Brrxcko5FhxW6G3+yxMnSP3zYWVUnxaSUi/ShaT8CA7d/nN2a4LOpwWpNjpWhi2OZRjLcAf2ejTqGTdcrNqD7RMR59tts3RkMe98JdJaJdTvSJ9XjR/EqCSagvhIIgCIKFGLWoRygaYXBpZCT74EH9Ih8Ndfr1Z3R/TUQlOno07zHQ/CN2TuDKD8fbgJL4BvsUoZtWIvq1AN04R9lQRdOpM5L7lMHp/HcVoNkjC3VGHKEXTuL6WyFq9fn8aFu8Ay640mQJXPzhZyfDadMB+znn66jlbWMxI45w4yn0uOUknJeVYA9bIsqEAzS1g6iV1MXJOXzChvqCtokdhjdbIwbni8X/J/WLUF8IBUEQBAsRG6H1xOvxOjyosXDe/d3a73gfEglNZu+2obqjjxjVycMMdJrcGqrlJOtoWBgR5dvQjfNhDZVspDGKPyTCcYR74aDPJsQwz5TDA+uEazeGcd4+G6yQxeiof2mejVESEq/ImKChj1YCMVTS3YS+s80JdUgu5dSkPASHJ+I1Ke2cZwCvquHTGS9XjRhkGEwdjytvOZJZRhAEQWjQhLpG2D4yFs81+txlG0DJQV8yXBbva4NafT7Ohqt4w3ZHIroiFdV0vz+OKpp3d0KNXkS0/CCakvEb935QcmBkB7wD919wAJS8cQs6WU92ZcSSLt6PbsYzvWjlgaGNGA8hHh04sAmq5aw7wajG9x9PBtqByHag5AM90Bw0RDRuE/o98EIv1FfgjZ2t8Q5s8qDG1wFw/U6W7W1ihyOg5L8PoYG/CBJQLwiCIDRsauc1ykiWYD2hvhAW+3QDzjX6WxaqZGjEyLKYXYIaXdywDxgeHElEuWVoIGOZgWoD+wvhPBlExT60txFwSsxiL2MEduahW2yD0AiqAwVouBsRlfkYNl2Q0wz7FBV40A5kl6AGqhLOFETA9rwi+IXddZyRXUgj1ES3Bw7OYz2EuD2vwIOOAOfzafdp9FiC9WjVSC28RrnyliM2QkEQBKFBoxkhHPr4wgsvfPL8Drz6RLENddhj3XOEgWqE4QYjcyCOR0MVzTINL2jAGIMYA1Uf7Qa6tdI1RgdKNdyiiTbrgF37iOMHqMFHQl6NYSfmjAAKPq3EmVncY7DUhgf8kQGrGbg7bgTsYUucoz6vhqrvpRojmhYfgQQdNT9vL1lSo8x7j6344o3VYIMmHsO9xvax08k4eLOWUD8aFQRBECzE4IdD1LvwiVBfCEttJT4bOqYdDTRfzKVoSk4iouU5qMeeC442Y5RO5ESG3ZCCWkcOlTA6sKMYVTQj4ZJpTjgLDxF1s6HefRclo0/Lv3BHTKISGyocpaO6owM+aSCia5uijrtON6q6bXUypgDPhZQPK69tdIaNcGgK2oF/56ImsgIbQ89O0FH7N37YMS4ZnVYiOulCldIvS/fhzdaIeI0KgiAIDRopw2Q9rOoTnePR2xl/wRa8D98f7wFK5sFeo2EGQyFrbEeTHF7TZRco+csRRgRVdhaqu0TZ0Psq9TKevfMaoXvhq7vsACXXnzgP70CJgWqEEYTaiRPC0PBQIhrXaQ8omZOPZhXZX8TIrYNbyArg+p3tYxk29au7/ApKZpw6H5Qs8DE0wmgN7W2EDR2BsR3QAFkiys5HvUb/uZ+RM0ig0F8IBUEQBAsxyNCZPpVcecsJ9YUwxojHq09sdaLb9sd/6I334ZSB5s/ETcS4IygRnYCz6c9c3w2ULINDA4nIqaMdKIa1ATdnBDacQo2v+35CtYHjnPIXNthChqevdHkZXqOzNnUBJT2wfYZTpYFsuE4Im8h2FjE68BQ8s0c8qJ5nhzO4ElGBgaYigp1G6YWfGfmVyuF3trHGML7WiFSfEARBEBo0hmFwo+xCOSqvUkJ9IYw0InEtu1hDt2y73Xi8Hat2INpVVmYZn4YK7/WgeVV0uE0i0uCNsw/eDDs4MZenCNXe8j1oV/GAP5awi1DlVdMYHdjtRU878GMJG2zKIiIdfmLxmcVfWCIq8cDpUuBxxRV9IiqFPYdtBtqDPfC0EpENDuVsZKBlQREks4wgCIIgnOOEukYYR1E6vMPtHIuGcA1oyrAPffQ76jOZY6CpbfDcE0SUpKP7uxvRQEraehr1RCWijaV5oKRTQyVTDEaC/C4RaKaMCa3R4g+LMxm75mM+1E5cakMPGxr7GMlOxzdDH8JDJehLvboInSwiKtDQw4YUowUo2SGcMQU3tUc7gJcKOeRlVKh3aqgrZmMDddy9ugljBNw6qmhuPx1MlczgO7+Is4wgCIJwTiFxhBbjNXSGjRD2Q8wpZST1cBvBP/FmGahwg+LxclTPK2F4LDJ66yBUKWe9KriP65ESNP2HW2dMK254s8MRoqxsqyfL0WaL4JllPYQqZrYMLjVKRIeL40BJFzyzrExgeApTvNHTLoZxCi9VEdyvLIMM/FjOhCtvOaG+EAqCIAgWohtyNGo1xVoZvnH+rRwNS/o1lzFPeAQVvsFmZZYp0VDXsn+eQEeApQ3gfrONDNQ8w9IJs7yoeSbzKOwzqaZyaARcp8JDDK38q/xToCQ+sxx3aIo3GoOSOsGlE32MBChvHUZnFh8B1hMQA+caxctE/FCATisL1tstUOgvhIIgCIKlGFJ9QhAEQWi4NIQ4wlBfCMu1Uh98NIpXuSz05eB9aGbrBEpGwYV1WJG85XDWrlzKBCUbac3wDjTS0WOxaPhgsAg+7yWiEhvq5n7cixagaRaGJi0joig9GpTEizO7OYV5j9uyQckoQsvtJumMpNuRBpoivBie2RIbI4opx7MblGzm6ApKRusxeAeiDdQTzQXPbK7td7wDOPgzgGDwnV/qnbOMBNQLgiAIDZpQ1wi9mgdPMOY20FjmMi8adk1Evgg4LANOrcSyZuO293KvE5T0OtAQdeLor+EawwkIxwv7lZR5UO8DbxgnggQPn1CzuXTBKcIdNlRxYR1L4DOL24fwaSWici8aUO91oFnuiFBFnzjFtN3wfZVzMr/jD6ENroaGfSrbRljvIglDfSEUBEEQLMQgA880a8KVt5xQXwijjBi8DFMEoSa6uAiWdQTdNuL7Jh/sYk5EYbDZqYUDLVUTDhvziMgL97aAGJY/nFg4iXDrqH6gZLTBsA/hnva45Y/l4Z6qpYOSdgN9qfFpJaKi/2nv7IOjKq8/fnY3u8mS5TWkSUDeQkKrAQNI0KikiZCJgKEwWEWHNtAyoNip0wrMdOqoU8bg+NbOFMYoHlN8AAAYoUlEQVS3VpHWTgU6ym8iNhBbFRkopUReBI3kJ0SCCSSRGNiQ7Mv9/bFMftTh5Xs293J3s9/P5I9luSzn3vNsnuc85zzfI3ATIvjOcLeKyGjv7eCVXtizDk1M3Amn6vEMWaZD0YYJx6k5nXVVeKCeEEJIQmNRsUxTU1NDQ8O4ceMGDVKI7lpErE+EyUYyLlKAq1t54O1+0bRaxZfYqoHlgq1ND6GZP7ywTVQ5D7ixjgeuQhSRFDh+TQuj9XLnYLeKxrMB+MHiw1VEBofRXxa4njveMUpEuhSeRZ2Fu1VEhsLh41k4eFXFxHjxdhIclONuFc0WQijmA7J169Y9/vjjeXl5hw8fXr9+/ezZs+21h1WjhBBCLkukWEb7c4UPbG1tXbVq1XvvvffBBx+8+OKLDz30UCikWJFYQexHhG48Ikx2oLczwKW48UAQjwbQ+lK8ga1ojqYNdKFL7K81esedTnSJjR/l9MCtakTEDS+x09ywwllA4QI8zMKF0/BAX0R8DjSA9huoAX4nKsgnIp0OtCQbjwjxgS0iQz3oEwh0o87CK9JFExM74WLU/rBbRZN8DZovuq0tlrnSr5eqqqobbrghPz9fRObOnbt06dI9e/YUFhb2ysrewYiQEELIZTEcEnYY2p8rfGBDQ0N2dnbktcvlGjlyZENDwzW5lcsS6xFhtwTxdFoIXgeF4LY+IhKAowGF3rGhWILgy7GOEJr1wYNX0VibIngppqJqErf26yD6BLo1TwAnSRRRDs45A/YsnPdyakLSZLh2Gkc1CNsCaKSLe1Z1PA6PdB3weeKzcPgumi+MuUWbhoSjOD4RCAQ2bdp08ZtDhgyZPn26iPj9fs9F8b3X6/X7FZsTVhDrEyEhhJD4whAjGAx+ayIcPXp0ZCLMzMw8cOBAz/stLS2ZmZnX2sT/JtYnwm5HF641asBXfqNZ4OA1YLhUh2oxjjfmbYM1OVUli3g2ywcf4cJvSjRlq80OdGmJn84UTc8mN/yxuGCQiHzjRFN0Tjh8V/UCc8ORLt6GSVW2ehZOUuIuULXiwmtc8YDsG+dZ3ACNXIuZbZgMMVRjNfKPvF7vxo0bL/l3U6ZMWb16dTAYTEpK+uqrr44fPz5x4sTe29kbmCMkhBByWSLFMtqfK3zgbbfdlpOT89Of/rS6urqiouLee+/NylL0ALCCWI8Iu5xdIaf50oWdopD4GyTfAa/Em7KqZB6DDnTh3OFAO536RCHq4YbzQ/0s6FEgmsN5ZxxN4JWDJAM3APcsXuCq6j6Bl+Pi+koejbYO3n2iAw7KA/DAFpE2B9oxZrCg+2zJcEMJEUmGnwB+SPecA93CEc0WgkoxB/h/jbCmvFYAU6uqqp566qkXXnihqKhoxYoVvbDOHGJ9IiSEENLHGDp06LPPPmu3Ff+PnRNhZ2en13uV1avTcBq4sgx8jtAjipWgJpGAb+IryrrwIjQ3fCxJtWbUaKiiK0dVWRseQLvheEgVlOPgiWoVLjhFh9+XqmYS9yz+bVENQo/Cs5a0QNFUTqIPVlVjrPGXyTnCPi+6bU+OcOvWrbm5uWlpaT6fb8OGDbbYQAgh5KpEimW0P3ZbrcOGiHDfvn0LFy584403ysrK/H5/S8uV2ox5jBRcWQZfi6eKD71UsxDTdGJTyJrga/wBcCt5ZTSAWnsOPsKl6siI1wGmGWjmT9liDb0YL3BVLdp9YXTE4nelGoS4tg5+Xyq92XQDrafQnKJTjIHzcEYTfwK+cH/cALtIhIjQhonwt7/97eLFi2fOnCkiPp/P51PMSYQQQq4lpkusxSA2TIQHDx4sKyubMmVKY2NjUVHR73//++9859JlmeFw2BNOggNCqxqpnxNUdR6PBgxNFRYeD/WDq+Dw5a2IdMEXn4crBvvBIY6IuOGsT384+3sWdqtoFESDcMWg6iBjP7hsFddV6dL038Av7hdGi1Hdmt8/uGc7DFQUVCVto+m/gXrWp9HrwTdRVNstRCyaCPfu3VtTU/OtN51O56pVq0Skubl548aN//jHPzIyMhYtWrR8+fLNmzdf8nM++eSTUMhlUeqbEEISnEAg4HZfZdo2HGHt8QlujYqIBAKBs2e/rZjgcl2Yz9LT0+fOnTtmzBgRWblyZUlJiWEYDsclljATJkz43/+pCwfNT9HpAne7V1caY/FnZRE2P6w425GBiav7ssRYxbfbmjGo+VTbvwXos7rqLCgXimV0bZK019uOJRNhYWHhFXpq5OXlBYMXdiSCwWDPBEkIISTWYLGMJSxfvnzBggXz588fNmzY6tWr77777kuGgxG6HN1X7ujx36ALsfMakUN8has4caiRecRrkc/CaQzVsh3vPpGqESvBCQqayzkjqCKlRYELntBV0anI58HnbjWDsJ8V3SfgzKuIfA2PAYs8i6sL4fg1aVr7UGuN8vjE1Zk2bVplZeWSJUu6urrKyspWr1597W0ghBBCItijLFNRUVFRUYFcGXB0490n8JpdlcghvhbG6wBVZV14PHQe7jmuUlnEo5xk+MrzmrUwXt3nd6By/v00wSvu2SS4tkt1jO88LM2Ki5WojvF5YA1V3LNBTdHmOYVn0YJkvLGMaLRG8eHaCVdZiwhesaI8I3u1TzPCYUOX89NebzvUGiWEEHJZomrDxK1RUzGsqZlUfaoVCQeLqsrMXQnGJ7bXzcYNqkFod+m0xJHHNNrEFt2U6R3qtVWjnAgJIYT0FaKICDkRmozH8OBao/gqSJUh06zvUPer1ld4MSou6qFa4OPrwU7FwVvFohXvpd7fGAReqQxxrFCRVTwBfMTi94VLyIrOsyiqFN0AA9XRtSh41fSPRD2bYqAtNTSfStTE+kRICCHERqLaGmWxjKm4Dbei+wS8FsRL+0QjyxmCyzs1hyMVC+cUuLhR1R49AN9XN1wxqFoL4/7ywqV9nZongHsW1xp1WVOyGILbRCjHAPotSIGDV5fma+iFj/HhX1hV4S4+tvEaY/ymxE6tUcMwlFujyuttJ9YnQkIIITaSCDlCexrzEkIIITECI0JCCCGXJYoD9QYP1JtLwBHAlWVwVAJ/+HY7vjWPq3eKZp8BV6RU5RDwXI5XoUipcCte39gBK7BYVFuo6TKoeAJ4S0j8vlRaoy7Bc7rofanKVjtgERaLPOtR5PPQJ6BqC2oX0W2NxsDBUwWxPhESQgixE8PQRnicCE1GGRHCh7009XLJcH0jXt6pKuvCSxbxNuIqnckkeJzgvbnxEEdEgnAl5Hk4bsDdKhrPWqQ1GoA964KdpWqU4YafAD4IcbeKxrMpCnFgRUyMq60G4PvCu96LZmdI20f3qv8vi2UIIYSQvkysR4QOuXyvwl7g1KwENTlKi7Qf8PND+MrGkq0LXDtR9aRwFzgdqGfNPmt1AYvUI3HPWjRcrbgvlQuccKht0aac6uwviOoJKL7dplpqiBHWniOUcHz1W4/1iZAQQoitGFqlmLjbGo31idBteFywsgyOaiEWxrfmrWl/gVvrhUU9NKZKCH4CQTjn4dRsy+PRgC+M9qLD3SqaeAhvHqkahHhOFzcVd6uIBOHMHy6Nq0zR9QevxD2rCnO7BS0swD2r6nqviXTNjIoNQ68sw4mQEEJInyGKYhn2IzSZJMOl0RpFUYkcdsElpng0YOi0RlFr8TrAgE5nEpevtERr1AWHj7gmp65sFX4CuNSnSmvUDY+BEFwxqBwD5muNqnYFUmDPnofvS3WQMQgPGPwsKV6LK5rfb6oHSyT2J0JCCCG2ot4aZURoMmFH2IpiLdUn4okERdZHYwF+LR6OqLIjiqyPKI6m4eDWBhxwAxBdmhbFCWsG6VpCwnGeYrgaChOSHOZ7VuUCvAWKZh9PYQB+QBP/PYB/YS98MHqdqecI9TlCToSEEEL6EmF9f8E4ayIc6xNhwNFthdaoqmIQz+XgW/OqTXzc2vNw0aZKZxIv2vTC2RGVrgqey8FlTVTt0XF/4So8qnioG9cahUNSVaYc1wzCM+WqFF2XEx3beIoO3+oQzRPAv7CqRDU+u1h0mLUPE+sTISGEEBsxoskRxtlMHOsTYVjCqoUzCL5oFc3C2Yo+FSKikfNHxyse5Ik1DQ1UOpNWqMiq4iF8DOKxY1gTD+FBhkuRSVKA90sJwmJQhkYSMwhnfxWe1e2L4J5Fh4tFh1lVHwv9z+oP5ERICCGkzxBNsQwnQlNJMtwOC5RlVMWN+MJZo7SpGFh4+Ijrj+hKFuH70uQ8VGWr6GLcG0Y7Dyi1N3BlGTzOU50lRUcsfl+qvRb8NC1+X6qunJpWl5aAl63iqBqA2EUU/QjjbiLkuUtCCCEJTcxHhJLktGBxoVqK4kfT8Co41QoLz3ngQhWqFB1+XwE4ItT0+1Yoy+Clfd2wW0XzBEJ4KkuToHLDX1U8T6zUVUEjQnxbQpWo1rQDtKRsFRfiwT3rUW1Nwec+VdWwAIZEszUaT1FWrE+EhBBCbCW6c4ScCM3DEMOKMzEqtRrNf29Rizv4SsV9WbKJr1rj4yi0dazpPKCoB9bIteBorLXIs+b/XlO5wKKeEjh4mIUn9XWmwiPLbFkXVo0SQghJZIwotkbjjFifCAOOgBXKMroUHbzjj7eQtkhZBtdVcWoSVFak6FTKMviRO78TTVIqtXXQJ4An85TKMmiCChc7xd0qmvSzIp+qGQPdTnRsa6SgFOG7G8594l9Y3K0igodZVpy97tvE+kRICCHERizKT8UUsT4RGhK2wgeq3tx43gtfYFuSR9IsRZUpHzjSxctxNWWrmhwh+rEW9WzTaAapEtXm60ziGxii8SzeEFGVeVJ41oEryyi+iHj4aCiu7KvKMnFGrE+EhBBC7KaPR4QOwwLdFrN44IEHqqurc3Jy7DYkenbu3Dl58mSvV9GNPfYJBAK7du0qKiqy2xCTaWlpOXHixMSJE+02xGTq6+udTueYMWPsNsRk9u3bN3r06CFDhthtiMm8//7706ZNc7ksKcD+FtnZ2S+99JLpH9vR0TFq1Ki2tjbTP9kiYnoirKur+/jjj+N6oB87dmzUqFEOWIY4Xvjiiy/63i/Wrq6u1tbWYcOG2W2IybS1tTmdzkGDBtltiMmcOHEiIyPD7Y4DlTIV1/LLNXDgwIKCAis++eDBgxMmTLDik60gpidCQgghxGri6fA/IYQQYjqcCAkhhCQ0nAgJIYQkNJwICSGEJDQ8R2gVe/bs+eabbyKvBwwYMHXqVHvt6Q2GYRw9evT48eOFhYWpqak977e2tlZXVycnJ995550Xvx8vhEKhzz777OTJk9///vd7ig8PHTrU1NQUeZ2UlFRcXGybfVFhGEZtbe2hQ4cGDx5cUlLi8/l6/qq+vn7Hjh3Dhw+fPn260xlni+BwOLxnz566urr09PQ77rgjOflCG6+dO3d2dnZGXqelpU2aNMk+G6Phyy+/3LdvX1tbW3Z2dlFRUU+FeSgUqqmpaWpqKi4uHjVqlL1G9nlYNWoVU6dODQaDaWlpIjJu3Lh169bZbVGUnDlzZvTo0W63u6Wl5fDhw9dff33k/fr6+ttuu62kpOTMmTPHjh3btWtXfBXof/7555MnT05JSWlpaWlraxs8eHDk/fvvv7+2tva6664TEZ/P99Zbb9lqpppFixbt3r27oKCgsbHxyJEjH374YW5uroi88847FRUV8+bN+89//jNq1Ki4u6+ZM2c2NjZOnjz5s88+O3369EcffZSZmSki2dnZ6enpAwYMEJGCgoLKykq7LdUxderUESNGDB48eOfOnRkZGZGVpWEYs2bNamlpyc/Pf/vtt//617/OmDHDbkv7NAaxhoKCgr///e92W2ECgUDg2LFjhmGIyOHDh3veX7p06YMPPmgYRjgcLi0tfeaZZ2wzMSr8fn9jY2Mk+Gtra+t5/7777lu7dq2NhvWSo0ePhsPhyOt77rln6dKlkdf5+fnr1683DKOjoyMrK2vHjh22mRgVR48ejbwIh8PFxcWPPfZY5I9jxozZu3evfXaZht/vHzp0aE1NjWEY27ZtGzlypN/vNwzjpZdeuuWWW+y2ro8TZ9sj8cWRI0e2bdt24sQJuw3pFUlJSZfcmamqqpo/f76IOByO+fPnV1VVXXPTeoXX673c2fnjx4+/++679fX119gkUxg7dmzP9tqwYcO6u7tFpKGh4cCBAxF/+Xy+srKyuPPX2LFjIy8cDkdmZmbkviLs379/+/btPRvacUpXV1fPHlJVVdWsWbMiilR333337t27T58+bbeBfRlOhFaRmpr6zjvvPPfcc9dff/3jjz9utzkmEwqFmpubI/uHIjJ8+PDGxkZ7TTILj8fzr3/9a+3atVOmTFm0aJERt7mD48ePb9iw4Sc/+YmInDx5cuDAgT35wrj214EDB7Zu3bpw4cLIHwcOHLhx48annnoqJyfnd7/7nb22RUdlZeWMGTNuuOGGysrKiMJfY2Pj8OHDI387ZMgQr9cbv/6KC1gsYxU1NTURtcBPPvmkoKBgzpw5N910k91GmUZk860n8nC5XMFg0F6TzOKPf/xjxHHNzc2TJk3atGnTPffcY7dRatra2n7wgx88/PDD06ZNE5FQKHSxzl/8+uvEiRPz5s17+umn8/LyIu/s3bs34q9du3YVFxfPmTMnOzvbVhvVzJkzZ9KkSR9++OGaNWvKy8uvu+66UCh0cTVT/PorXmBEaBU9mrl5eXk33njjxx9/bK895uJ2u9PS0nq2a5qbm/uMRGeP4zIyMu64447a2lp77YmC9vb2srKy0tLSxx57LPJOZmZme3t7V9eF3rbNzc1ZWVn2GRglX3311fTp05cvX75s2bKeN3v8VVhYOGLEiIMHD9pkXfSMHz9+5syZa9asGT9+/J/+9CcRycrKOnXqVORvz507d/bs2T7z/YpNOBFaTnt7e319/ciRI+02xGSKi4urq6sjr7dt21ZSUmKvPaYTDAb3798fd447d+5ceXn5rbfe+swzz/S8OWbMmJEjR9bU1IhIKBR677334s5fp06dKi0t/fGPf/zII49c8oKmpqaTJ0+OGDHiGhtmFoZhtLa2Rspfi4uLa2pqwuGwiFRXV3/3u9+Nx4VLHMGtUUuoq6v7+c9/fuutt7pcrr/85S/5+fn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"cutoff_exc = 1e-6\n", + "m_order_exc = 6\n", + "eta_exc = 1 / (Ncheb_exc + 1)\n", + "omega_exc = range(-8.0, 8.0; length=160)\n", + "\n", + "ldos_exc_mat = TensorBinding.get_exciton_ldos_spatial(H_exc, Ncheb_exc, omega_exc;\n", + " X_list = X_list_exc,\n", + " kernel = :hodc,\n", + " eta = eta_exc,\n", + " m_order = m_order_exc,\n", + " maxdim = mdim_exc,\n", + " cutoff = cutoff_exc)\n", + "\n", + "heatmap(X_list_exc, collect(omega_exc), ldos_exc_mat;\n", + " xlabel=\"X\", ylabel=\"energy\", colorbar_title=\"LDOS\",\n", + " title=\"Exciton LDOS A(X, omega)\", color=:inferno)" ] }, { @@ -120,71 +805,118 @@ "execution_count": null, "id": "sp_exc_ldos", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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The CPU helper supports bound-sector enrichment.\n", + "N_sample_exc = 20\n", + "N_bound_exc = 12\n", + "seed_exc = 42\n", "\n", - "plot(collect(omega_exc), ldos_exc;\n", - " xlabel=\"energy\", ylabel=\"LDOS\", title=\"Exciton LDOS at X=$(X_exc)\",\n", - " legend=false, lw=2)" - ] - }, - { - "cell_type": "markdown", - "id": "mb_sec2", - "metadata": {}, - "source": [ - "---\n", - "## 2. RPA susceptibility via Wynn ε-algorithm\n", + "dos_full_exc = TensorBinding.get_dos_stochastic(H_exc, Ncheb_exc, omega_exc;\n", + " N_sample = N_sample_exc,\n", + " N_bound = 0,\n", + " seed = seed_exc,\n", + " kernel = :hodc,\n", + " eta = eta_exc,\n", + " m_order = m_order_exc,\n", + " normalize = false,\n", + " dos_weighting = :sample,\n", + " maxdim = mdim_exc,\n", + " cutoff = cutoff_exc)\n", "\n", - "The Random Phase Approximation resums the Dyson series for the interacting\n", - "susceptibility:\n", - "$$\\chi = \\chi_0 + \\chi_0 U \\chi_0 + \\cdots = \\frac{\\chi_0}{1 - U\\chi_0}$$\n", + "dos_bound_exc = TensorBinding.get_dos_stochastic(H_exc, Ncheb_exc, omega_exc;\n", + " N_sample = 0,\n", + " N_bound = N_bound_exc,\n", + " seed = seed_exc + 1,\n", + " kernel = :hodc,\n", + " eta = eta_exc,\n", + " m_order = m_order_exc,\n", + " normalize = false,\n", + " dos_weighting = :sample,\n", + " maxdim = mdim_exc,\n", + " cutoff = cutoff_exc)\n", "\n", - "The bare $\\chi_0(q,\\omega)$ is computed from the KPM density matrix and Green's\n", - "function. Wynn's epsilon algorithm accelerates convergence of the partial sums." + "dos_total_exc = dos_full_exc .+ dos_bound_exc\n", + "\n", + "plot(collect(omega_exc), [dos_full_exc dos_bound_exc dos_total_exc];\n", + " xlabel=\"energy\", ylabel=\"sample-weighted DOS\",\n", + " title=\"Stochastic exciton DOS\",\n", + " label=[\"full-space samples\" \"bound |X,X> samples\" \"sum\"], lw=2)" ] }, { "cell_type": "markdown", "id": "68de97f0", "metadata": {}, - "source": [ - "---\n", - "## 5. RPA susceptibility via Wynn ε-algorithm\n", - "\n", - "The Random Phase Approximation resums the Dyson series for the interacting\n", - "susceptibility:\n", - "\n", - "$$\\chi_\\text{RPA} = \\Pi_0 + \\Pi_0 V \\Pi_0 + \\Pi_0 V \\Pi_0 V \\Pi_0 + \\cdots\n", - "= (I - \\Pi_0 V)^{-1} \\Pi_0$$\n", - "\n", - "where $\\Pi_0(\\omega)$ is the non-interacting polarization bubble and $V$ is the\n", - "bare interaction.\n", - "\n", - "**Efficient workflow** (two improvements over the raw MPO approach):\n", - "\n", - "1. **Density matrix once** — $P = \\theta(\\mu - H)$ is computed a single time via\n", - " McWeeny purification and cached in `H._density_cache`. All frequencies share\n", - " the same $P$; no KPM loop is needed.\n", - "\n", - "2. **Wynn ε-algorithm** — instead of solving the large linear system $(I - \\Pi_0 V)\\chi = \\Pi_0$\n", - " at every $\\omega$, we build the Neumann series\n", - " $T_0 = \\Pi_0$, $T_n = T_{n-1} V \\Pi_0$, extract scalars\n", - " $s_n(q) = -\\operatorname{Im}\\langle q | T_n | q \\rangle / \\pi$ via `get_spect_k`,\n", - " and apply the Wynn ε-algorithm to the partial-sum sequence $[S_0, S_1, \\ldots, S_K]$\n", - " per $(q, \\omega)$. With $K = 6$ terms, the Padé estimate $\\varepsilon_6$ typically\n", - " converges as well as hundreds of MPO solves.\n", - "\n", - "All of this is exposed through the single call `get_rpa_susceptibility_wynn`." - ] + "source": "---\n## 2. RPA susceptibility via Wynn ε-algorithm\n\nThe Random Phase Approximation resums the Dyson series for the interacting\nsusceptibility:\n\n$$\\chi_\\text{RPA} = \\Pi_0 + \\Pi_0 V \\Pi_0 + \\Pi_0 V \\Pi_0 V \\Pi_0 + \\cdots\n= (I - \\Pi_0 V)^{-1} \\Pi_0$$\n\nwhere $\\Pi_0(\\omega)$ is the non-interacting polarization bubble and $V$ is the\nbare interaction.\n\n**Efficient workflow** (two improvements over the raw MPO approach):\n\n1. **Density matrix once** — $P = \\theta(\\mu - H)$ is computed a single time via\n McWeeny purification and cached in `H._density_cache`. All frequencies share\n the same $P$; no KPM loop is needed.\n\n2. **Wynn ε-algorithm** — instead of solving the large linear system $(I - \\Pi_0 V)\\chi = \\Pi_0$\n at every $\\omega$, we build the Neumann series\n $T_0 = \\Pi_0$, $T_n = T_{n-1} V \\Pi_0$, extract scalars\n $s_n(q) = -\\operatorname{Im}\\langle q | T_n | q \\rangle / \\pi$ via `get_spect_k`,\n and apply the Wynn ε-algorithm to the partial-sum sequence $[S_0, S_1, \\ldots, S_K]$\n per $(q, \\omega)$. With $K = 6$ terms, the Padé estimate $\\varepsilon_6$ typically\n converges as well as hundreds of MPO solves.\n\nAll of this is exposed through the single call `get_rpa_susceptibility_wynn`." }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 22, "id": "27fc4738", "metadata": {}, "outputs": [ @@ -192,16 +924,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Pre-computing P via McWeeny purification..." - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tr(P) = 15.99996522081863 (target N/2 = 16)\n", - "maxlinkdim(P) = 13\n", + "Pre-computing P via McWeeny purification...\n", + "Tr(P) = 15.99999515968329 (target N/2 = 16)\n", + "maxlinkdim(P) = 12\n", "\n", "Interaction: on-site Hubbard U = 2.0\n" ] @@ -209,7 +934,12 @@ ], "source": [ "# ── System ────────────────────────────────────────────────────────────────────\n", - "# Reuse H1 from section 1 (L=5, N=32, 1D nearest-neighbour chain).\n", + "\n", + "L = 5\n", + "t = -1.0\n", + "\n", + "H1 = TensorBinding.get_Hamiltonian(\"chain_1d\", t; L=L, scale=scale_exc)\n", + "\n", "# The density matrix is computed once via McWeeny purification and\n", "# stored in H1._density_cache. Every call to get_bubble_mpo with\n", "# P_method=:purification will find it there and skip recomputation.\n", @@ -229,58 +959,34 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "6f997278", + "id": "f6c3c099", + "source": "# ── RPA susceptibility via Wynn ε-acceleration ──────────────────────────────\nK_max = 6\nomegalist_rpa = range(-4.0, 4.0; length=80)\n\nchi_partial, chi_wynn = TensorBinding.get_rpa_susceptibility_wynn(H1, MPOV, omegalist_rpa;\n mode = :charge,\n K_max = K_max,\n verbose = false)\n\nn_wynn = K_max ÷ 2\nprintln(\"chi_partial: \", size(chi_partial), \" chi_wynn: \", size(chi_wynn))", "metadata": {}, - "outputs": [], - "source": [ - "q_axis = 0:nq-1\n", - "ω_axis = collect(ωlist_rpa)\n", - "\n", - "# ── Panel 1: Π₀ vs best Wynn estimate ─────────────────────────────────────────\n", - "# chi_partial[1,...] = partial sum at K=0, i.e. bare Π₀(q,ω)\n", - "# chi_wynn[n_wynn,...] = highest Padé estimate, using K_max+1 terms total\n", - "p_pi0 = heatmap(q_axis, ω_axis, chi_partial[1, :, :];\n", - " title=\"Π₀(q,ω) [bare bubble]\",\n", - " xlabel=\"q\", ylabel=\"ω\", color=:inferno)\n", - "\n", - "p_rpa = heatmap(q_axis, ω_axis, chi_wynn[n_wynn, :, :];\n", - " title=\"χ_RPA(q,ω) [Wynn ε_$(2n_wynn), $(K_max+1) terms]\",\n", - " xlabel=\"q\", ylabel=\"ω\", color=:inferno)\n", - "\n", - "display(plot(p_pi0, p_rpa; layout=(1, 2), size=(860, 360),\n", - " plot_title=\"RPA susceptibility (U = $U_hub, N = $(H1.N))\"))\n", - "\n", - "# ── Panel 2: Convergence of partial sums vs Wynn estimates ────────────────────\n", - "# Total spectral weight Σ_{q,ω} |−Im χ| at each approximation level\n", - "spec_partial = [sum(abs.(chi_partial[k+1, :, :])) for k in 0:K_max]\n", - "spec_wynn = [sum(abs.(chi_wynn[m, :, :])) for m in 1:n_wynn]\n", - "\n", - "p_conv = plot(0:K_max, spec_partial;\n", - " label=\"Partial sum K\", lw=2, marker=:circle, color=:crimson,\n", - " xlabel=\"Order K (number of terms = K+1)\",\n", - " ylabel=\"Σ_{q,ω} |−Im χ|\",\n", - " title=\"Convergence: Neumann series vs Wynn ε-acceleration\")\n", - "\n", - "for m in 1:n_wynn\n", - " scatter!(p_conv, [2m], [spec_wynn[m]];\n", - " label=\"Wynn ε_$(2m) ($(2m+1) terms)\",\n", - " markersize=9, markershape=:star5)\n", - "end\n", - "\n", - "hline!(p_conv, [spec_wynn[end]]; lw=1, ls=:dot, color=:gray,\n", - " label=\"Wynn ε_$(2n_wynn) (best estimate)\")\n", - "\n", - "display(plot(p_conv; size=(680, 400)))" - ] + "execution_count": null, + "outputs": [] }, { "cell_type": "code", "execution_count": null, - "id": "b7771ce7", + "id": "6f997278", "metadata": {}, "outputs": [], - "source": [] + "source": "nq = H1.N\nq_axis = 0:nq-1\nomega_axis = collect(omegalist_rpa)\n\n# ── Panel 1: Π₀ vs best Wynn estimate ─────────────────────────────────────────\n# chi_partial[1,...] = partial sum at K=0, i.e. bare Π₀(q,ω)\n# chi_wynn[n_wynn,...] = highest Padé estimate, using K_max+1 terms total\np_pi0 = heatmap(q_axis, omega_axis, chi_partial[1, :, :];\n title=\"Π₀(q,ω) [bare bubble]\",\n xlabel=\"q\", ylabel=\"ω\", color=:inferno)\n\np_rpa = heatmap(q_axis, omega_axis, chi_wynn[n_wynn, :, :];\n title=\"χ_RPA(q,ω) [Wynn ε_$(2n_wynn), $(K_max+1) terms]\",\n xlabel=\"q\", ylabel=\"ω\", color=:inferno)\n\ndisplay(plot(p_pi0, p_rpa; layout=(1, 2), size=(860, 360),\n plot_title=\"RPA susceptibility (U = $U_hub, N = $(H1.N))\"))\n\n# ── Panel 2: Convergence of partial sums vs Wynn estimates ────────────────────\n# Total spectral weight Σ_{q,ω} |−Im χ| at each approximation level\nspec_partial = [sum(abs.(chi_partial[k+1, :, :])) for k in 0:K_max]\nspec_wynn = [sum(abs.(chi_wynn[m, :, :])) for m in 1:n_wynn]\n\np_conv = plot(0:K_max, spec_partial;\n label=\"Partial sum K\", lw=2, marker=:circle, color=:crimson,\n xlabel=\"Order K (number of terms = K+1)\",\n ylabel=\"Σ_{q,ω} |−Im χ|\",\n title=\"Convergence: Neumann series vs Wynn ε-acceleration\")\n\nfor m in 1:n_wynn\n scatter!(p_conv, [2m], [spec_wynn[m]];\n label=\"Wynn ε_$(2m) ($(2m+1) terms)\",\n markersize=9, markershape=:star5)\nend\n\nhline!(p_conv, [spec_wynn[end]]; lw=1, ls=:dot, color=:gray,\n label=\"Wynn ε_$(2n_wynn) (best estimate)\")\n\ndisplay(plot(p_conv; size=(680, 400)))" } - ] + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.12", + "language": "julia", + "name": "julia-1.12" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 } \ No newline at end of file diff --git a/examples/manybody/nonhermitian_loss_chain.ipynb b/examples/manybody/nonhermitian_loss_chain.ipynb index c2c3650..a7e46e6 100644 --- a/examples/manybody/nonhermitian_loss_chain.ipynb +++ b/examples/manybody/nonhermitian_loss_chain.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "794ec812", "metadata": {}, "outputs": [], @@ -26,7 +26,7 @@ "using ITensors\n", "using ITensorMPS\n", "\n", - "include(\"../src/TensorBinding.jl\")\n", + "include(\"../../src/TensorBinding.jl\")\n", "using .TensorBinding" ] }, @@ -42,10 +42,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "e496bd95", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TBHamiltonian | L=3, N=8, scale=auto, maxlinkdim=3 | geometry: 8 sites, 1D | no Tn cache\n" + ] + } + ], "source": [ "L = 3 # N = 2^L sites\n", "t = 1.0\n", @@ -72,9 +80,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "z = 0.0 + 0.15im, scale = 4.0\n" + ] + } + ], "source": [ "z = 0.0 + 0.15im\n", "scale = 4.0\n", @@ -95,9 +111,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NonHermitianHamiltonian | z=0.0 + 0.15im, blockdim=2, hermitized maxlinkdim=4\n", + "TBHamiltonian | L=3, N=8, scale=4.0, maxlinkdim=4 | geometry: 8 sites, 1D | no Tn cache\n" + ] + } + ], "source": [ "NH = TensorBinding.hermitize(H; z=z, scale=scale, maxdim=maxdim,\n", " convention=:z_minus_H)\n", @@ -108,14 +133,415 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "9bdf9423", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6499999999999997\n", + "-0.6500000000000004\n" + ] + }, + { + "data": { + "image/png": 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", + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/html": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#check the proper hermitrized Hamiltonian\n", "hermitized_mpo = NH.hermitized\n", - "M = TensorBinding.get_matrix(hermitized_mpo.mpo, hermitrized_mpo.sites)\n", + "M = TensorBinding.get_matrix(hermitized_mpo.mpo, hermitized_mpo.sites)\n", "println(maximum(imag.(M)))\n", "println(minimum(imag.(M)))\n", "heatmap(imag.(M),yflip=true)" @@ -132,9 +558,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total spectral weight at z = -2.309436793446442 + 8.079261817456338e-15im\n", + "computed 40 partial moments\n" + ] + } + ], "source": [ "A_mps, dos_point, partials = TensorBinding.nh_spectral_function(\n", " NH, Ncheb_half;\n", @@ -148,19 +583,95 @@ "println(\"total spectral weight at z = \", dos_point)\n", "println(\"computed \", length(partials), \" partial moments\")" ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "658989aa", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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"nbformat": 4, diff --git a/examples/manybody/scf_examples.ipynb b/examples/manybody/scf_examples.ipynb index cd72376..00ad31d 100644 --- a/examples/manybody/scf_examples.ipynb +++ b/examples/manybody/scf_examples.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "a002", "metadata": {}, "outputs": [], @@ -25,7 +25,7 @@ "using ITensors\n", "using ITensorMPS\n", "\n", - "include(\"../src/TensorBinding.jl\")\n", + "include(\"../../src/TensorBinding.jl\")\n", "using .TensorBinding" ] }, diff --git a/examples/misc/miscellaneous.ipynb b/examples/misc/miscellaneous.ipynb index f88e815..64da1ce 100644 --- a/examples/misc/miscellaneous.ipynb +++ b/examples/misc/miscellaneous.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "misc_imports", "metadata": {}, "outputs": [], @@ -825,1662 +825,17 @@ " lw=2, ls=:dash)\n", "vline!([0.0]; ls=:dot, color=:gray, alpha=0.6, label=\"ω = 0\")" ] - }, - { - "cell_type": "markdown", - "id": "c66d9c65", - "metadata": {}, - "source": [ - "---\n", - "## 5. QPI — 1D chain scattering wavevector map\n", - "\n", - "A single on-site impurity scatters quasiparticles between time-reversed Fermi\n", - "points. In 1D the backscattering wavevector $q = 2k_F(\\omega)$ follows the\n", - "dispersion $\\varepsilon(k) = -2t\\cos(ka)$ exactly, so the QPI map\n", - "$|\\delta\\tilde{A}(q,\\omega)|^2$ should trace two symmetric arcs in $(q,\\omega)$\n", - "space that touch $q = \\pi$ at $\\omega = 0$ and fan out to $q \\to 0, 2\\pi$ at\n", - "the band edges.\n", - "\n", - "A smooth disk window (via `sdf_interval` + logistic sigmoid, same machinery as\n", - "`mask_hamiltonian`) is applied to $\\delta A(r,\\omega)$ before the QFT to\n", - "suppress open-boundary ringing.\n", - "\n", - "**Impurity MPO check:** before the main run a small $L=3$, $N=8$ system verifies\n", - "that `_impurity_mpo` places $V$ exactly at site $x_0$ and is zero elsewhere." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "096321ee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Impurity MPO check (L=3, N=8, x0=4, V=0.1)\n", - "Diagonal entries : [0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0]\n", - "Expected : all zeros except index 4 → 0.1\n", - "Max off-diagonal : 0.0\n", - "Impurity MPO: OK\n", - "\n", - "TBHamiltonian | L=8, N=256, scale=2.0, maxlinkdim=3 | geometry: 256 sites, 1D | no Tn cache\n", - "QPI: impurity at site 128 / 256, V = 2.0, Ncheb = 80\n", - "KPM_Tn: estimating spectral bounds via DMRG…\n", - " E_min = -1.9974, E_max = 2.8284\n", - " center = 0.4155, scale = 2.6542\n", - "QPI: online KPM clean…\n", - " QPI clean 10/80 maxlinkdim=4\n", - " QPI clean 20/80 maxlinkdim=4\n", - " QPI clean 30/80 maxlinkdim=4\n", - " QPI clean 40/80 maxlinkdim=4\n", - " QPI clean 50/80 maxlinkdim=4\n", - " QPI clean 60/80 maxlinkdim=4\n", - " QPI clean 70/80 maxlinkdim=4\n", - " QPI clean 80/80 maxlinkdim=4\n", - "QPI: online KPM impurity…\n", - " QPI imp 10/80 maxlinkdim=12\n", - " QPI imp 20/80 maxlinkdim=17\n", - " QPI imp 30/80 maxlinkdim=25\n", - " QPI imp 40/80 maxlinkdim=34\n", - " QPI imp 50/80 maxlinkdim=44\n", - " QPI imp 60/80 maxlinkdim=50\n", - " QPI imp 70/80 maxlinkdim=52\n", - " QPI imp 80/80 maxlinkdim=52\n", - "QPI: building spatial window (fraction=0.6, σ=1.5)…\n", - "QPI: processed 10 / 100 energies\n", - "QPI: processed 20 / 100 energies\n", - "QPI: processed 30 / 100 energies\n", - "QPI: processed 40 / 100 energies\n", - "QPI: processed 50 / 100 energies\n", - "QPI: processed 60 / 100 energies\n", - "QPI: processed 70 / 100 energies\n", - "QPI: processed 80 / 100 energies\n", - "QPI: processed 90 / 100 energies\n" - ] - }, - { - "data": { - "image/png": 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", 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H_chk.sites, V_chk)\n", - "mat_chk = TensorBinding.get_matrix(imp_chk, H_chk.sites)\n", - "diag_chk = real.(diag(mat_chk))\n", - "max_od = maximum(abs.(mat_chk .- Diagonal(diag(mat_chk))))\n", - "\n", - "println(\"Impurity MPO check (L=$L_chk, N=$(2^L_chk), x0=$x0_chk, V=$V_chk)\")\n", - "println(\"Diagonal entries : \", round.(diag_chk; digits=8))\n", - "println(\"Expected : all zeros except index $x0_chk → $V_chk\")\n", - "println(\"Max off-diagonal : \", max_od)\n", - "@assert abs(diag_chk[x0_chk] - V_chk) < 1e-10 \"diagonal at x0 wrong\"\n", - "@assert maximum(abs.(diag_chk[setdiff(1:2^L_chk, [x0_chk])])) < 1e-10 \"unwanted diagonal leak\"\n", - "@assert max_od < 1e-10 \"off-diagonal leak\"\n", - "println(\"Impurity MPO: OK\")\n", - "\n", - "# ── 1D chain Hamiltonian ──────────────────────────────────────────────────────\n", - "L_fs = 8 # N = 64 sites\n", - "H_fs = TensorBinding.get_Hamiltonian(\"chain_1d\", 1.0; L=L_fs, bc = :periodic, scale=2.0)\n", - "println(\"\\n\", H_fs)\n", - "\n", - "# ── QPI sweep ─────────────────────────────────────────────────────────────────\n", - "# ε(k) = -2t cos(2πk/N), bandwidth [-2, 2]. Stay 10% inside to avoid band edge.\n", - "Ncheb_fs = 80\n", - "ω_fs = range(-2.0, 2.0; length=100)\n", - "\n", - "qpi_fs = TensorBinding.get_qpi(H_fs, Ncheb_fs, ω_fs;\n", - " V = 2.0,\n", - " window_fraction = 0.6,\n", - " window_sigma = 1.5,\n", - " maxdim = 60,\n", - " cutoff = 1e-6,\n", - " verbose = true)\n", - "\n", - "# ── QPI heatmap: q vs ω ───────────────────────────────────────────────────────\n", - "# Circshift by N/2 so Γ (q=0) sits in the centre; LSB-first k → physical q = k·2π/N\n", - "N_fs = H_fs.N\n", - "qpi_plot = circshift(qpi_fs, (0, N_fs ÷ 2)) .* N_fs\n", - "q_ax = ((-(N_fs÷2)):(N_fs÷2 - 1)) ./ N_fs .* 2π # [-π, π)\n", - "\n", - "heatmap(q_ax, collect(ω_fs), qpi_plot;\n", - " xlabel = \"q\",\n", - " ylabel = \"ω / t\",\n", - " title = \"QPI — 1D chain (N=$N_fs, V=2.0, bulk window)\",\n", - " color = :inferno,\n", - " xticks = ([-π, -π/2, 0, π/2, π], [\"-π\", \"-π/2\", \"0\", \"π/2\", \"π\"]),\n", - " size = (600, 420))" - ] - }, - { - "cell_type": "markdown", - "id": "b9c24dee", - "metadata": {}, - "source": [ - "---\n", - "## 6. QPI — 2D square lattice\n", - "\n", - "The square lattice dispersion $\\varepsilon(\\mathbf{k}) = -2t(\\cos k_x + \\cos k_y)$\n", - "has bandwidth $[-4t,\\, 4t]$. At half-filling ($\\omega = 0$) the Fermi surface\n", - "is a diamond rotated 45°, with perfect nesting along $\\mathbf{q} = (\\pi,0)$,\n", - "$(0,\\pi)$, and $(\\pi,\\pi)$.\n", - "\n", - "The impurity response\n", - "$\\delta A(\\mathbf{r},\\omega) = A_\\mathrm{imp}(\\mathbf{r},\\omega) - A_\\mathrm{clean}(\\mathbf{r},\\omega)$\n", - "is Fourier-transformed with the forward QFT MPO and the QPI pattern\n", - "$|\\delta\\tilde{A}(\\mathbf{q},\\omega)|^2$ is displayed for representative energies.\n", - "A circular apodization window (`window_fraction=0.7`) suppresses open-boundary\n", - "ringing before the transform." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b765b018", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TBHamiltonian | L=10, N=1024, scale=4.4, maxlinkdim=4 | geometry: 1024 sites, 2D | no Tn cache\n", - "QPI: impurity at site 496 / 1024, V=2.0, mode=:delta, Ncheb=100\n", - "KPM_Tn: estimating spectral bounds via DMRG…\n", - " E_min = -3.9784, E_max = 4.0575\n", - " center = 0.0395, scale = 4.4197\n", - "QPI: online KPM clean…\n", - " QPI clean 10/100 maxlinkdim=30\n", - " QPI clean 20/100 maxlinkdim=40\n", - " QPI clean 30/100 maxlinkdim=50\n", - " QPI clean 40/100 maxlinkdim=54\n", - " QPI clean 50/100 maxlinkdim=62\n", - " QPI clean 60/100 maxlinkdim=65\n", - " QPI clean 70/100 maxlinkdim=67\n", - " QPI clean 80/100 maxlinkdim=67\n", - " QPI clean 90/100 maxlinkdim=70\n", - " QPI clean 100/100 maxlinkdim=76\n", - "QPI: online KPM impurity…\n", - " QPI imp 10/100 maxlinkdim=63\n", - " QPI imp 20/100 maxlinkdim=100\n", - " QPI imp 30/100 maxlinkdim=100\n", - " QPI imp 40/100 maxlinkdim=100\n", - " QPI imp 50/100 maxlinkdim=100\n", - " QPI imp 60/100 maxlinkdim=100\n", - " QPI imp 70/100 maxlinkdim=100\n", - " QPI imp 80/100 maxlinkdim=100\n", - " QPI imp 90/100 maxlinkdim=100\n", - " QPI imp 100/100 maxlinkdim=100\n", - "QPI: building spatial window (fraction=0.8, σ=1.5)…\n", - "QPI: processed 10 / 80 energies\n", - "QPI: processed 20 / 80 energies\n", - "QPI: processed 30 / 80 energies\n", - "QPI: processed 40 / 80 energies\n", - "QPI: processed 50 / 80 energies\n", - "QPI: processed 60 / 80 energies\n", - "QPI: processed 70 / 80 energies\n", - "QPI: processed 80 / 80 energies\n" - ] - }, - { - "data": { - "image/png": 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The smooth potential is compressible by QTCI and\n", - "# yields a much lower-BD impurity MPO, keeping the KPM recursion manageable.\n", - "Ncheb_sq = 100\n", - "ω_sq = range(-3.6, 3.6; length=80)\n", - "\n", - "qpi_sq = TensorBinding.get_qpi(H_sq, Ncheb_sq, ω_sq;\n", - " V = 2.0,\n", - " window_fraction = 0.8,\n", - " window_sigma = 1.5,\n", - " maxdim = 100,\n", - " cutoff = 1e-6,\n", - " verbose = true)\n", - "\n", - "# ── Reshape and plot for representative energies ──────────────────────────────\n", - "# qpi_sq[iω, k+1]: k = kx + Nx*ky (x varies fast, LSB-first)\n", - "# reshape(row, Nx, Ny)' → matrix indexed [qy, qx] for heatmap\n", - "Nx_sq = 2^Lx_sq; Ny_sq = 2^Ly_sq\n", - "qx_ax = ((-(Nx_sq÷2)):(Nx_sq÷2 - 1)) ./ Nx_sq .* 2π\n", - "qy_ax = ((-(Ny_sq÷2)):(Ny_sq÷2 - 1)) ./ Ny_sq .* 2π\n", - "\n", - "ω_targets = [0.0, -1.5, -3.0] # Fermi level, van Hove singularity, near band edge\n", - "ω_arr = collect(ω_sq)\n", - "\n", - "ps = map(ω_targets) do ω_p\n", - " iω = argmin(abs.(ω_arr .- ω_p))\n", - " qpi_2d = reshape(qpi_sq[iω, :], Nx_sq, Ny_sq)'\n", - " qpi_c = circshift(qpi_2d, (Ny_sq÷2, Nx_sq÷2))\n", - " heatmap(qx_ax, qy_ax, sqrt.(qpi_c);\n", - " xlabel=\"qx\", ylabel=\"qy\",\n", - " title=\"ω ≈ $(round(ω_arr[iω]; digits=2))\",\n", - " color=:inferno, aspect_ratio=1,\n", - " xticks=([-π, 0, π], [\"-π\", \"0\", \"π\"]),\n", - " yticks=([-π, 0, π], [\"-π\", \"0\", \"π\"]))\n", - "end\n", - "\n", - "plot(ps...;\n", - " layout = (1, length(ω_targets)),\n", - " size = (300 * length(ω_targets), 300),\n", - " plot_title = \"QPI — square lattice, Gaussian impurity (σ=1.5, N=$(H_sq.N), V=2.0)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "36af7ce1", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Julia 1.12.4", + "display_name": "Julia 1.10", "language": "julia", - "name": "julia-1.12" + "name": "julia-1.10" }, "language_info": { - "file_extension": ".jl", - "mimetype": "application/julia", "name": "julia", - "version": "1.12.4" + "version": "1.10.0" } }, "nbformat": 4, diff --git a/examples/spectral/aux_ldos_examples.ipynb b/examples/spectral/aux_ldos_examples.ipynb index b8fe6a0..0fbaf70 100644 --- a/examples/spectral/aux_ldos_examples.ipynb +++ b/examples/spectral/aux_ldos_examples.ipynb @@ -12,7 +12,7 @@ "using LaTeXStrings\n", "using ITensors\n", "using ITensorMPS\n", - "include(\"../src/TensorBinding.jl\")\n", + "include(\"../../src/TensorBinding.jl\")\n", "using .TensorBinding\n", "using QuanticsTCI\n", "import TensorCrossInterpolation as TCI" @@ -12818,15 +12818,15 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.11.6", + "display_name": "Julia 1.12", "language": "julia", - "name": "julia-1.11" + "name": "julia-1.12" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.11.6" + "version": "1.12.3" } }, "nbformat": 4, diff --git a/examples/spectral/momentum_spectral_functions.ipynb b/examples/spectral/momentum_spectral_functions.ipynb index 4468b2b..e14e89f 100644 --- a/examples/spectral/momentum_spectral_functions.ipynb +++ b/examples/spectral/momentum_spectral_functions.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "6919ba61", "metadata": {}, "outputs": [], @@ -26,7 +26,7 @@ "using LaTeXStrings\n", "using ITensors\n", "using ITensorMPS\n", - "include(\"../src/TensorBinding.jl\")\n", + "include(\"../../src/TensorBinding.jl\")\n", "using .TensorBinding\n", "using QuanticsTCI\n", "import TensorCrossInterpolation as TCI" @@ -6932,9 +6932,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia (18 threads) 1.11.2", + "display_name": "Julia 1.12", "language": "julia", - "name": "julia-_18-threads_-1.11" + "name": "julia-1.12" }, "language_info": { "file_extension": ".jl", diff --git a/examples/topology/real_space_topology.ipynb b/examples/topology/real_space_topology.ipynb index 5b8e4cd..1a946f1 100644 --- a/examples/topology/real_space_topology.ipynb +++ b/examples/topology/real_space_topology.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 1, "id": "top001", "metadata": {}, "outputs": [], @@ -25,7 +25,9 @@ "using ITensors\n", "using ITensorMPS\n", "include(\"../../src/TensorBinding.jl\")\n", - "using .TensorBinding" + "using .TensorBinding\n", + "using QuanticsTCI\n", + "import TensorCrossInterpolation as TCI" ] }, { @@ -3578,7 +3580,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 2, "id": "27b1542b", "metadata": {}, "outputs": [ @@ -3607,19 +3609,10 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "12dbb990", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unique eigenvalues of V (rounded to 2 dp):\n", - "[-0.92, -0.92, -0.84, -0.84, -0.8, -0.8, -0.75, -0.75, -0.68, -0.68, -0.63, -0.63, -0.62, -0.62, -0.58, -0.58, -0.5, -0.5, -0.48, -0.48, -0.45, -0.45, -0.43, -0.43, -0.38, -0.38, -0.34, -0.34, -0.31, -0.31, -0.29, -0.29, -0.26, -0.26, -0.25, -0.25, -0.21, -0.21, -0.17, -0.17, -0.16, -0.16, -0.14, -0.14, -0.14, -0.14, -0.11, -0.11, -0.1, -0.1, -0.08, -0.08, -0.04, -0.04, -0.03, -0.03, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03, 0.03, 0.04, 0.04, 0.08, 0.08, 0.1, 0.1, 0.11, 0.11, 0.14, 0.14, 0.14, 0.14, 0.16, 0.16, 0.17, 0.17, 0.21, 0.21, 0.25, 0.25, 0.26, 0.26, 0.29, 0.29, 0.31, 0.31, 0.34, 0.34, 0.38, 0.38, 0.43, 0.43, 0.45, 0.45, 0.48, 0.48, 0.5, 0.5, 0.58, 0.58, 0.62, 0.62, 0.63, 0.63, 0.68, 0.68, 0.75, 0.75, 0.8, 0.8, 0.84, 0.84, 0.92, 0.92]\n" - ] - } - ], + "outputs": [], "source": [ "# Sanity check: eigenvalues of the valley operator V\n", "# For a valid valley operator the spectrum should be ±1 (or scaled versions).\n", @@ -3632,7 +3625,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 3, "id": "8dd218f1", "metadata": {}, "outputs": [ @@ -3642,20 +3635,18 @@ "text": [ "KPM_Tn: estimating spectral bounds via DMRG…\n", " E_min = -2.9602, E_max = 2.9602\n", - " center = -0.0, scale = 3.2562\n", + " center = 0.0, scale = 3.2562\n", "Quenched operator products done\n", "C1 done\n", "C2 done\n", "C3 done\n", "C4 done\n", - "PK wrapping done\n", "K valley done\n", "Quenched operator products done\n", "C1 done\n", "C2 done\n", "C3 done\n", "C4 done\n", - "PK wrapping done\n", "K′ valley done\n" ] } @@ -3665,17 +3656,17 @@ "\n", "# Purified P is cached in H_sem._density_cache after the first call and reused.\n", "C_K_at = TensorBinding.get_valley_C(H_sem; sequential=false,\n", - " valley=:K, method=:mcweeny, maxdim=mdim_s, l=Lx_s, Λ=15, cutoff=1e-8, fermi=0.0)\n", + " valley=:K, method=:mcweeny, maxdim=mdim_s, l=Lx_s, Λ=15, cutoff=1e-8)\n", "println(\"K valley done\")\n", "\n", "C_Kp_at = TensorBinding.get_valley_C(H_sem; sequential=false,\n", - " valley=:K_prime, method=:mcweeny, maxdim=mdim_s, l=Lx_s, Λ=15, cutoff=1e-8, fermi=0.0)\n", + " valley=:K_prime, method=:mcweeny, maxdim=mdim_s, l=Lx_s, Λ=15, cutoff=1e-8)\n", "println(\"K′ valley done\")" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 5, "id": "359d206a", "metadata": {}, "outputs": [ @@ -3689,117 +3680,117 @@ }, { "data": { - "image/png": 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", + "image/png": 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≈ $(round(mean(CKp_vals[bulk]); digits=3))\",\n", @@ -4099,7 +4422,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "41768265", "metadata": {}, "outputs": [ @@ -4107,13 +4430,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "TBHamiltonian | L=10, N=1024 +2sublattices, scale=auto, maxlinkdim=9 | geometry: 1024 sites, 2D | no Tn cache\n", - "ω₀ = 0.4534 amp = 0.2\n" + "TBHamiltonian | L=8, N=256 +2sublattices, scale=auto, maxlinkdim=7 | geometry: 256 sites, 2D | no Tn cache\n", + "ω₀ = 1.8138 amp = 0.2\n" ] } ], "source": [ - "Lx_bg, Ly_bg = 5, 5\n", + "Lx_bg, Ly_bg = 4, 4\n", "t1_bg = 1.0\n", "amp_bg = 0.2\n", "Nx_bg, Ny_bg = 2^Lx_bg, 2^Ly_bg\n", @@ -4147,7 +4470,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "eb197d9c", "metadata": {}, "outputs": [ @@ -4156,8 +4479,20 @@ "output_type": "stream", "text": [ "KPM_Tn: estimating spectral bounds via DMRG…\n", - " E_min = -3.3536, E_max = 3.3535\n", - " center = -0.0, scale = 3.6889\n" + " E_min = -3.0412, E_max = 3.0422\n", + " center = 0.0005, scale = 3.3458\n", + "Quenched operator products done\n", + "C1 done\n", + "C2 done\n", + "C3 done\n", + "C4 done\n", + "K valley done\n", + "Quenched operator products done\n", + "C1 done\n", + "C2 done\n", + "C3 done\n", + "C4 done\n", + "K′ valley done\n" ] } ], @@ -4165,17 +4500,17 @@ "mdim_bg = 800\n", "\n", "C_K_bg_at = TensorBinding.get_valley_C(H_bg; sequential=false,\n", - " valley=:K, method=:mcweeny, fermi=0.2, maxdim=mdim_bg, l=Lx_bg, Λ=15, cutoff=1e-7, use_sign=false)\n", + " valley=:K, method=:mcweeny, fermi=0.2, maxdim=mdim_bg, l=Lx_bg, Λ=15, cutoff=1e-8)\n", "println(\"K valley done\")\n", "\n", "C_Kp_bg_at = TensorBinding.get_valley_C(H_bg; sequential=false,\n", - " valley=:K_prime, method=:mcweeny, fermi=0.2, maxdim=mdim_bg, l=Lx_bg, Λ=15, cutoff=1e-7, use_sign=false)\n", + " valley=:K_prime, method=:mcweeny, fermi=0.2, maxdim=mdim_bg, l=Lx_bg, Λ=15, cutoff=1e-8)\n", "println(\"K′ valley done\")" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 9, "id": "3e0c2b9c", "metadata": {}, "outputs": [ @@ -4183,1050 +4518,1437 @@ "name": "stdout", "output_type": "stream", "text": [ - "C_K bulk mean : 0.016\n", - "C_K′ bulk mean : -0.016\n" + "C_K bulk mean : -0.035\n", + "C_K′ bulk mean : 0.035\n" ] }, { "data": { - "image/png": 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"source": [ @@ -5278,2537 +6000,16 @@ " title = \"K′ valley ⟨C_K′⟩_bulk ≈ $(round(mean(CKp_bg_vals[bulk_bg]); digits=3))\",\n", " xlabel = \"x\", ylabel = \"y\")\n", "\n", - "\n", - "p_V_bg = scatter(xs_bg, ys_bg;\n", - " marker_z = 2 .* CKp_bg_vals - 2 .* CK_bg_vals,\n", - " color = :RdBu,\n", - " markersize = 14,\n", - " markerstrokewidth = 0.5,\n", - " markerstrokecolor = :black,\n", - " colorbar = true,\n", - " colorbar_title = \"C_v(r)\",\n", - " clims = (-1.1, 1.1),\n", - " aspect_ratio = :equal,\n", - " legend = false,\n", - " title = \"C valley\",\n", - " xlabel = \"x\", ylabel = \"y\")\n", - "\n", - "plot(p_K_bg, p_Kp_bg, p_V_bg;\n", - " layout = (1, 3),\n", + "plot(p_K_bg, p_Kp_bg;\n", + " layout = (1, 2),\n", " size = (900, 400),\n", " plot_title = \"Buckled graphene valley Chern (amp=$(amp_bg), ω₀=$(round(omega0_bg; digits=3)), $(2^Lx_bg)×$(2^Ly_bg) UCs)\")" ] }, - { - "cell_type": "markdown", - "id": "48a07cc2", - "metadata": {}, - "source": [ - "### Bond modulation maps\n", - "\n", - "For each physical site the NN hoppings are summed into three angular bins\n", - "(one per inequivalent bond direction in the honeycomb) and as a grand total.\n", - "Spatial colour variation reveals how the sinusoidal modulation\n", - "$\\delta t_{ij} = A\\sin(\\mathbf{G}_n\\cdot\\mathbf{r}_{ij}^{\\rm mid})$\n", - "redistributes hopping weight across the lattice." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "2ca9ea3f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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], - "source": [ - "N_uc_bg = 2^(Lx_bg + Ly_bg)\n", - "M_bg_mat = TensorBinding.get_matrix(H_bg.mpo, H_bg.sites)\n", - "N_phys_bg = 2 * N_uc_bg # all physical sites (A + B sublattice)\n", - "\n", - "rs_all_bg = [H_bg.geometry(i) for i in 1:N_phys_bg]\n", - "xs_all_bg = [r[1] for r in rs_all_bg]\n", - "ys_all_bg = [r[2] for r in rs_all_bg]\n", - "\n", - "# For each site i accumulate Re(M[i,j]) into 3 angular bins (π/3 each) and a total.\n", - "# Folding the bond angle into [0, π) maps each of the 3 NN directions to one bin.\n", - "bond_dir = [zeros(N_phys_bg) for _ in 1:3]\n", - "bond_sum = zeros(N_phys_bg)\n", - "hop_thr = 0.05 # ignore near-zero off-diagonals\n", - "\n", - "for i in 1:N_phys_bg, j in 1:N_phys_bg\n", - " i == j && continue\n", - " mij = real(M_bg_mat[i, j])\n", - " abs(mij) < hop_thr && continue\n", - " dx_ij = rs_all_bg[j][1] - rs_all_bg[i][1]\n", - " dy_ij = rs_all_bg[j][2] - rs_all_bg[i][2]\n", - " ang = mod(atan(dy_ij, dx_ij), π)\n", - " bin = clamp(Int(floor(ang / (π / 3))) + 1, 1, 3)\n", - " bond_dir[bin][i] += mij\n", - " bond_sum[i] += mij\n", - "end\n", - "\n", - "dir_labels = [\"bond direction 1 (0°–60°)\",\n", - " \"bond direction 2 (60°–120°)\",\n", - " \"bond direction 3 (120°–180°)\"]\n", - "ms, msw = 7, 0.3\n", - "\n", - "pd = [scatter(xs_all_bg, ys_all_bg;\n", - " marker_z=bond_dir[k], color=:RdBu,\n", - " markersize=ms, markerstrokewidth=msw, markerstrokecolor=:black,\n", - " colorbar=true, colorbar_title=\"Σ t\",\n", - " aspect_ratio=:equal, legend=false,\n", - " title=dir_labels[k], xlabel=\"x\", ylabel=\"y\")\n", - " for k in 1:3]\n", - "\n", - "push!(pd,\n", - " scatter(xs_all_bg, ys_all_bg;\n", - " marker_z=bond_sum, color=:RdBu,\n", - " markersize=ms, markerstrokewidth=msw, markerstrokecolor=:black,\n", - " colorbar=true, colorbar_title=\"Σ t\",\n", - " aspect_ratio=:equal, legend=false,\n", - " title=\"sum of all bonds per site\", xlabel=\"x\", ylabel=\"y\"))\n", - "\n", - "plot(pd...;\n", - " layout = (2, 2),\n", - " size = (1000, 840),\n", - " plot_title = \"Buckled graphene bond modulation (amp=$(amp_bg), ω₀=$(round(omega0_bg; digits=3)), $(Nx_bg)×$(Ny_bg) UCs)\")" - ] - }, { "cell_type": "code", "execution_count": null, - "id": "79520d0a", + "id": "48a07cc2", "metadata": {}, "outputs": [], "source": [] @@ -7816,7 +6017,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.12.4", + "display_name": "Julia 1.12", "language": "julia", "name": "julia-1.12" }, @@ -7824,7 +6025,7 @@ "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.12.4" + "version": "1.12.3" } }, "nbformat": 4, diff --git a/src/GPU_tk.jl b/src/GPU_tk.jl new file mode 100644 index 0000000..2e7ffc0 --- /dev/null +++ b/src/GPU_tk.jl @@ -0,0 +1,2378 @@ +# ============================================================ +# GPU_tk.jl — GPU production toolkit for TensorBinding +# ============================================================ +# +# This file is the GPU companion to the CPU solvers in src/solvers/ and +# src/core/Utils.jl. Its purpose is to make LARGE PRODUCTION RUNS (big L, +# many Chebyshev moments, fine spatial/k grids — the regime targeted by the +# SLURM scripts in examples/nontracked/APSOS/) tractable by moving the +# dominant Chebyshev-recurrence / MPO-product cost onto a GPU via the +# NDTensors CUDA backend. Every function here mirrors a CPU counterpart with +# a `_gpu` suffix and (unless its own docstring says otherwise) accepts the +# same keyword arguments and returns the same shape of result. +# +# REQUIREMENTS +# using CUDA # must be loaded *before* calling any *_gpu function +# include("TensorBinding.jl"); using .TensorBinding +# +# σ = TensorBinding.get_conductivity_ward_gpu(H, H, ωlist; Ncheb=80, ...) +# +# ENTRY POINTS (each documented in its own docstring below) +# Conductivity (experimental/not working yet) +# get_conductivity_ward_gpu(H1, H2, ωlist; ...) — Ward-identity σ(ω) (Experimental/not working yet) +# get_conductivity_cheb2d_gpu(H1, H2, ωlist; ...) — 2D-Chebyshev σ(ω) (Experimental/not working yet) + +# Spectral / spatial maps +# get_bands_gpu(H, Ncheb, ω; kpath=..., ...) — A(k,ω) bands +# get_ldos_spatial_gpu(H, Ncheb, ω; reduce=..., ...) — A(r,ω) real-space LDOS +# (:point or :block sampling, +# sublattice :average/:resolve) +# get_dos_stochastic_gpu(H, Ncheb, ω; ...) — stochastic-trace DOS +# get_exciton_ldos_spatial_gpu(H, Ncheb, ω; ...) — A(X,ω) exciton LDOS +# Topology +# get_C_gpu(H, xfunc, yfunc; ...) — real-space Chern marker +# Magnetic Hubbard SCF +# scf_magnetic_hubbard_gpu(H0, U; ...) — collinear mean-field loop +# get_scf_magnetization_gpu(res; ...) — post-hoc (r) map +# get_scf_bands_gpu(res, Ncheb, ω; ...) — post-hoc spin-summed bands +# +# GPU/CPU SPLIT (general pattern — see each function's docstring for specifics) +# GPU : Chebyshev recurrence (KPM_Tn_gpu / inline recursions), weighted MPO +# sums (_weighted_mpo_sum_gpu), MPO×MPO and MPO×MPS products + +# truncation, Hadamard products (_hadamard_mpo_gpu), projections +# (_project_aux_gpu), diagonal extraction +# (extract_diagonal_to_mps_gpu) and real-space/QFT sampling. +# CPU : one-time setup (Hamiltonian/operator construction, Tucker SVDs, +# k-/spatial-group bookkeeping, KPM weight matrices, McWeeny initial +# guesses) and the final per-ω scalar accumulation. +# +# PRECISION (WHY F32) +# The NDTensors GPU backend requires Float32 storage, so every MPO/MPS +# moved to GPU via _to_gpu_mpo / _to_gpu_mps is first cast to ComplexF32; +# results moved back via _to_cpu_mpo / _to_cpu_mps are promoted to +# ComplexF64. This is fine for the observables computed here, but +# ComplexF32 eigendecomposition can produce NaN at very tight `cutoff` on +# large systems — functions on this path warn (without altering the value) +# if `cutoff` is below a recommended floor, typically 1e-4 to 1e-6 +# depending on the routine. +# +# REAL-SPACE / BIT-ORDERING CONVENTIONS +# Real-space sampling (_eval_mps_bigendian_gpu, _eval_block_mps_gpu, used by +# get_ldos_spatial_gpu and get_scf_magnetization_gpu) encodes the position +# index MSB-first across the site list, matching the CPU +# eval_mps/binary_to_MPS convention exactly. The QFT/bands path +# (_eval_diag_mps_gpu, used by get_bands_gpu) instead uses the legacy +# LSB-first convention required by the quantics-Fourier MPO. The two are +# not interchangeable — see spatial_sampling_plan in Utils.jl for how the +# shared sampler keeps them straight. +# +# PRODUCTION SCRIPTS +# See examples/nontracked/APSOS/*.jl + *.sh for the SLURM driver scripts +# that exercise this toolkit (magnetic-Hubbard SCF + magnetization/bands +# maps, spatial LDOS with the :point/:block samplers, ...). +# ============================================================ + + +# ── 1. CUDA bridge (no hard dependency) ───────────────────────────────────── + +const _TB_CUDA = Ref{Union{Module,Nothing}}(nothing) + +function _tb_cuda_module() + if _TB_CUDA[] === nothing + id = Base.PkgId(Base.UUID("052768ef-5323-5732-b1bb-66c8b64840ba"), "CUDA") + _TB_CUDA[] = get(Base.loaded_modules, id, nothing) + end + return _TB_CUDA[] +end + +function _check_gpu(caller::String = "") + m = _tb_cuda_module() + if m === nothing + tag = isempty(caller) ? "" : " (called from $caller)" + error(""" +TensorBinding$tag: GPU functions require CUDA.jl. +Load it before calling any *_gpu function: + + using CUDA + +Install once with: ] add CUDA +""") + end +end + +function _gpu_gc!() + m = _tb_cuda_module() + m === nothing && return + m.synchronize() + GC.gc(false) + m.reclaim() +end + + +# ── 2. MPO type conversion ─────────────────────────────────────────────────── + +# CPU F64 → CPU F32 (prerequisite before cu()) +function _mpo_to_f32(mpo::MPO) + return MPO([ + let idx = inds(mpo[i]) + ITensor(ComplexF32.(Array(mpo[i], idx...)), idx) + end + for i in 1:length(mpo) + ]) +end + +# CPU F64 → GPU F32 +function _to_gpu_mpo(mpo::MPO) + _check_gpu("_to_gpu_mpo") + return _tb_cuda_module().cu(_mpo_to_f32(mpo)) +end + +# CPU MPS → GPU F32 MPS +function _to_gpu_mps(mps::MPS) + _check_gpu("_to_gpu_mps") + m = _tb_cuda_module() + result = similar(mps) + for j in 1:length(mps) + idx = inds(mps[j]) + arr = Array(mps[j], idx...) # CPU: typeassert safe + result[j] = ITensors.itensor(m.cu(ComplexF32.(arr)), idx...) + end + return result +end + +function _to_cpu_mps(mps::MPS) + result = similar(mps) + for j in 1:length(mps) + T = mps[j] + s = NDTensors.storage(ITensors.tensor(T)) + arr_cpu = Array(NDTensors.data(s)) + result[j] = ITensors.itensor(ComplexF64.(arr_cpu), inds(T)...) + end + return result +end + +# GPU F32 → CPU F64 (called after Hadamard products) +# Array(::ITensor, inds...) typeasserts the result as Array{T,N}, which fails +# for GPU tensors (CuArray ≠ Array). Go through storage() → raw CuArray → +# Array (bulk copy) → itensor (no typeassert). +function _to_cpu_mpo(mpo::MPO) + result = similar(mpo) + for j in 1:length(mpo) + T = mpo[j] + s = NDTensors.storage(ITensors.tensor(T)) # Dense{F32, CuArray} + arr_cpu = Array(NDTensors.data(s)) # CuArray → Array{F32,1} + result[j] = ITensors.itensor(ComplexF64.(arr_cpu), inds(T)...) + end + return result +end + + +# Build the two QFT operators for the given Hamiltonian and move them to GPU F32. +# Call once before the Tucker pairs loop so the build cost is amortised across +# all r_m × r_n pairs. +function _build_qft_ops_gpu(H::TBHamiltonian) + pos_s = _pos_sites(H) + R = length(pos_s) + FTirev_cpu = _embed_in_full_sites(H, fix_sites( + MPO(TCI.reverse(QuanticsTCI.quanticsfouriermpo(R; sign=-1.0, normalize=true))), pos_s)) + FTrev_cpu = _embed_in_full_sites(H, fix_sites( + MPO(TCI.reverse(QuanticsTCI.quanticsfouriermpo(R; sign=+1.0, normalize=true))), pos_s)) + return _to_gpu_mpo(FTirev_cpu), _to_gpu_mpo(FTrev_cpu) +end + +# Apply the QFT sandwich U·W·U† on GPU using pre-built GPU QFT operators. +# Returns a GPU F32 MPO. +function _apply_qft_conj_gpu(W::MPO, FTirev_gpu::MPO, FTrev_gpu::MPO; + tol::Real = 1e-9, maxdim::Int = 100) + Op1 = apply(W, FTirev_gpu; cutoff=tol, maxdim=maxdim) + Op2 = apply(swapprime(FTrev_gpu, 0 => 1), Op1; cutoff=tol, maxdim=maxdim) + return ITensorMPS.truncate!(Op2; cutoff=tol, maxdim=maxdim) +end + +# ── 3. GPU-safe primitives ─────────────────────────────────────────────────── + +# delta() produces a DiagTensor{Float64} (CPU). When contracted with a +# Dense{ComplexF32} GPU tensor, NDTensors promotes the output to ComplexF64 +# and the _contract! dispatch fails (all three tensors must share El). +# Fix: materialise the delta as a dense F32 GPU tensor. +function _make_delta_gpu(i::Index, j::Index, k::Index) + d_dense = dense(delta(i, j, k)) # DiagStorage → DenseStorage + idx = inds(d_dense) + arr = Array(d_dense, idx...) + return _tb_cuda_module().cu(ITensor(Float32.(arr), idx)) +end + +# GPU-safe Hadamard product: identical logic to _hadamard_mpo but uses +# _make_delta_gpu so all contractions stay within {ComplexF32, GPU}. +function _hadamard_mpo_gpu(A::MPO, B::MPO, out_sites::Vector{<:Index}; + maxdim::Int = typemax(Int), cutoff::Real = 0.0) + L = length(A) + @assert length(B) == L && length(out_sites) == L + sindsA = siteinds(A) + sindsB = siteinds(B) + + links_B_old = Vector{Index}(undef, max(L - 1, 0)) + links_B_new = Vector{Index}(undef, max(L - 1, 0)) + for b in 1:L-1 + lB = only(commoninds(B[b], B[b+1])) + links_B_old[b] = lB + links_B_new[b] = sim(lB) + end + + tens = Vector{ITensor}(undef, L) + for n in 1:L + bra_A, ket_A = _bra_ket(sindsA[n]) + bra_B, ket_B = _bra_ket(sindsB[n]) + bra_out = prime(out_sites[n]) + ket_out = out_sites[n] + bra_B_f = sim(bra_B) + ket_B_f = sim(ket_B) + old_inds = Index[bra_B, ket_B] + new_inds = Index[bra_B_f, ket_B_f] + n > 1 && push!(old_inds, links_B_old[n-1]); n > 1 && push!(new_inds, links_B_new[n-1]) + n < L && push!(old_inds, links_B_old[n]); n < L && push!(new_inds, links_B_new[n]) + B_n = replaceinds(B[n], old_inds, new_inds) + # Contract delta tensors into A *before* multiplying B_n to avoid an + # 8D intermediate. Old order: (A*B)→8D→*δ→6D→*δ→5D. + # New order: (A*δ_bra*δ_ket)→6D→*B_n→6D. + # The 8D path overflows int32 CUDA indexing for maxdim ≳ 115 + # (16·χ⁴ > 2³¹ when χ > ~115), causing ERROR_ILLEGAL_ADDRESS. + W = A[n] * _make_delta_gpu(bra_A, bra_B_f, bra_out) # 4D→5D + W = W * _make_delta_gpu(ket_A, ket_B_f, ket_out) # 5D→6D + W = W * B_n # 6D→6D + tens[n] = W + end + + if L == 1 + mpo = MPO(tens) + (maxdim < typemax(Int) || cutoff > 0.0) && ITensorMPS.truncate!(mpo; maxdim=maxdim, cutoff=cutoff) + return mpo + end + Cs = Vector{ITensor}(undef, L - 1) + for b in 1:L-1 + lA = only(commoninds(A[b], A[b+1])) + lB = links_B_new[b] + Cs[b] = combiner(lA, lB; tags="Link,l=$b") + end + tens[1] = tens[1] * Cs[1] + for n in 2:L-1 + tens[n] = tens[n] * Cs[n-1] * Cs[n] + end + tens[L] = tens[L] * Cs[L-1] + mpo = MPO(tens) + (maxdim < typemax(Int) || cutoff > 0.0) && ITensorMPS.truncate!(mpo; maxdim=maxdim, cutoff=cutoff) + return mpo +end + +# Evaluate an MPS element at bit-index `idx` entirely on GPU using the legacy +# LSB-first convention used by the GPU QFT/bands path. +# +# Basis vectors are built as explicit dense arrays matching the element type of +# A so that the contraction is GPU×GPU with a consistent dtype throughout. +function _eval_diag_mps_gpu(A::MPS, idx::Int) + cuda = _tb_cuda_module() + s = siteinds(A) + ElT = eltype(A[1]) + acc = cuda.cu(ITensor(one(ElT))) + for i in 1:length(s) + b = (idx >> (i - 1)) & 1 + v_arr = zeros(ElT, dim(s[i])) + v_arr[b + 1] = one(real(ElT)) + v = cuda.cu(ITensor(v_arr, s[i])) + acc *= A[i] * v + end + return real(scalar(acc)) +end + +# Real-space MPS element evaluation on GPU, matching binary_to_MPS/eval_mps: +# `idx` is encoded big-endian across the site order. +function _eval_mps_bigendian_gpu(A::MPS, idx::Int) + cuda = _tb_cuda_module() + s = siteinds(A) + ElT = eltype(A[1]) + n = length(s) + acc = cuda.cu(ITensor(one(ElT))) + for i in 1:n + b = (idx >> (n - i)) & 1 + v_arr = zeros(ElT, dim(s[i])) + v_arr[b + 1] = one(real(ElT)) + v = cuda.cu(ITensor(v_arr, s[i])) + acc *= A[i] * v + end + return real(scalar(acc)) +end + +# Block-integrated MPS element on GPU (reduce=:block): sum the profile over one +# coarse block by tracing out the within-block position bits and pinning the +# block to the coarse pixel (ixp, iyp). The big-endian position site order is +# [iy_MSB..iy_LSB, ix_MSB..ix_LSB] (sites 1..Ly carry iy, Ly+1..L carry ix), so +# we keep the top b bits of iy (sites 1..b) and top a bits of ix (sites +# Ly+1..Ly+a) as onehot, and contract every lower bit with [1,1] (a sum). +function _eval_block_mps_gpu(A::MPS, ixp::Int, iyp::Int, + a::Int, b::Int, Lx::Int, Ly::Int) + cuda = _tb_cuda_module() + s = siteinds(A) + ElT = eltype(A[1]) + L = Lx + Ly + acc = cuda.cu(ITensor(one(ElT))) + for i in 1:L + v_arr = zeros(ElT, dim(s[i])) + if i <= b # keep: iy block bit (b - i) + v_arr[((iyp >> (b - i)) & 1) + 1] = one(real(ElT)) + elseif i <= Ly # sum: iy within-block bit + v_arr .= one(real(ElT)) + elseif i <= Ly + a # keep: ix block bit (a - (i - Ly)) + v_arr[((ixp >> (a - (i - Ly))) & 1) + 1] = one(real(ElT)) + else # sum: ix within-block bit + v_arr .= one(real(ElT)) + end + v = cuda.cu(ITensor(v_arr, s[i])) + acc *= A[i] * v + end + return real(scalar(acc)) +end + +""" + extract_diagonal_to_mps_gpu(M::MPO) -> MPS + +GPU-resident analogue of `extract_diagonal_to_mps`. `M` is expected to already +be a GPU ComplexF32 MPO (e.g. a Chebyshev moment from `KPM_Tn_gpu`, after +`_apply_qft_conj_gpu` and/or `_project_aux_gpu`); the returned MPS is also on +GPU (ComplexF32). +""" +# extract_diagonal_to_mps (in RPA_tk.jl) uses plain onehot() which returns a +# CPU DiagBlockSparse tensor. Contracting a GPU ComplexF32 MPO tensor with a +# CPU onehot fails (GPU×CPU mismatch). Here we wrap each onehot call with +# cu() so NDTensors resolves the contraction entirely on the GPU. +# The zero ITensor `res` has no committed storage, so the first `+=` with a +# GPU ComplexF32 result promotes it to the correct GPU type. +function extract_diagonal_to_mps_gpu(M::MPO)::MPS + cuda = _tb_cuda_module() + N = length(M) + new_tensors = Vector{ITensor}(undef, N) + for i in 1:N + t = M[i] + s2, s1 = siteinds(M, i) # s2 = bra (primed), s1 = ket + d_s = dim(s1) + v_inds = uniqueinds(t, s1, s2) + + res = ITensor(v_inds..., s1) # zero tensor; type determined by first += + for v in 1:d_s + slice = t * cuda.cu(onehot(s1 => v)) * cuda.cu(onehot(s2 => v)) + res += slice * cuda.cu(onehot(s1 => v)) + end + new_tensors[i] = res + end + return MPS(new_tensors) +end + +function _is_gpu_tensor(T::ITensor) + storage = NDTensors.storage(ITensors.tensor(T)) + data = try + NDTensors.data(storage) + catch + return false + end + return occursin("CuArray", string(typeof(data))) +end + +function _ensure_gpu_mpo(W::MPO; caller::String = "_ensure_gpu_mpo") + flags = [_is_gpu_tensor(W[i]) for i in 1:length(W)] + all(flags) && return W + any(flags) && error("$caller: mixed CPU/GPU MPO tensors are not supported.") + return _to_gpu_mpo(W) +end + +""" + density_profile_from_dm_gpu(density_mpo, sites=nothing; mode=:direct) -> MPS + +GPU-resident analogue of `density_profile_from_dm`. If `density_mpo` is a CPU +MPO it is uploaded once; if it is already on GPU it is used in place. The +returned profile is a GPU MPS. `mode=:complement` returns `1 - diag(D)` on GPU. +""" +function density_profile_from_dm_gpu(density_mpo::MPO, sites=nothing; + mode::Symbol = :direct, + maxdim::Int = 100, + cutoff::Real = 1e-8) + _check_gpu("density_profile_from_dm_gpu") + dm_gpu = _ensure_gpu_mpo(density_mpo; caller="density_profile_from_dm_gpu") + diag_mps = extract_diagonal_to_mps_gpu(dm_gpu) + mode === :direct && return diag_mps + if mode === :complement + profile_sites = sites === nothing ? collect(siteinds(diag_mps)) : collect(sites) + one_mps = _to_gpu_mps(constant_mps(profile_sites, 1.0)) + return +(one_mps, -diag_mps; maxdim=maxdim, cutoff=cutoff) + end + error("Unsupported density extraction mode :$mode. Use :direct or :complement.") +end + +function _mps_to_diagonal_mpo_gpu(mps::MPS, sites)::MPO + N = length(mps) + mpo_tensors = Vector{ITensor}(undef, N) + for i in 1:N + mps_t = mps[i] + old_s = if N == 1 + only(siteinds(mps)) + elseif i == 1 + uniqueind(mps_t, mps[i+1]) + elseif i == N + uniqueind(mps_t, mps[i-1]) + else + uniqueind(mps_t, mps[i-1], mps[i+1]) + end + s = sites[i] + s_temp = Index(dim(s), "temp") + mpo_tensors[i] = replaceind(mps_t, old_s => s_temp) * + _make_delta_gpu(s_temp, s, s') + end + return MPO(mpo_tensors) +end + +function _rms_error_gpu(a::MPS, b::MPS; cutoff::Real = 1e-12) + diff = +(a, -1.0 * b; cutoff=Float64(cutoff)) + n = prod(dim(s) for s in siteinds(a)) + return sqrt(abs(real(inner(diff', diff))) / n) +end + +function _local_hartree_from_density_gpu(rho::MPS, sites, U::Number, bg::MPS; + maxdim::Int, cutoff::Real) + coeff = +(rho, -1.0 * bg; maxdim=maxdim, cutoff=Float64(cutoff)) + return _mps_to_diagonal_mpo_gpu(U * coeff, sites) +end + +function _hartree_mpo_from_density_gpu(rho::MPS, interaction_op::MPO, sites, bg::MPS; + maxdim::Int, cutoff::Real) + coeff = +(rho, -1.0 * bg; maxdim=maxdim, cutoff=Float64(cutoff)) + coeff_mps = apply(interaction_op, coeff; maxdim=maxdim, cutoff=Float64(cutoff)) + return _mps_to_diagonal_mpo_gpu(coeff_mps, sites) +end + +# Project one auxiliary site out of a GPU MPO. +# Mirrors project_aux (CPU) but builds a dense ComplexF32 projector on GPU so +# every contraction stays on the GPU. The contracted site is absorbed into the +# neighbouring site, returning an MPO with one fewer site. +# +# setelt() produces a DiagBlockSparse ITensor that cu() leaves on CPU — we +# therefore build the |sec> (Tn_list, scale, center) + +GPU version of `KPM_Tn`. Moves the identity and scaled Hamiltonian MPOs to +GPU (ComplexF32) before the recurrence so all Tn tensors stay on GPU. +Requires `using CUDA`. + +If `keep_indices` is provided (a `Set{Int}`, 1-based into the returned vector +where index 1 = T_0, 2 = T_1, …), only those Tns are retained in memory. +All other slots are set to `nothing`. The recurrence itself always runs to +completion — `keep_indices` only controls which results are stored. +""" +function KPM_Tn_gpu(H_mpo::MPO, N::Int, sites; + scale::Union{Real,Nothing} = nothing, + center::Real = 0.0, + maxdim::Int = 40, + dmrg_nsweeps::Int = 5, + dmrg_maxdim = [10, 20, 40], + dmrg_linkdim::Int = 4, + cutoff::Real = 1e-8, + keep_indices::Union{Nothing,AbstractSet{Int}} = nothing, + verbose::Bool = true) + + _check_gpu("KPM_Tn_gpu") + + if isnothing(scale) + scale, center = _estimate_spectral_bounds(H_mpo, sites; + dmrg_nsweeps = dmrg_nsweeps, + dmrg_maxdim = dmrg_maxdim, + dmrg_linkdim = dmrg_linkdim) + end + + I_mpo = MPO(sites, "Id") + Ham_n = (1 / scale) * +(H_mpo, (-center) * I_mpo; cutoff = cutoff) + + I_mpo = _to_gpu_mpo(I_mpo) + Ham_n = _to_gpu_mpo(Ham_n) + + keep = keep_indices + T_k_minus_2 = I_mpo + T_k_minus_1 = Ham_n + Tn_list = Vector{Union{MPO,Nothing}}(undef, N + 1) + Tn_list[1] = (keep === nothing || 1 ∈ keep) ? T_k_minus_2 : nothing + Tn_list[2] = (keep === nothing || 2 ∈ keep) ? T_k_minus_1 : nothing + + for k in 3:N+1 + T_k = +(2 * apply(Ham_n, T_k_minus_1; cutoff = cutoff), + -T_k_minus_2; maxdim = maxdim) + T_k = ITensorMPS.truncate!(T_k; cutoff = cutoff) + Tn_list[k] = (keep === nothing || k ∈ keep) ? T_k : nothing + T_k_minus_2 = T_k_minus_1 + T_k_minus_1 = T_k + _gpu_gc!() + if verbose && (k % 5 == 0 || k == N+1) + println(" [gpu] T_$((k-1)) maxlinkdim=$(ITensorMPS.maxlinkdim(T_k))") + end + end + + return Tn_list, scale, center +end + + +# ── 5. Shared Tucker component builder ────────────────────────────────────── + +# Computes C_tuck, B_tuck, A_tuck, E_tuck fully on GPU. +# All inputs (Tn1, Tn2, P1_gpu, P2_gpu) are expected to be GPU F32 MPOs. +function _build_tucker_components_gpu(Tn1, Tn2, P1_gpu, P2_gpu; + U_m, V_n, r_m, r_n, + maxdim, cutoff) + C_tuck = [_weighted_mpo_sum_gpu(U_m[:, s1], Tn1; maxdim=maxdim, cutoff=cutoff) + for s1 in 1:r_m] + B_tuck = [_weighted_mpo_sum_gpu(conj.(V_n[:, s2]), Tn2; maxdim=maxdim, cutoff=cutoff) + for s2 in 1:r_n] + A_tuck = [isnothing(C_tuck[s1]) ? nothing : + ITensorMPS.truncate!(apply(C_tuck[s1], P1_gpu; maxdim=maxdim, cutoff=cutoff); cutoff=cutoff) + for s1 in 1:r_m] + E_tuck = [isnothing(B_tuck[s2]) ? nothing : + ITensorMPS.truncate!(apply(B_tuck[s2], P2_gpu; maxdim=maxdim, cutoff=cutoff); cutoff=cutoff) + for s2 in 1:r_n] + return C_tuck, B_tuck, A_tuck, E_tuck +end + + +# ── 6. GPU entry points ────────────────────────────────────────────────────── + +""" + get_conductivity_ward_gpu(H1, H2, ωlist; kwargs...) -> Vector{ComplexF64} + +GPU-accelerated version of `get_conductivity_ward`. + +GPU handles: Chebyshev recurrence, density matrix transfer, weighted MPO sums, + MPO×MPO multiplications, Hadamard products. +CPU handles: replace_sites, QFT sandwich, inner products, per-ω accumulation. + +All kwargs are identical to `get_conductivity_ward` (threading is removed — +use julia -t N at the process level instead). +""" +function get_conductivity_ward_gpu(H1::TBHamiltonian, H2::TBHamiltonian, + ωlist::AbstractVector{<:Real}; + q_int::Int = 1, + Lx::Int = H1.L ÷ 2, + q_sq::Union{Real,Nothing} = nothing, + Ncheb::Int = 50, + maxdim::Int = 200, + cutoff::Real = 1e-8, + ϵF::Real = 0.0, + P_method::Symbol = :purification, + purify_method::Symbol = :mcweeny, + purify_maxdim::Int = 40, + purify_maxiters::Int = 30, + purify_tol::Float64 = 1e-5, + η::Real = 1e-3, + coeff_tol::Real = 1e-12, + kernel::Symbol = :jackson, + tucker_tol::Real = 1e-3, + tucker_maxrank::Int = 20, + hooi_iters::Int = 3, + qft_tol::Real = 1e-9, + qft_maxdim::Int = 100, + verbose::Bool = false) + + _check_gpu("get_conductivity_ward_gpu") + H1.geometry === nothing && error("get_conductivity_ward_gpu: H1.geometry is not set") + L1 = H1.L; L2 = H2.L + @assert L1 == L2 "get_conductivity_ward_gpu: H1 and H2 must have the same L" + nω = length(ωlist) + + if q_sq === nothing + q_vec, q_sq_val = _q_int_to_physical(H1, q_int; Lx=Lx) + q_sq_val = Float64(q_sq_val) + verbose && println("ward_gpu: q_int=$q_int → |q|²=$(round(q_sq_val; digits=6))") + else + q_sq_val = Float64(q_sq) + verbose && println("ward_gpu: q_int=$q_int, |q|²=$(round(q_sq_val; digits=6)) (provided)") + end + (isnan(q_sq_val) || q_sq_val < 1e-14) && + error("get_conductivity_ward_gpu: q_sq is NaN or ≈ 0. Provide q_sq explicitly.") + + v_q = _density_vertex_mps(H1, q_int) + + _ensure_scale!(H1); _ensure_scale!(H2) + scale1 = H1.scale; center1 = H1.center + scale2 = H2.scale; center2 = H2.center + + # ── 2D GF coefficients (CPU, small dense matrices) ─────────────────────── + # Computed BEFORE the Tn recurrence so we know which (m,n) pairs matter. + N = Ncheb + 1 + verbose && println("ward_gpu: 2D GF coefficients for $nω frequencies (CPU)...") + C_all = [chebyshev2d_gf_coeffs(ω, scale1, center1, scale2, center2, η, N) + for ω in ωlist] + if kernel == :jackson + g_jk = _jackson_kernel(N) + G_jk = g_jk * g_jk' + C_all = [G_jk .* C for C in C_all] + elseif kernel != :none + error("get_conductivity_ward_gpu: unknown kernel=$kernel") + end + + # Active (m,n) pairs: those where max_ω |C_mn(ω)| exceeds the threshold. + # This determines which Tns we need — no Tucker SVD required. + max_C_abs = reduce((A, B) -> max.(A, abs.(B)), C_all; init=zeros(N, N)) + pair_tol = tucker_tol * maximum(max_C_abs) + active_pairs = [(m, n) for m in 1:N for n in 1:N if max_C_abs[m, n] > pair_tol] + active_m_set = Set(m for (m, _) in active_pairs) + active_n_set = H1 === H2 ? active_m_set : Set(n for (_, n) in active_pairs) + verbose && println("ward_gpu: $(length(active_pairs)) active (m,n) pairs " * + "($(length(active_m_set)) unique m, $(length(active_n_set)) unique n)") + + # ── Chebyshev recurrence on GPU (only active indices stored) ───────────── + verbose && println("ward_gpu: Chebyshev recurrence on GPU (Ncheb=$Ncheb)...") + Tn1, _, _ = KPM_Tn_gpu(H1.mpo, Ncheb, H1.sites; + scale=scale1, center=center1, + maxdim=maxdim, cutoff=cutoff, + keep_indices=active_m_set, verbose=false) + Tn2 = H1 === H2 ? Tn1 : KPM_Tn_gpu(H2.mpo, Ncheb, H2.sites; + scale=scale2, center=center2, + maxdim=maxdim, cutoff=cutoff, + keep_indices=active_n_set, verbose=false)[1] + + # ── Density matrices: purify on CPU, move to GPU ────────────────────────── + verbose && println("ward_gpu: density matrices (CPU purification → GPU)...") + P1_gpu = _get_density_matrix_gpu(H1, ϵF, P_method, Ncheb, maxdim, cutoff, + purify_method, purify_maxdim, purify_maxiters, + purify_tol, verbose) + P2_gpu = H1 === H2 ? P1_gpu : _get_density_matrix_gpu(H2, ϵF, P_method, Ncheb, maxdim, cutoff, + purify_method, purify_maxdim, purify_maxiters, + purify_tol, verbose) + + out_sites = [Index(dim(s), "Bubble,n=$i") for (i, s) in enumerate(H1.sites)] + + # ── Cache T_m·P1 for each active m, T_n·P2 for each active n ──────────── + verbose && println("ward_gpu: caching T_m·P and T_n·P on GPU...") + A_cache = Dict{Int,MPO}() # A_cache[m] = T_m · P1 + for m in active_m_set + isnothing(Tn1[m]) && continue + A_cache[m] = ITensorMPS.truncate!( + apply(Tn1[m], P1_gpu; maxdim=maxdim, cutoff=cutoff); cutoff=cutoff) + _gpu_gc!() + end + E_cache = H1 === H2 ? A_cache : Dict{Int,MPO}() # E_cache[n] = T_n · P2 + if H1 !== H2 + for n in active_n_set + isnothing(Tn2[n]) && continue + E_cache[n] = ITensorMPS.truncate!( + apply(Tn2[n], P2_gpu; maxdim=maxdim, cutoff=cutoff); cutoff=cutoff) + _gpu_gc!() + end + end + + # ── Build QFT operators once ────────────────────────────────────────────── + verbose && println("ward_gpu: building QFT operators (CPU → GPU)...") + FTirev_gpu, FTrev_gpu = _build_qft_ops_gpu(H1) + v_q_gpu = _to_gpu_mps(v_q) + + # ── Per (m,n) pair: two independent Hadamard → QFT → inner products ─────── + # No weighted MPO sums are formed — f_mn = val_A − val_B is a scalar difference. + # This avoids the densitymatrix NaN from accumulating many F32 MPOs. + n_pairs = length(active_pairs) + verbose && println("ward_gpu: $n_pairs (m,n) pairs × 2 inner products (GPU)...") + f_direct = Dict{Tuple{Int,Int},ComplexF64}() + t0_inner = time() + + for (idx, (m, n)) in enumerate(active_pairs) + (isnothing(Tn1[m]) || isnothing(Tn2[n]) || + !haskey(A_cache, m) || !haskey(E_cache, n)) && continue + + # (T_m·P1) ⊙ T_n → QFT sandwich → ⟨v_q|·|v_q⟩ + had_A = _hadamard_mpo_gpu(A_cache[m], Tn2[n], out_sites; maxdim=maxdim, cutoff=cutoff) + D_A = _apply_qft_conj_gpu(replace_sites(had_A, H1.sites), FTirev_gpu, FTrev_gpu; + tol=qft_tol, maxdim=qft_maxdim) + val_A = ComplexF64(inner(v_q_gpu, D_A, v_q_gpu)) + _gpu_gc!() + + # T_m ⊙ (T_n·P2) → QFT sandwich → ⟨v_q|·|v_q⟩ + had_B = _hadamard_mpo_gpu(Tn1[m], E_cache[n], out_sites; maxdim=maxdim, cutoff=cutoff) + D_B = _apply_qft_conj_gpu(replace_sites(had_B, H1.sites), FTirev_gpu, FTrev_gpu; + tol=qft_tol, maxdim=qft_maxdim) + val_B = ComplexF64(inner(v_q_gpu, D_B, v_q_gpu)) + _gpu_gc!() + + f_direct[(m, n)] = val_A - val_B + + if verbose && (idx % 20 == 0 || idx == n_pairs) + println(" ($m,$n) [$idx/$n_pairs, $(round(time()-t0_inner; digits=1))s]") + end + end + + # ── Per-ω accumulation (CPU scalars only) ──────────────────────────────── + σ = zeros(ComplexF64, nω) + for (iω, ω) in enumerate(ωlist) + abs(ω) < 1e-14 && continue + chi_q = sum(C_all[iω][m, n] * get(f_direct, (m, n), zero(ComplexF64)) + for (m, n) in active_pairs; init=zero(ComplexF64)) + σ[iω] = chi_q * ω / q_sq_val + end + + verbose && println("ward_gpu: done.") + return σ +end + + +""" + get_conductivity_cheb2d_gpu(H1, H2, ωlist; kwargs...) -> Vector{Float64} + +GPU-accelerated version of `get_conductivity_cheb2d`. +Same GPU/CPU split as `get_conductivity_ward_gpu`. +""" +function get_conductivity_cheb2d_gpu(H1::TBHamiltonian, H2::TBHamiltonian, + ωlist::AbstractVector{<:Real}; + mu_a::Symbol = :x, + mu_b::Symbol = :x, + Ncheb::Int = 50, + maxdim::Int = 200, + cutoff::Real = 1e-8, + ϵF::Real = 0.0, + P_method::Symbol = :purification, + purify_method::Symbol = :mcweeny, + purify_maxdim::Int = 40, + purify_maxiters::Int = 30, + purify_tol::Float64 = 1e-5, + η::Real = 1e-3, + kernel::Symbol = :jackson, + tucker_tol::Real = 1e-3, + tucker_maxrank::Int = 20, + hooi_iters::Int = 3, + tol_dr::Real = 1e-8, + verbose::Bool = false) + + _check_gpu("get_conductivity_cheb2d_gpu") + H1.geometry === nothing && error("get_conductivity_cheb2d_gpu: H1.geometry is not set") + H2.geometry === nothing && error("get_conductivity_cheb2d_gpu: H2.geometry is not set") + nω = length(ωlist) + + _ensure_scale!(H1); _ensure_scale!(H2) + scale1 = H1.scale; center1 = H1.center + scale2 = H2.scale; center2 = H2.center + + # Current vertex (CPU — built once, not on the hot path) + verbose && println("conductivity_cheb2d_gpu: building current vertex...") + j_b = make_current_operator_mpo(H1, + make_displacement_mpo(H1, mu_b; tol=tol_dr); + maxdim=maxdim, cutoff=cutoff) + j_a = (mu_a == mu_b) ? j_b : + make_current_operator_mpo(H1, + make_displacement_mpo(H1, mu_a; tol=tol_dr); + maxdim=maxdim, cutoff=cutoff) + out_sites_jab = [Index(dim(s), "Jab,n=$i") for (i, s) in enumerate(H1.sites)] + J_ab = _hadamard_mpo(transpose_mpo(j_a), j_b, out_sites_jab; + maxdim=maxdim, cutoff=cutoff) + J_ab = replace_sites(J_ab, H1.sites) + ITensorMPS.truncate!(J_ab; cutoff=cutoff, maxdim=maxdim) + + # 2D GF + Tucker (CPU) — done first to derive active Tn indices + N = Ncheb + 1 + C_all = [chebyshev2d_gf_coeffs(ω, scale1, center1, scale2, center2, η, N) + for ω in ωlist] + if kernel == :jackson + g_jk = _jackson_kernel(N) + G_jk = g_jk * g_jk' + C_all = [G_jk .* C for C in C_all] + elseif kernel != :none + error("get_conductivity_cheb2d_gpu: unknown kernel=$kernel") + end + + T1 = hcat(C_all...); T2 = hcat([transpose(C) for C in C_all]...) + F1 = svd(T1); F2 = svd(T2) + r_m = min(tucker_maxrank, sum(F1.S .> tucker_tol * F1.S[1])) + r_n = min(tucker_maxrank, sum(F2.S .> tucker_tol * F2.S[1])) + U_m = F1.U[:, 1:r_m]; V_n = F2.U[:, 1:r_n] + for _ in 1:hooi_iters + Y = hcat([C * V_n for C in C_all]...); U_m = svd(Y).U[:, 1:r_m] + Z = hcat([C' * U_m for C in C_all]...); V_n = svd(Z).U[:, 1:r_n] + end + verbose && println("conductivity_cheb2d_gpu: Tucker r_m=$r_m, r_n=$r_n → $(r_m*r_n) pairs") + + row_norms_m = [norm(U_m[m, :]) for m in 1:N] + row_norms_n = [norm(V_n[n, :]) for n in 1:N] + active_m = Set(m for m in 1:N if row_norms_m[m] > tucker_tol * maximum(row_norms_m)) + active_n = H1 === H2 ? active_m : + Set(n for n in 1:N if row_norms_n[n] > tucker_tol * maximum(row_norms_n)) + verbose && println("conductivity_cheb2d_gpu: active Tns: $(length(active_m))/$N (H1)" * + (H1 === H2 ? " [reused for H2]" : ", $(length(active_n))/$N (H2)")) + + # Chebyshev moments on GPU (only active indices stored) + verbose && println("conductivity_cheb2d_gpu: Chebyshev recurrence on GPU (Ncheb=$Ncheb)...") + Tn1, _, _ = KPM_Tn_gpu(H1.mpo, Ncheb, H1.sites; + scale=scale1, center=center1, + maxdim=maxdim, cutoff=cutoff, + keep_indices=active_m, verbose=false) + Tn2 = H1 === H2 ? Tn1 : KPM_Tn_gpu(H2.mpo, Ncheb, H2.sites; + scale=scale2, center=center2, + maxdim=maxdim, cutoff=cutoff, + keep_indices=active_n, verbose=false)[1] + + # Density matrices: CPU purification → GPU + verbose && println("conductivity_cheb2d_gpu: density matrices (CPU → GPU)...") + P1_gpu = _get_density_matrix_gpu(H1, ϵF, P_method, Ncheb, maxdim, cutoff, + purify_method, purify_maxdim, purify_maxiters, + purify_tol, verbose) + P2_gpu = H1 === H2 ? P1_gpu : _get_density_matrix_gpu(H2, ϵF, P_method, Ncheb, maxdim, cutoff, + purify_method, purify_maxdim, purify_maxiters, + purify_tol, verbose) + + out_sites = [Index(dim(s), "Bubble,n=$i") for (i, s) in enumerate(H1.sites)] + + A_core = zeros(ComplexF64, r_m, r_n, nω) + for iω in 1:nω; A_core[:, :, iω] = U_m' * C_all[iω] * V_n; end + + # Tucker MPO components on GPU + verbose && println("conductivity_cheb2d_gpu: Tucker components (GPU)...") + C_tuck, B_tuck, A_tuck, E_tuck = _build_tucker_components_gpu( + Tn1, Tn2, P1_gpu, P2_gpu; + U_m=U_m, V_n=V_n, r_m=r_m, r_n=r_n, + maxdim=maxdim, cutoff=cutoff) + + # ω-independent Hadamard (GPU) → back to CPU → store D_tuck + n_pairs = r_m * r_n + verbose && println("conductivity_cheb2d_gpu: $n_pairs Hadamard products (GPU → CPU)...") + D_tuck = Matrix{Union{Nothing,MPO}}(nothing, r_m, r_n) + for s1 in 1:r_m, s2 in 1:r_n + (isnothing(A_tuck[s1]) || isnothing(B_tuck[s2]) || + isnothing(C_tuck[s1]) || isnothing(E_tuck[s2])) && continue + + had_A = _hadamard_mpo_gpu(A_tuck[s1], B_tuck[s2], out_sites; + maxdim=maxdim, cutoff=cutoff) + had_B = _hadamard_mpo_gpu(C_tuck[s1], E_tuck[s2], out_sites; + maxdim=maxdim, cutoff=cutoff) + D_gpu = ITensorMPS.truncate!(+(had_A, -1f0 * had_B; maxdim=maxdim); cutoff=cutoff) + _gpu_gc!() + + D_tuck[s1, s2] = replace_sites(_to_cpu_mpo(D_gpu), H1.sites) + + if verbose + idx = (s1 - 1) * r_n + s2 + (idx % 10 == 0 || idx == n_pairs) && + println(" ($s1,$s2)/($r_m,$r_n) [$idx/$n_pairs]") + end + end + + # Per-ω accumulation (CPU) + coeff_tol_val = 1e-12 + Π = Vector{Union{Nothing,MPO}}(nothing, nω) + for iω in 1:nω + for s1 in 1:r_m, s2 in 1:r_n + g = A_core[s1, s2, iω] + (abs(g) < coeff_tol_val || isnothing(D_tuck[s1, s2])) && continue + if Π[iω] === nothing + Π[iω] = g * D_tuck[s1, s2] + else + Π[iω] = +(Π[iω], g * D_tuck[s1, s2]; maxdim=maxdim) + ITensorMPS.truncate!(Π[iω]; cutoff=cutoff) + end + end + end + + σ = zeros(Float64, nω) + for (iω, (ω, Pi0)) in enumerate(zip(ωlist, Π)) + abs(ω) < 1e-14 && continue + isnothing(Pi0) && continue + σ[iω] = -imag(inner(J_ab, Pi0)) / ω + end + + verbose && println("conductivity_cheb2d_gpu: done.") + return σ +end + + +""" + get_bands_gpu(H, Ncheb, ω_phys_vals; kwargs...) + -> Matrix{Float64} or NamedTuple(Ak, ticks, labels) + +GPU-accelerated version of `get_bands`. + +GPU handles: the full Chebyshev MPO recurrence (the dominant cost) and the + QFT sandwich applied to each Chebyshev moment. +CPU handles: k-group setup, KPM weight matrix, final scalar accumulation. + +All GPU operators (Hamiltonian, identity, QFT pair, per-step aux projectors) +use ComplexF32 via `_to_gpu_mpo`/`_make_delta_gpu`, matching the rest of this +toolkit (see the PRECISION note at the top of GPU_tk.jl). ComplexF32 +eigendecomposition can produce NaN at very tight `cutoff` on large systems — +if `Ak` comes back all-NaN, raise `cutoff` rather than lowering it. + +All keyword arguments are identical to the TBHamiltonian overload of +`get_bands`. The return value is also identical: a plain `Matrix{Float64}` +when no `kpath` is given, or a `NamedTuple(Ak, ticks, labels)` when a +high-symmetry path is requested. + +Usage: +```julia +using CUDA +res = TensorBinding.get_bands_gpu(H, 500, omega; + kpath=[:G, :M, :Kp, :G], kpath_lattice=:honeycomb, + num_x=50, maxdim=200, printinfo=true) +heatmap(1:size(res.Ak,2), omega, res.Ak; xticks=(res.ticks, res.labels)) +``` +""" +function get_bands_gpu(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; + kpath = nothing, + kpath_lattice = nothing, + kpath_Lx = nothing, + spin_proj::Bool = false, + proj_s = nothing, + nambu_proj::Bool = false, + proj_nambu = nothing, + layer_proj::Bool = false, + proj_layer = nothing, + sublattice::Bool = false, + proj_sl = nothing, + sublat_proj::Bool = false, + k_groups_override = nothing, + xmin::Int = 0, + xmax = nothing, + num_x::Int = 60, + num_avg::Int = 1, + ymin::Int = 0, + ymax = nothing, + num_y::Int = 10, + kernel::Symbol = :jackson, + lambda::Real = 4.0, + tol::Real = 1e-9, + maxdim::Int = 100, + cutoff::Real = 1e-10, + printinfo::Bool = false) + + _check_gpu("get_bands_gpu") + + _ensure_scale!(H) + nambu_proj, spin_proj, layer_proj, sublat_proj = + _autoenable_proj(H, nambu_proj, spin_proj, layer_proj, sublat_proj) + + ω_resc = (collect(ω_phys_vals) .- H.center) ./ H.scale + Nω = length(ω_resc) + valid = [abs(ω) < 1.0 for ω in ω_resc] + W_kpm = _kpm_weight_matrix(Ncheb, ω_resc; kernel=kernel, lambda=lambda) + + # ── Auto-detect aux indices (mirrors the CPU TBHamiltonian overload) ──── + nambu_s_det, nambu_side_det = !isnothing(H.nambu_s) ? + aux_site(H, :nambu) : (nothing, :pre) + spin_s_det = H.spin_s + layer_s_det, layer_side_det = !isnothing(H.layer_s) ? + aux_site(H, :layer) : (nothing, :pre) + sublat_s_det, sublat_side_det = !isnothing(H.sublattice_s) ? + aux_site(H, :sublattice) : (nothing, :post) + + # ── L_pos: position qubits only (excluding aux sites) ─────────────────── + isnothing(H.geometry) && error("get_bands_gpu: H.geometry must be set (needed to infer D).") + D = length(H.geometry(1)) + L = H.L + L_pos = L - (spin_proj ? 1 : 0) - (!isnothing(nambu_s_det) ? 1 : 0) - + (!isnothing(layer_s_det) ? 1 : 0) - (!isnothing(sublat_s_det) ? 1 : 0) + + # ── k-path shortcut ────────────────────────────────────────────────────── + kpath_ticks = nothing; kpath_labels = nothing + if !isnothing(kpath) + isnothing(kpath_lattice) && error("get_bands_gpu: kpath requires kpath_lattice.") + Lx_kp = isnothing(kpath_Lx) ? H.L ÷ 2 : Int(kpath_Lx) + Ly_kp = H.L - Lx_kp + k_groups_override, kpath_ticks, kpath_labels = + kpath_setup(kpath_lattice, Lx_kp, Ly_kp, kpath; npts_per_segment=num_x) + end + + # ── k-groups (same logic as low-level CPU get_bands) ──────────────────── + Lx_pos = D == 2 ? div(L_pos, 2) : 0 + N_pos = 2^L_pos + if !isnothing(k_groups_override) + k_groups = k_groups_override + num_x = length(k_groups) + elseif D == 1 + _xmax = xmax === nothing ? N_pos - 1 : Int(xmax) + xcenters = ilinspace(xmin, _xmax, num_x) + half_step = num_x > 1 ? (_xmax - xmin) / (2 * num_x) : 0 + offsets = num_avg > 1 ? round.(Int, range(-half_step, half_step; length=num_avg)) : Int[0] + k_groups = [clamp.(xcenters[i] .+ offsets, 0, N_pos - 1) for i in 1:num_x] + elseif D == 2 + Nx_loc = 2^Lx_pos; Ny_loc = 2^(L_pos - Lx_pos) + num_x = min(num_x, Nx_loc) + _xmax = xmax === nothing ? Nx_loc - 1 : Int(xmax) + _ymax = ymax === nothing ? Ny_loc - 1 : Int(ymax) + xcenters = ilinspace(xmin, _xmax, Nx_loc) + ycenters = ilinspace(ymin, _ymax, Ny_loc) + hsx = num_x > 1 ? (_xmax - xmin) / (2 * num_x) : 0 + hsy = num_y > 1 ? (_ymax - ymin) / (2 * num_y) : 0 + x_offs = num_avg > 1 ? round.(Int, range(-hsx, hsx; length=num_avg)) : Int[0] + y_offs = num_avg > 1 ? round.(Int, range(-hsy, hsy; length=num_avg)) : Int[0] + k_groups = [ + begin + xs = clamp.(xcenters[i] .+ x_offs, 0, Nx_loc - 1) + ys = clamp.(ycenters[i] .+ y_offs, 0, Ny_loc - 1) + [(y << Lx_pos) | x for (x, y) in zip(xs, ys)] + end + for i in 1:num_x + ] + else + error("D must be 1 or 2") + end + + Ak_w = zeros(Float64, Nω, num_x) + + # ── Position sites (used for QFT ops and optional sublattice masks) ────── + aux_to_drop = Set{Index}() + spin_proj && push!(aux_to_drop, + isnothing(spin_s_det) ? H.sites[1] : spin_s_det::Index) + !isnothing(nambu_s_det) && push!(aux_to_drop, nambu_s_det::Index) + !isnothing(layer_s_det) && push!(aux_to_drop, layer_s_det::Index) + !isnothing(sublat_s_det) && push!(aux_to_drop, sublat_s_det::Index) + pos_sites_cpu = filter(s -> s ∉ aux_to_drop, H.sites) + + # ── Legacy sublattice masks — pre-built on CPU, moved to GPU once ──────── + # Built only when `sublattice=true` (legacy models without a sublat aux index). + # For models that use H.sublattice_s (honeycomb, kagome…), sublat_proj=true + # and sublattice=false, so this block is skipped entirely. + if sublattice + if D == 1 + mask_A_gpu = _to_gpu_mpo(_col_select_mpo(L_pos, 0, pos_sites_cpu; keep=:odd)) + mask_B_gpu = _to_gpu_mpo(_col_select_mpo(L_pos, 0, pos_sites_cpu; keep=:even)) + else + Ly_pos = L_pos - Lx_pos + mask_A_gpu = _to_gpu_mpo(_row_checker_mpo(Lx_pos, Ly_pos, pos_sites_cpu)) + mask_B_gpu = _to_gpu_mpo(MPO(pos_sites_cpu, "Id") - + _row_checker_mpo(Lx_pos, Ly_pos, pos_sites_cpu)) + end + end + + # ── GPU Ham and QFT operators ──────────────────────────────────────────── + I_mpo_cpu = MPO(H.sites, "Id") + Ham_n_cpu = (1 / H.scale) * +(H.mpo, (-H.center) * I_mpo_cpu; cutoff=cutoff) + I_mpo_gpu = _to_gpu_mpo(I_mpo_cpu) + Ham_n_gpu = _to_gpu_mpo(Ham_n_cpu) + + # QFT operators sized for pos_sites_cpu (the post-projection site list). + # Calling fix_sites maps the abstract QFT indices onto the actual pos_sites. + R_pos = length(pos_sites_cpu) + FTirev_gpu = _to_gpu_mpo(fix_sites( + MPO(TCI.reverse(QuanticsTCI.quanticsfouriermpo(R_pos; sign=-1.0, normalize=true))), + pos_sites_cpu)) + FTrev_gpu = _to_gpu_mpo(fix_sites( + MPO(TCI.reverse(QuanticsTCI.quanticsfouriermpo(R_pos; sign=+1.0, normalize=true))), + pos_sites_cpu)) + + local _nambu_side = nambu_side_det + local _layer_side = layer_side_det + local _sublat_side = sublat_side_det + local _spin_idx = isnothing(spin_s_det) ? H.sites[1] : spin_s_det + + # ── Online accumulation — fully on GPU ────────────────────────────────── + # Workflow: prebuild everything on CPU (done above), then T_n stays on GPU + # for the entire accumulate step. Only the final scalar() calls transfer + # numbers out of the GPU — no explicit MPO/MPS moves back to CPU. + # + # Per step: + # projection → _project_aux_gpu (dense ComplexF32 projector, GPU throughout) + # QFT → _apply_qft_conj_gpu (pre-built GPU QFT operators) + # diagonal → extract_diagonal_to_mps_gpu (CPU array slice, back to GPU F32) + # sampling → _eval_diag_mps_gpu (scalars pulled out of GPU directly) + function accumulate_Tn_gpu!(ak_accum, Tn_gpu, n) + # Step 0: Nambu (BdG) projection + after_nambu = nambu_proj ? + [_project_aux_gpu(Tn_gpu, nambu_s_det::Index, sec; side=_nambu_side) + for sec in (isnothing(proj_nambu) ? (1:2) : (proj_nambu:proj_nambu))] : + MPO[Tn_gpu] + + # Step 1: spin projection + after_spin = spin_proj ? + [_project_aux_gpu(T, _spin_idx, sec; side=:pre) + for T in after_nambu, sec in (isnothing(proj_s) ? (1:2) : (proj_s:proj_s))] : + after_nambu + + # Step 1c: layer projection + after_layer = if layer_proj + n_lay = dim(layer_s_det::Index) + lay_range = isnothing(proj_layer) ? (1:n_lay) : (proj_layer:proj_layer) + [_project_aux_gpu(T, layer_s_det::Index, sec; side=_layer_side) + for T in after_spin for sec in lay_range] + else + after_spin + end + + # Step 1b: sublattice aux projection + after_sl_aux = if sublat_proj + sl_range = isnothing(proj_sl) ? (1:dim(sublat_s_det::Index)) : (proj_sl:proj_sl) + [_project_aux_gpu(T, sublat_s_det::Index, sec; side=_sublat_side) + for T in after_layer for sec in sl_range] + else + after_layer + end + + # Step 2: legacy sublattice mask sandwich (all GPU — masks pre-built above) + if sublattice + masks = isnothing(proj_sl) ? [mask_A_gpu, mask_B_gpu] : + proj_sl == 1 ? [mask_A_gpu] : [mask_B_gpu] + sl_mpas = MPO[] + for T in after_sl_aux, mask in masks + push!(sl_mpas, apply(apply(mask, T; cutoff=cutoff, maxdim=maxdim), mask; + cutoff=cutoff, maxdim=maxdim)) + end + else + sl_mpas = after_sl_aux + end + + # Step 3: QFT (GPU) → diagonal MPS (GPU) → scalar sampling (GPU) + for T_gpu in sl_mpas + Tn_k_gpu = _apply_qft_conj_gpu(T_gpu, FTirev_gpu, FTrev_gpu; + tol=tol, maxdim=maxdim) + A_mps_gpu = ITensorMPS.truncate!(extract_diagonal_to_mps_gpu(Tn_k_gpu); cutoff=cutoff) + for (ik, xs) in enumerate(k_groups) + s = sum(_eval_diag_mps_gpu(A_mps_gpu, x) for x in xs) / length(xs) + for ie in 1:Nω + ak_accum[ie, ik] += W_kpm[n, ie] * s + end + end + end + + _gpu_gc!() + end + + # ── Chebyshev recurrence (GPU) ─────────────────────────────────────────── + Tkm2 = I_mpo_gpu + Tkm1 = Ham_n_gpu + + accumulate_Tn_gpu!(Ak_w, Tkm2, 1) + accumulate_Tn_gpu!(Ak_w, Tkm1, 2) + + for k in 3:Ncheb + Tk = +(2 * apply(Ham_n_gpu, Tkm1; cutoff=cutoff, maxdim=maxdim), + -Tkm2; cutoff=cutoff, maxdim=maxdim) + ITensorMPS.truncate!(Tk; cutoff=cutoff) + accumulate_Tn_gpu!(Ak_w, Tk, k) + Tkm2 = Tkm1 + Tkm1 = Tk + _gpu_gc!() + printinfo && (k % 10 == 0 || k == Ncheb) && + println(" [gpu] bands step $k/$Ncheb maxlinkdim=$(maxlinkdim(Tkm1))") + end + + # ── KPM normalization: 1 / (π² Ncheb √(1 − ε²)) ──────────────────────── + for iω in 1:Nω + valid[iω] || continue + Ak_w[iω, :] ./= (π^2 * Ncheb * sqrt(1 - ω_resc[iω]^2)) + end + + return isnothing(kpath_ticks) ? Ak_w : + (Ak = Ak_w, ticks = kpath_ticks, labels = kpath_labels) +end + + +""" + get_ldos_spatial_gpu(H, Ncheb, ω_phys_vals; kwargs...) + -> Matrix{Float64} shape (Nω × n_spatial_cols) + +GPU-accelerated version of `get_ldos_spatial` (MPO mode only). + +**Sampling procedures (`reduce`)** — see [`spatial_sampling_plan`](@ref). + +- `:point` (default) — read the LDOS at `num_x[×num_y]` cells / `x_groups` + (optionally box-averaged). Coarse grids alias thin features. +- `:block` — integrate over `num_x × num_y` blocks (powers of two) by tracing out + the within-block bits; gap-free, so thin in-gap edge channels on a large system + cannot be missed. The scalable tool for large-scale edge-state maps. + +**Column layout** + +- No sublattice DOF: `(Nω × ng)`, one column per pixel (group or block). +- Sublattice resolved: `(Nω × ng×n_sub)`, interleaved `[A₀, B₀, A₁, B₁, …]`. +- Sublattice averaged (large scale / `:block`): `(Nω × ng)`, one value per pixel. + +For `:block`, columns are row-major over coarse pixels (`col = ixp + iyp·num_x + 1`). + +**GPU/CPU split** + +GPU: entire Chebyshev MPO recurrence, aux projections, diagonal extraction, + real-space scalar sampling (point eval or block integration). +CPU: KPM weight matrix, output accumulation (scalars only). + +Keyword arguments are identical to `get_ldos_spatial` (`:mps` mode is not +available on GPU; only the single-pass MPO mode is implemented here). + +Usage +----- +```julia +using CUDA +# point map +ldos = TensorBinding.get_ldos_spatial_gpu(H, 200, ωlist; + x_groups = [[uc] for uc in 1:H.N], maxdim=200, printinfo=true) +# block-integrated large-scale edge-state map (num_x, num_y powers of two) +ldos = TensorBinding.get_ldos_spatial_gpu(H, 200, ωlist; + reduce=:block, num_x=128, num_y=128, sublattice=:average, maxdim=200) +``` +""" +function get_ldos_spatial_gpu(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; + x_groups = nothing, + num_x::Int = H.N, + num_y = nothing, + num_avg::Int = 1, + x_start::Int = 1, + x_end::Int = H.N, + grid::Bool = false, + xwin = nothing, + ywin = nothing, + box_half::Int = 0, + reduce::Symbol = :point, + sublattice::Symbol = :auto, + kernel::Symbol = :jackson, + lambda::Real = 4.0, + maxdim::Int = 100, + cutoff::Real = 1e-8, + verbose::Bool = false, + printinfo::Bool = false, + nambu_proj::Bool = false, + proj_nambu = nothing, + spin_proj::Bool = false, + proj_s = nothing, + layer_proj::Bool = false, + proj_layer = nothing, + sublat_proj::Bool = false, + proj_sl = nothing) + + _check_gpu("get_ldos_spatial_gpu") + + # ── Geometry-aware sampling plan (same convention as get_ldos_spatial) ──── + if box_half > 0 || grid || xwin !== nothing || ywin !== nothing || reduce === :block + isnothing(H.geometry) && + error("get_ldos_spatial_gpu: box_half/grid/window/block sampling requires H.geometry to be set.") + length(H.geometry(1)) == 2 || + error("get_ldos_spatial_gpu: box_half/grid/window/block sampling is only supported for 2D systems.") + end + Lx_uc = something(H.Lx, H.L ÷ 2) + Ly_uc = H.L - Lx_uc + n_sub_H = isnothing(H.sublattice_s) ? 1 : dim(H.sublattice_s) + plan = spatial_sampling_plan(H.L; + Lx = Lx_uc, + grid = grid, + reduce = reduce, + n_sub = n_sub_H, + num_x = num_x, num_y = num_y, num_avg = num_avg, + x_start = x_start, x_end = x_end, + xwin = xwin, ywin = ywin, + x_groups = x_groups, box_half = box_half, + sublattice = sublattice) + groups = plan.groups + is_block = plan.reduce === :block + block_a = plan.a + block_b = plan.b + nbx = 2^block_a # coarse pixels along x (block mode) + + _ensure_scale!(H) + nambu_proj, spin_proj, layer_proj, sublat_proj = + _autoenable_proj(H, nambu_proj, spin_proj, layer_proj, sublat_proj) + + # ── Aux site detection ─────────────────────────────────────────────────── + nambu_s_det, nambu_side_det = !isnothing(H.nambu_s) ? aux_site(H, :nambu) : (nothing, :pre) + spin_s_det = H.spin_s + layer_s_det, layer_side_det = !isnothing(H.layer_s) ? aux_site(H, :layer) : (nothing, :pre) + sublat_s_det, sublat_side_det = !isnothing(H.sublattice_s) ? aux_site(H, :sublattice) : (nothing, :post) + + has_sublat = !isnothing(sublat_s_det) + n_sub = has_sublat ? dim(sublat_s_det::Index) : 1 + # Large-scale sampling traces out the sublattice (one value per unit cell); + # atomic-scale / proj_sl=k resolves it into per-atom columns. See plan above. + resolve_sl = has_sublat && (plan.resolve_sublattice || !isnothing(proj_sl)) + average_sl = has_sublat && !resolve_sl + sl_fill = has_sublat ? + (isnothing(proj_sl) ? (1:n_sub) : (proj_sl:proj_sl)) : + (1:1) + + # ── KPM setup ──────────────────────────────────────────────────────────── + ω_vals = (collect(ω_phys_vals) .- H.center) ./ H.scale + Nω = length(ω_vals) + W = _kpm_weight_matrix(Ncheb, ω_vals; kernel=kernel, lambda=lambda) + valid = [abs(ω) < 1.0 for ω in ω_vals] + + ng = length(groups) + n_cols = average_sl ? ng : ng * n_sub + accum = zeros(Float64, Nω, n_cols) + + # ── GPU operators ──────────────────────────────────────────────────────── + I_mpo_cpu = MPO(H.sites, "Id") + Ham_n_cpu = (1 / H.scale) * +(H.mpo, (-H.center) * I_mpo_cpu; cutoff=cutoff) + I_mpo_gpu = _to_gpu_mpo(I_mpo_cpu) + Ham_n_gpu = _to_gpu_mpo(Ham_n_cpu) + + local _nambu_side = nambu_side_det + local _layer_side = layer_side_det + local _sublat_side = sublat_side_det + local _spin_idx = isnothing(spin_s_det) ? H.sites[1] : spin_s_det + + # ── Online accumulation (GPU) ──────────────────────────────────────────── + # No QFT sandwich: positions are real-space, so after projections we extract + # the diagonal MPS and reduce it to per-pixel scalars: + # reduce=:point → evaluate at each group's cells (big-endian) and average, + # reduce=:block → integrate over each coarse block by tracing the within- + # block bits (_eval_block_mps_gpu). Both return (u, value) + # where u is the 1-indexed output pixel (column unit). + function spatial_vals_gpu(diag_mps) + if is_block + return [(ixp + iyp * nbx + 1, + _eval_block_mps_gpu(diag_mps, ixp, iyp, block_a, block_b, Lx_uc, Ly_uc)) + for iyp in 0:(2^block_b - 1) for ixp in 0:(nbx - 1)] + else + return [(ig, sum(_eval_mps_bigendian_gpu(diag_mps, x - 1) for x in grp) / length(grp)) + for (ig, grp) in enumerate(groups)] + end + end + + function accumulate_Tn_ldos_gpu!(ak_accum, Tn_gpu, n) + after_nambu = nambu_proj ? + [_project_aux_gpu(Tn_gpu, nambu_s_det::Index, sec; side=_nambu_side) + for sec in (isnothing(proj_nambu) ? (1:2) : (proj_nambu:proj_nambu))] : + MPO[Tn_gpu] + + after_spin = spin_proj ? + [_project_aux_gpu(T, _spin_idx, sec; side=:pre) + for T in after_nambu, sec in (isnothing(proj_s) ? (1:2) : (proj_s:proj_s))] : + after_nambu + + after_layer = if layer_proj + n_lay = dim(layer_s_det::Index) + lay_range = isnothing(proj_layer) ? (1:n_lay) : (proj_layer:proj_layer) + [_project_aux_gpu(T, layer_s_det::Index, sec; side=_layer_side) + for T in after_spin for sec in lay_range] + else + after_spin + end + + if has_sublat + # Resolved → per-atom column; averaged → fold all atoms into the + # single per-pixel column u (mean over the n_sub atoms). + for Tl in after_layer, s in sl_fill + Tp = _project_aux_gpu(Tl, sublat_s_det::Index, s; side=_sublat_side) + diag_mps = ITensorMPS.truncate!(extract_diagonal_to_mps_gpu(Tp); cutoff=cutoff) + scale = average_sl ? 1.0 / n_sub : 1.0 + for (u, val) in spatial_vals_gpu(diag_mps) + c = average_sl ? u : (u - 1) * n_sub + s + for iω in 1:Nω + valid[iω] || continue + ak_accum[iω, c] += W[n, iω] * val * scale + end + end + end + else + for Tp in after_layer + diag_mps = ITensorMPS.truncate!(extract_diagonal_to_mps_gpu(Tp); cutoff=cutoff) + for (u, val) in spatial_vals_gpu(diag_mps) + for iω in 1:Nω + valid[iω] || continue + ak_accum[iω, u] += W[n, iω] * val + end + end + end + end + + _gpu_gc!() + end + + # ── Chebyshev recurrence (GPU) ─────────────────────────────────────────── + cutoff < 1e-6 && @warn "get_ldos_spatial_gpu: cutoff=$cutoff is below 1e-6; ComplexF32 eigendecomposition may produce NaN on large systems — consider cutoff ≥ 1e-4." + gpu_cutoff = Float64(cutoff) + Tkm2 = I_mpo_gpu + Tkm1 = Ham_n_gpu + + accumulate_Tn_ldos_gpu!(accum, Tkm2, 1) + accumulate_Tn_ldos_gpu!(accum, Tkm1, 2) + + for k in 3:Ncheb + Tk = +(2 * apply(Ham_n_gpu, Tkm1; cutoff=gpu_cutoff, maxdim=maxdim), + -Tkm2; cutoff=gpu_cutoff, maxdim=maxdim) + ITensorMPS.truncate!(Tk; cutoff=gpu_cutoff) + accumulate_Tn_ldos_gpu!(accum, Tk, k) + Tkm2 = Tkm1 + Tkm1 = Tk + _gpu_gc!() + (verbose || printinfo) && (k % 10 == 0 || k == Ncheb) && + println(" [gpu] ldos step $k/$Ncheb maxlinkdim=$(maxlinkdim(Tkm1))") + end + + # ── KPM normalization ──────────────────────────────────────────────────── + result = zeros(Float64, Nω, n_cols) + for iω in 1:Nω + valid[iω] || continue + result[iω, :] = accum[iω, :] ./ (π^2 * Ncheb * sqrt(1 - ω_vals[iω]^2)) + end + + return result +end + + +""" + get_dos_stochastic_gpu(H, Ncheb, ω_phys_vals; kwargs...) + -> Vector{Float64} length Nω + +GPU-accelerated stochastic density of states via MPS Chebyshev KPM. + +For each random sample the scaled Hamiltonian MPO lives on GPU and the product- +state MPS is transferred to GPU once before the recursion starts. +The Chebyshev moments ⟨ψ₀|T_n(H̃)|ψ₀⟩ are scalars pulled to CPU at each step. + +Signature and optional kwargs are identical to `get_dos_stochastic` (CPU). +`N_bound` (exciton bound-sector enrichment) is supported. Use +`dos_weighting=:sample` to return the unweighted sampled signal +`avg_full + avg_bound`, which is useful when visualising exciton peaks that are +otherwise hidden by continuum phase-space factors in the trace DOS. +For exciton Hamiltonians, `continuum_only=true` samples ordered electron-hole +product states with `x_e != x_h` for the `N_sample` branch. + +`kernel=:hodc` selects the Higher-Order Delta Chebyshev reconstruction +(`eta`, `m_order` control the contour); its weights already carry the full KPM +normalisation, so no `√(1−ω²)` denominator is applied. `eta=0` falls back to +`1/(Ncheb+1)`. Otherwise `kernel` is a convolution kernel (`:jackson` default, +`:lorentz` with `lambda`, `:fejer`, `:dirichlet`). +""" +function get_dos_stochastic_gpu(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; + N_sample::Int = 50, + N_bound::Int = 0, + seed::Union{Int,Nothing} = 42, + normalize::Bool = false, + dos_weighting::Symbol = :trace, + kernel::Symbol = :jackson, + lambda::Real = 4.0, + eta::Real = 0.0, + m_order::Int = 4, + maxdim::Int = 100, + cutoff::Real = 1e-8, + verbose::Bool = false, + printinfo::Bool = false, + continuum_only::Bool = false, + nambu_proj::Bool = false, + proj_nambu = nothing, + spin_proj::Bool = false, + proj_s = nothing, + layer_proj::Bool = false, + proj_layer = nothing, + sublat_proj::Bool = false, + proj_sl = nothing) + + _check_gpu("get_dos_stochastic_gpu") + cutoff < 1e-6 && @warn "get_dos_stochastic_gpu: cutoff=$cutoff is below 1e-6; ComplexF32 eigendecomposition may produce NaN on large systems — consider cutoff ≥ 1e-4." + _ensure_scale!(H) + dos_weighting in (:trace, :sample) || + error("get_dos_stochastic_gpu: dos_weighting must be :trace or :sample.") + N_sample >= 0 || error("get_dos_stochastic_gpu: N_sample must be non-negative.") + N_bound >= 0 || error("get_dos_stochastic_gpu: N_bound must be non-negative.") + + I_mpo_cpu = MPO(H.sites, "Id") + Ham_n_cpu = (1 / H.scale) * +(H.mpo, (-H.center) * I_mpo_cpu; cutoff=cutoff) + Ham_n_gpu = _to_gpu_mpo(Ham_n_cpu) + + D = prod(ITensors.dim(s) for s in H.sites) + N_phys = H.N + is_exc = length(H.sites) == 2 * H.L + continuum_only && !is_exc && + error("get_dos_stochastic_gpu: continuum_only=true requires an exciton Hamiltonian.") + continuum_only && N_phys < 2 && + error("get_dos_stochastic_gpu: continuum_only=true requires H.N >= 2.") + + (; nambu_range, spin_range, layer_range, sl_range, any_aux_proj) = + _aux_setup(H, nambu_proj, proj_nambu, spin_proj, proj_s, + layer_proj, proj_layer, sublat_proj, proj_sl) + + ω_vals = (collect(ω_phys_vals) .- H.center) ./ H.scale + Nω = length(ω_vals) + W, denom = _dos_weight_matrix(Ncheb, ω_vals; + kernel=kernel, lambda=lambda, eta=eta, m_order=m_order) + valid = [abs(ω) < 1.0 for ω in ω_vals] + + rng = seed === nothing ? Random.default_rng() : Random.MersenneTwister(seed) + accum_full = zeros(Float64, Nω) + accum_bound = zeros(Float64, Nω) + + function _run_kpm_mps_gpu!(psi0_gpu, accum, weight) + apply_kwargs = (cutoff=Float64(cutoff), maxdim=maxdim) + function kpm_step!(phi, n) + mu = Float64(real(inner(psi0_gpu, phi))) + for iω in 1:Nω + valid[iω] || continue + accum[iω] += W[n, iω] * mu * weight + end + end + phi_km2 = psi0_gpu + phi_km1 = apply(Ham_n_gpu, phi_km2; apply_kwargs...) + kpm_step!(phi_km2, 1) + kpm_step!(phi_km1, 2) + for k in 3:Ncheb + phi_k = +(2 * apply(Ham_n_gpu, phi_km1; apply_kwargs...), + -phi_km2; apply_kwargs...) + kpm_step!(phi_k, k) + phi_km2 = phi_km1 + phi_km1 = phi_k + end + _gpu_gc!() + return maxlinkdim(phi_km1) + end + + function _exciton_pair_mps_gpu_seed(xe::Int, xh::Int) + Lphys = div(length(H.sites), 2) + bits_e = to_binary_vector(xe - 1, Lphys) + bits_h = to_binary_vector(xh - 1, Lphys) + state = Vector{String}(undef, 2 * Lphys) + for b in 1:Lphys + state[2b - 1] = bits_e[b] + state[2b] = bits_h[b] + end + return MPS(H.sites, state) + end + + if any_aux_proj + continuum_only && + error("get_dos_stochastic_gpu: continuum_only is not supported together with auxiliary projections.") + D_eff = N_phys + if N_sample > 0 + xs = rand(rng, 1:N_phys, N_sample) + for (i, x) in enumerate(xs) + for σ_n in nambu_range, σ_s in spin_range, σ_l in layer_range, σ_sl in sl_range + psi0_gpu = _to_gpu_mps(_ldos_make_psi0(H, x, σ_n, σ_s, σ_l, σ_sl)) + χ = _run_kpm_mps_gpu!(psi0_gpu, accum_full, 1.0/N_sample) + (verbose || printinfo) && i % 10 == 0 && + σ_n == first(nambu_range) && σ_s == first(spin_range) && + σ_l == first(layer_range) && σ_sl == first(sl_range) && + println(" [gpu] dos sample $i/$N_sample (projected) maxlinkdim=$χ") + end + end + end + + result = zeros(Float64, Nω) + for iω in 1:Nω + valid[iω] || continue + if dos_weighting == :sample + result[iω] = accum_full[iω] / denom[iω] + else + result[iω] = D_eff * accum_full[iω] / denom[iω] + end + end + normalize && dos_weighting == :trace && (result ./= D_eff) + return result + end + + # ── Full / continuum Hilbert-space sampling ─────────────────────────────── + if N_sample > 0 + if continuum_only + xs_e = rand(rng, 1:N_phys, N_sample) + ys_h = rand(rng, 1:(N_phys - 1), N_sample) + for i in 1:N_sample + xe = xs_e[i] + xh = ys_h[i] < xe ? ys_h[i] : ys_h[i] + 1 + psi0_gpu = _to_gpu_mps(_exciton_pair_mps_gpu_seed(xe, xh)) + χ = _run_kpm_mps_gpu!(psi0_gpu, accum_full, 1.0/N_sample) + (verbose || printinfo) && i % 10 == 0 && + println(" [gpu] dos continuum sample $i/$N_sample (xe=$xe, xh=$xh) maxlinkdim=$χ") + end + else + samples = rand(rng, 0:(D - 1), N_sample) + for (i, k) in enumerate(samples) + psi0_gpu = _to_gpu_mps(_basis_state_mps(k, H.sites)) + χ = _run_kpm_mps_gpu!(psi0_gpu, accum_full, 1.0/N_sample) + (verbose || printinfo) && i % 10 == 0 && + println(" [gpu] dos sample $i/$N_sample maxlinkdim=$χ") + end + end + end + + # ── Bound-sector enrichment (exciton) ───────────────────────────────────── + if N_bound > 0 && is_exc + xs = rand(rng, 1:N_phys, N_bound) + for (i, x) in enumerate(xs) + psi0_gpu = _to_gpu_mps(mpsexciton(x, H.sites)) + χ = _run_kpm_mps_gpu!(psi0_gpu, accum_bound, 1.0/N_bound) + (verbose || printinfo) && i % 10 == 0 && + println(" [gpu] dos bound sample $i/$N_bound (x=$x) maxlinkdim=$χ") + end + end + + # ── Normalise ───────────────────────────────────────────────────────────── + result = zeros(Float64, Nω) + for iω in 1:Nω + valid[iω] || continue + if dos_weighting == :sample + result[iω] = (accum_full[iω] + + ((N_bound > 0 && is_exc) ? accum_bound[iω] : 0.0)) / denom[iω] + elseif N_bound > 0 && is_exc + result[iω] = ((D - N_phys) * accum_full[iω] + + N_phys * accum_bound[iω]) / denom[iω] + elseif continuum_only && is_exc + result[iω] = (D - N_phys) * accum_full[iω] / denom[iω] + else + result[iω] = D * accum_full[iω] / denom[iω] + end + end + if normalize && dos_weighting == :trace + norm_dim = (continuum_only && is_exc && N_bound == 0) ? (D - N_phys) : D + result ./= norm_dim + end + return result +end + + +""" + get_exciton_ldos_spatial_gpu(H, Ncheb, ω_phys_vals; X_list, X_groups, + num_x, num_avg, x_start, x_end, kernel, + lambda, eta, m_order, maxdim, cutoff, verbose, printinfo) + -> Matrix{Float64} (Nω × n_X) + +GPU-accelerated spatial exciton LDOS: for each bound exciton position `X` (electron += hole = X, 1-indexed in `1:H.N`) the local spectral weight +`A(X, ω) = ⟨X,X|δ(ω−H)|X,X⟩` is reconstructed from the Chebyshev moments +`μ_n = ⟨X,X|T_n(H̃)|X,X⟩`. One GPU MPS Chebyshev recursion is run per X starting +from `|X,X⟩ = mpsexciton(X, H.sites)`; moments are scalars pulled to CPU. + +This is the spatial / batched GPU analog of the CPU `get_exciton_ldos` (same +reconstruction via the KPM weight matrix), columns ordered as `X_list` or as +the coarse centers of the generated spatial groups. + +`X_list` selects the positions directly. `X_groups` selects explicit position +groups and averages all probes inside each group into one output column. If +neither is provided, `num_x` coarse groups are built over `x_start:x_end`; each +group contains `num_avg` subpositions with the same stride convention as +`get_ldos_spatial`. All positions are 1-indexed in `1:H.N`. + +`kernel=:hodc` selects the Higher-Order Delta Chebyshev reconstruction (`eta`, +`m_order`); its weights carry the full normalisation (no `√(1−ω²)` denominator). +`eta=0` falls back to `1/(Ncheb+1)`. Otherwise `kernel` is a convolution kernel +(`:jackson` default, `:lorentz` with `lambda`, `:fejer`, `:dirichlet`). +""" +function get_exciton_ldos_spatial_gpu(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; + X_list = nothing, + X_groups = nothing, + x_groups = nothing, + num_x::Int = H.N, + num_avg::Int = 1, + x_start::Int = 1, + x_end::Int = H.N, + kernel::Symbol = :jackson, + lambda::Real = 4.0, + eta::Real = 0.0, + m_order::Int = 4, + maxdim::Int = 100, + cutoff::Real = 1e-8, + verbose::Bool = false, + printinfo::Bool = false) + + _check_gpu("get_exciton_ldos_spatial_gpu") + cutoff < 1e-6 && @warn "get_exciton_ldos_spatial_gpu: cutoff=$cutoff is below 1e-6; ComplexF32 eigendecomposition may produce NaN on large systems — consider cutoff ≥ 1e-4." + _ensure_scale!(H) + length(H.sites) == 2 * H.L || + error("get_exciton_ldos_spatial_gpu: H is not an exciton Hamiltonian (expected length(H.sites) == 2*H.L).") + + X_groups !== nothing && x_groups !== nothing && + error("get_exciton_ldos_spatial_gpu: pass only one of X_groups or x_groups.") + X_list !== nothing && (X_groups !== nothing || x_groups !== nothing) && + error("get_exciton_ldos_spatial_gpu: pass either X_list or grouped positions, not both.") + + group_arg = X_groups !== nothing ? X_groups : x_groups + groups = if group_arg !== nothing + group_arg isa AbstractVector{<:AbstractVector} ? + [collect(Int, grp) for grp in group_arg] : + [[Int(x)] for x in group_arg] + elseif X_list !== nothing + [[Int(x)] for x in X_list] + else + num_x > 0 || error("get_exciton_ldos_spatial_gpu: num_x must be positive.") + num_avg > 0 || error("get_exciton_ldos_spatial_gpu: num_avg must be positive.") + 1 <= x_start <= x_end <= H.N || + error("get_exciton_ldos_spatial_gpu: expected 1 <= x_start <= x_end <= H.N.") + window = x_end - x_start + 1 + num_x <= window || + error("get_exciton_ldos_spatial_gpu: num_x=$num_x exceeds sampling window length $window.") + dx = div(window, num_x) + dx_sub = max(1, div(dx, num_avg)) + [[x_start + (i - 1) * dx + k * dx_sub + for k in 0:num_avg-1 + if x_start + (i - 1) * dx + k * dx_sub <= x_end] + for i in 1:num_x] + end + isempty(groups) && error("get_exciton_ldos_spatial_gpu: no spatial groups were selected.") + for grp in groups + isempty(grp) && error("get_exciton_ldos_spatial_gpu: empty spatial group.") + all(x -> 1 <= x <= H.N, grp) || + error("get_exciton_ldos_spatial_gpu: all positions must lie in 1:H.N.") + end + Xs = first.(groups) + + I_mpo_cpu = MPO(H.sites, "Id") + Ham_n_cpu = (1 / H.scale) * +(H.mpo, (-H.center) * I_mpo_cpu; cutoff=cutoff) + Ham_n_gpu = _to_gpu_mpo(Ham_n_cpu) + + ω_vals = (collect(ω_phys_vals) .- H.center) ./ H.scale + Nω = length(ω_vals) + W, denom = _dos_weight_matrix(Ncheb, ω_vals; + kernel=kernel, lambda=lambda, eta=eta, m_order=m_order) + valid = [abs(ω) < 1.0 for ω in ω_vals] + + nX = length(groups) + result = zeros(Float64, Nω, nX) + apply_kwargs = (cutoff=Float64(cutoff), maxdim=maxdim) + + for (j, group) in enumerate(groups) + last_linkdim = 0 + + for X in group + psi0_gpu = _to_gpu_mps(mpsexciton(X, H.sites)) + accum = zeros(Float64, Nω) + + function kpm_step!(phi, n) + mu = Float64(real(inner(psi0_gpu, phi))) + for iω in 1:Nω + valid[iω] || continue + accum[iω] += W[n, iω] * mu + end + end + + phi_km2 = psi0_gpu + phi_km1 = apply(Ham_n_gpu, phi_km2; apply_kwargs...) + kpm_step!(phi_km2, 1) + kpm_step!(phi_km1, 2) + for k in 3:Ncheb + phi_k = +(2 * apply(Ham_n_gpu, phi_km1; apply_kwargs...), + -phi_km2; apply_kwargs...) + kpm_step!(phi_k, k) + phi_km2 = phi_km1 + phi_km1 = phi_k + end + + last_linkdim = maxlinkdim(phi_km1) + for iω in 1:Nω + valid[iω] || continue + result[iω, j] += accum[iω] / denom[iω] + end + + _gpu_gc!() + end + + for iω in 1:Nω + result[iω, j] /= length(group) + end + (verbose || printinfo) && (j % 5 == 0 || j == nX) && + println(" [gpu] exciton ldos $j/$nX (X=$(Xs[j]), n_avg=$(length(group))) maxlinkdim=$last_linkdim") + end + + return result +end + + +# ============================================================ +# GPU Chern marker +# ============================================================ + +""" + get_C_gpu(H::TBHamiltonian, xfunc=nothing, yfunc=nothing; kwargs...) -> Function + +GPU-accelerated real-space Chern marker. Mirrors `get_C` exactly but runs all +MPO×MPO products (projector assembly and C1–C4 construction) on GPU in F32. + +Returns the same closure `C_at(uc::Int) -> ComplexF64` as `get_C`. + +# Key differences from `get_C` +- All `apply`/`truncate!` operations run on GPU tensors (F32). +- `cutoff` is passed directly; a warning is emitted if `cutoff < 1e-6` since + ComplexF32 eigendecompositions can produce NaN on large systems at tight cutoffs. +- The projector is built on CPU first (via `_get_projector`), then moved to GPU. + For method=:mcweeny this means the purification loop runs on GPU. +- `sequential` mode is not supported (non-sequential quenched is always used). + +All keyword arguments are identical to `get_C`. +""" +function get_C_gpu(H::TBHamiltonian, xfunc=nothing, yfunc=nothing; + method::Symbol = :mcweeny, + fermi::Real = 0.0, + l = nothing, + Λ::Real = 10, + Lambda = nothing, + Nchebychev::Int = 300, + maxdim::Int = 500, + cutoff::Real = 1e-8, + Nel = nothing, + quenched::Bool = true, + printinfo::Bool = false) + + _check_gpu("get_C_gpu") + cutoff < 1e-6 && @warn "get_C_gpu: cutoff=$cutoff is below 1e-6; ComplexF32 eigendecomposition may produce NaN on large systems — consider cutoff ≥ 1e-4." + Λ_val = Lambda !== nothing ? Float64(Lambda) : Float64(Λ) + ak = (cutoff=Float64(cutoff), maxdim=maxdim) + + # ── geometry ────────────────────────────────────────────────────────────── + if xfunc === nothing || yfunc === nothing + geom = H.geometry_uc !== nothing ? H.geometry_uc : + H.geometry !== nothing ? H.geometry : + error("get_C_gpu: H has no geometry; provide xfunc and yfunc explicitly.") + xfunc === nothing && (xfunc = (i, _) -> geom(i + 1)[1]) + yfunc === nothing && (yfunc = (i, _) -> geom(i + 1)[2]) + end + + # ── sublattice bookkeeping (mirrors get_C_op_MPO_from_P) ────────────────── + L = H.L + l_bits = l === nothing ? div(L, 2) : l + L_chain = 2^l_bits + sites = H.sites + n_sub = length(sites) > L ? dim(sites[L+1]) : 1 + has_sub = n_sub > 1 + pos_sites = has_sub ? collect(sites[1:L]) : collect(sites) + sub_s = has_sub ? sites[L+1] : nothing + I_mat = has_sub ? Matrix{Float64}(LinearAlgebra.I, n_sub, n_sub) : nothing + + xfunc_pos = has_sub ? ((i, Lc) -> xfunc(i * n_sub, Lc)) : xfunc + yfunc_pos = has_sub ? ((i, Lc) -> yfunc(i * n_sub, Lc)) : yfunc + + a1x = xfunc_pos(1, L_chain) - xfunc_pos(0, L_chain) + a1y = yfunc_pos(1, L_chain) - yfunc_pos(0, L_chain) + a2x = xfunc_pos(L_chain, L_chain) - xfunc_pos(0, L_chain) + a2y = yfunc_pos(L_chain, L_chain) - yfunc_pos(0, L_chain) + A_cell = abs(a1x * a2y - a1y * a2x) + + # ── projector: build initial guess on CPU, purify on GPU ────────────────── + printinfo && println("[gpu] Building initial projector guess (CPU)...") + _ensure_scale!(H) + P0_cpu = purification_initial_guess(H; ϵF=fermi, maxdim=maxdim, cutoff=cutoff) + P = _to_gpu_mpo(P0_cpu) + + if method == :mcweeny + printinfo && println("[gpu] McWeeny purification on GPU...") + maxiters_mc = 30 + tol_mc = 1e-5 + for iter in 1:maxiters_mc + P2 = apply(P, P; ak...) + ITensorMPS.truncate!(P2; cutoff=Float64(cutoff)) + err = let diff = +(P2, -1.0 * P; cutoff=1e-12) + n = norm(diff); d = norm(P); d > 0 ? n / d : n + end + printinfo && iter % 5 == 0 && + println(" McWeeny iter $iter: err=$err maxlinkdim=$(maxlinkdim(P))") + err < tol_mc && break + P_inte = +(3.0 * P, -2.0 * P2; cutoff=Float64(cutoff)) + P = apply(P, P_inte; ak...) + ITensorMPS.truncate!(P; cutoff=Float64(cutoff)) + _gpu_gc!() + end + H._density_cache = nothing # don't cache GPU MPO in CPU field + elseif method == :sp2 + Nel_val = Nel === nothing ? H.N ÷ 2 : Int(Nel) + printinfo && println("[gpu] SP2 purification on GPU (Nel=$Nel_val)...") + maxiters_sp = 40 + tol_sp = 1e-5 + for iter in 1:maxiters_sp + P2 = apply(P, P; ak...) + ITensorMPS.truncate!(P2; cutoff=Float64(cutoff)) + err = let diff = +(P2, -1.0 * P; cutoff=1e-12) + n = norm(diff); d = norm(P); d > 0 ? n / d : n + end + printinfo && println(" SP2 iter $iter: err=$err maxlinkdim=$(maxlinkdim(P))") + err < tol_sp && break + tr_P2 = real(tr(P2)) + if tr_P2 >= Nel_val + P = P2 + else + P = +(2.0 * P, -1.0 * P2; ak...) + ITensorMPS.truncate!(P; cutoff=Float64(cutoff)) + end + _gpu_gc!() + end + elseif method == :KPM + # KPM: use CPU projector, just move to GPU + P_cpu = _get_projector(H; method=:KPM, fermi=fermi, Nchebychev=Nchebychev, + maxdim=maxdim, cutoff=cutoff) + P = _to_gpu_mpo(P_cpu) + else + error("get_C_gpu: unknown method :$method. Choose :mcweeny, :sp2, or :KPM") + end + printinfo && println("[gpu] Projector ready, maxlinkdim=$(maxlinkdim(P))") + + # ── Q = I − P on GPU ────────────────────────────────────────────────────── + I_gpu = _to_gpu_mpo(MPO(collect(sites), "Id")) + Q = +(I_gpu, -1.0 * P; ak...) + ITensorMPS.truncate!(Q; cutoff=Float64(cutoff)) + _gpu_gc!() + + # ── basis MPS closure (returns GPU MPS) ─────────────────────────────────── + make_alpha_gpu = if has_sub + all_sites = collect(sites) + alpha -> begin + n_cell = (alpha - 1) ÷ n_sub + sub = (alpha - 1) % n_sub + 1 + pos_bits = [((n_cell >> (L - i)) & 1) + 1 for i in 1:L] + _to_gpu_mps(_product_state_mps(all_sites, [pos_bits; sub])) + end + else + alpha -> _to_gpu_mps(binary_to_MPS(alpha - 1, L, collect(sites))) + end + + if quenched + # ── position operators on GPU ────────────────────────────────────────── + sinX_gpu = _to_gpu_mpo(has_sub ? + postpend_op(get_sinx_op(L, pos_sites, L_chain, Λ_val, xfunc_pos), sub_s, I_mat) : + get_sinx_op(L, pos_sites, L_chain, Λ_val, xfunc_pos)) + cosX_gpu = _to_gpu_mpo(has_sub ? + postpend_op(get_cosx_op(L, pos_sites, L_chain, Λ_val, xfunc_pos), sub_s, I_mat) : + get_cosx_op(L, pos_sites, L_chain, Λ_val, xfunc_pos)) + sinY_gpu = _to_gpu_mpo(has_sub ? + postpend_op(get_siny_op(L, pos_sites, L_chain, Λ_val, yfunc_pos), sub_s, I_mat) : + get_siny_op(L, pos_sites, L_chain, Λ_val, yfunc_pos)) + cosY_gpu = _to_gpu_mpo(has_sub ? + postpend_op(get_cosy_op(L, pos_sites, L_chain, Λ_val, yfunc_pos), sub_s, I_mat) : + get_cosy_op(L, pos_sites, L_chain, Λ_val, yfunc_pos)) + printinfo && println("[gpu] Position operators on GPU.") + + # ── 8 intermediate MPO products ──────────────────────────────────────── + sinY_P = apply(sinY_gpu, P; ak...); cosY_P = apply(cosY_gpu, P; ak...) + P_sinX = apply(P, sinX_gpu; ak...); P_cosX = apply(P, cosX_gpu; ak...) + sinY_Q = apply(sinY_gpu, Q; ak...); cosY_Q = apply(cosY_gpu, Q; ak...) + Q_sinX = apply(Q, sinX_gpu; ak...); Q_cosX = apply(Q, cosX_gpu; ak...) + printinfo && println("[gpu] 8 intermediate MPO products done.") + _gpu_gc!() + + # C1 = Q sinX P sinY Q − P sinX Q sinY P + C1 = +(apply(apply(Q_sinX, P; ak...), sinY_Q; ak...), + -apply(apply(P_sinX, Q; ak...), sinY_P; ak...); ak...) + ITensorMPS.truncate!(C1; cutoff=Float64(cutoff)) + printinfo && println("[gpu] C1 done, maxlinkdim=$(maxlinkdim(C1))") + _gpu_gc!() + + # C2 = Q cosX P cosY Q − P cosX Q cosY P + C2 = +(apply(apply(Q_cosX, P; ak...), cosY_Q; ak...), + -apply(apply(P_cosX, Q; ak...), cosY_P; ak...); ak...) + ITensorMPS.truncate!(C2; cutoff=Float64(cutoff)) + printinfo && println("[gpu] C2 done, maxlinkdim=$(maxlinkdim(C2))") + _gpu_gc!() + + # C3 = Q sinX P cosY Q − P sinX Q cosY P + C3 = +(apply(apply(Q_sinX, P; ak...), cosY_Q; ak...), + -apply(apply(P_sinX, Q; ak...), cosY_P; ak...); ak...) + ITensorMPS.truncate!(C3; cutoff=Float64(cutoff)) + printinfo && println("[gpu] C3 done, maxlinkdim=$(maxlinkdim(C3))") + _gpu_gc!() + + # C4 = Q cosX P sinY Q − P cosX Q sinY P + C4 = +(apply(apply(Q_cosX, P; ak...), sinY_Q; ak...), + -apply(apply(P_cosX, Q; ak...), sinY_P; ak...); ak...) + ITensorMPS.truncate!(C4; cutoff=Float64(cutoff)) + printinfo && println("[gpu] C4 done. Closure ready.") + _gpu_gc!() + + calculate_chern_number = uc -> begin + sum(sub -> begin + alpha = (uc - 1) * n_sub + sub + α = make_alpha_gpu(alpha) + x = xfunc(alpha - 1, L_chain) + y = yfunc(alpha - 1, L_chain) + cos_x, sin_x = cos(x / Λ_val), sin(x / Λ_val) + cos_y, sin_y = cos(y / Λ_val), sin(y / Λ_val) + ch = cos_x * cos_y * inner(α', C1, α) + ch += sin_x * sin_y * inner(α', C2, α) + ch -= cos_x * sin_y * inner(α', C3, α) + ch -= sin_x * cos_y * inner(α', C4, α) + ch * 2im * π * Λ_val^2 + end, 1:n_sub) / A_cell + end + + else + # flat (non-quenched) mode + x_op = has_sub ? + postpend_op(get_diagonal_mpo(L, pos_sites, i -> xfunc_pos(i-1, L_chain)), sub_s, I_mat) : + get_diagonal_mpo(L, pos_sites, i -> xfunc_pos(i-1, L_chain)) + y_op = has_sub ? + postpend_op(get_diagonal_mpo(L, pos_sites, i -> yfunc_pos(i-1, L_chain)), sub_s, I_mat) : + get_diagonal_mpo(L, pos_sites, i -> yfunc_pos(i-1, L_chain)) + x_gpu = _to_gpu_mpo(x_op) + y_gpu = _to_gpu_mpo(y_op) + + T1 = apply(Q, apply(x_gpu, apply(P, apply(y_gpu, Q; ak...); ak...); ak...); ak...) + T2 = apply(P, apply(x_gpu, apply(Q, apply(y_gpu, P; ak...); ak...); ak...); ak...) + C_op = 2im * π * +(T1, -1.0 * T2; ak...) + ITensorMPS.truncate!(C_op; cutoff=Float64(cutoff)) + _gpu_gc!() + + calculate_chern_number = uc -> begin + sum(sub -> begin + alpha = (uc - 1) * n_sub + sub + α = make_alpha_gpu(alpha) + inner(α', C_op, α) + end, 1:n_sub) / A_cell + end + end + + return calculate_chern_number +end + + +# ============================================================ +# GPU magnetic Hubbard SCF +# ============================================================ + +# GPU McWeeny purification of a (rescaled) single-channel Hamiltonian. +# Builds the initial guess on CPU, moves it to GPU, iterates the McWeeny map +# on GPU (F32), and returns the purified density matrix back on CPU +# (ComplexF64). Mirrors the purification loop in `get_C_gpu`. +function _mcweeny_purify_gpu(H::TBHamiltonian; ϵF::Real, + maxdim::Int, cutoff::Real, + maxiters::Int, tol::Real, + return_gpu::Bool = false) + ak = (cutoff = Float64(cutoff), maxdim = maxdim) + P0_cpu = purification_initial_guess(H; ϵF=ϵF, maxdim=maxdim, cutoff=Float64(cutoff)) + P = _to_gpu_mpo(P0_cpu) + for iter in 1:maxiters + P2 = apply(P, P; ak...) + ITensorMPS.truncate!(P2; cutoff=Float64(cutoff)) + err = let diff = +(P2, -1.0 * P; cutoff=1e-12) + n = norm(diff); d = norm(P); d > 0 ? n / d : n + end + err < tol && break + P_inte = +(3.0 * P, -2.0 * P2; cutoff=Float64(cutoff)) + P = apply(P, P_inte; ak...) + ITensorMPS.truncate!(P; cutoff=Float64(cutoff)) + _gpu_gc!() + end + return return_gpu ? P : _to_cpu_mpo(P) +end + +function _purification_initial_guess_gpu(H_mpo_gpu::MPO, sites; + ϵF::Real, + scale::Real, + center::Real = 0.0, + maxdim::Int, + cutoff::Real, + Id_gpu::Union{Nothing,MPO} = nothing) + scale == 0 && error("_purification_initial_guess_gpu: scale must be non-zero.") + Id = Id_gpu === nothing ? _to_gpu_mpo(MPO(collect(sites), "Id")) : Id_gpu + coeff_I = 0.5 + (ϵF + center) / (2 * scale) + coeff_H = -0.5 / scale + ρ0 = +(coeff_I * Id, coeff_H * H_mpo_gpu; cutoff=Float64(cutoff)) + ITensorMPS.truncate!(ρ0; maxdim=maxdim, cutoff=Float64(cutoff)) + return ρ0 +end + +function _mcweeny_purify_mpo_gpu(H_mpo_gpu::MPO, sites; + ϵF::Real, + scale::Real, + center::Real = 0.0, + Id_gpu::Union{Nothing,MPO} = nothing, + maxdim::Int, + cutoff::Real, + maxiters::Int, + tol::Real) + ak = (cutoff = Float64(cutoff), maxdim = maxdim) + P = _purification_initial_guess_gpu(H_mpo_gpu, sites; + ϵF=ϵF, scale=scale, center=center, maxdim=maxdim, + cutoff=cutoff, Id_gpu=Id_gpu) + for iter in 1:maxiters + P2 = apply(P, P; ak...) + ITensorMPS.truncate!(P2; cutoff=Float64(cutoff)) + err = let diff = +(P2, -1.0 * P; cutoff=1e-12) + n = norm(diff); d = norm(P); d > 0 ? n / d : n + end + err < tol && break + P_inte = +(3.0 * P, -2.0 * P2; cutoff=Float64(cutoff)) + P = apply(P, P_inte; ak...) + ITensorMPS.truncate!(P; cutoff=Float64(cutoff)) + _gpu_gc!() + end + return P +end + +""" + scf_magnetic_hubbard_gpu(H0, U; kwargs...) -> NamedTuple + +GPU-accelerated two-channel collinear magnetic mean-field loop for the on-site +Hubbard model. The SCF iteration keeps the density profiles, Hartree MPOs, +Hamiltonian MPOs, density matrices, RMS checks, and mixing on GPU; CPU objects +are built only for initialization and for the compatibility fields returned at +the end. + +```text +H_up = H0_up + U·diag(n_dn − background) +H_dn = H0_dn + U·diag(n_up − background) +``` + +Only `density_method=:mcweeny` is supported here (grand-canonical at `fermi`); +for particle-number-fixed SP2 use the CPU `scf_magnetic_hubbard`. A concrete +purification `scale` is required so the GPU initial guess can be formed without +estimating spectral bounds on CPU during the loop. + +ComplexF32 eigen-decompositions can NaN at very tight cutoffs; a warning is +emitted if `cutoff < 1e-5`, but the requested `cutoff` is used as-is. + +Post-convergence observables are intentionally separate. Use +[`get_scf_magnetization_gpu`](@ref) or [`get_scf_bands_gpu`](@ref) on the +returned result when you want those GPU-accelerated diagnostics. +""" +function scf_magnetic_hubbard_gpu(H0::TBHamiltonian, U::Union{Number, MPO}; + initial_up::Union{Nothing,MPS}=nothing, + initial_dn::Union{Nothing,MPS}=nothing, + background::Real = 0.5, + Nel_up::Int = H0.N ÷ 2, + Nel_dn::Int = H0.N ÷ 2, + fermi::Real = 0.0, + scale::Union{Nothing,Real} = H0.scale == 0.0 ? nothing : H0.scale, + purification_scale_padding::Real = 1.05, + max_scf_iter::Int = 30, + scf_tol::Real = 1e-6, + mix::Real = 0.4, + maxdim::Int = 100, + cutoff::Real = 1e-8, + purif_maxiter::Int = 40, + purif_tol::Real = 1e-6, + verbose::Bool = true) + _check_gpu("scf_magnetic_hubbard_gpu") + cutoff < 1e-5 && @warn "scf_magnetic_hubbard_gpu: cutoff=$cutoff is below 1e-5; ComplexF32 eigen-decomposition may produce NaN — consider cutoff ≥ 1e-5." + + H0_up, H0_dn = _split_spin_channels(H0) + sites = H0_up.sites + scale === nothing && + error("scf_magnetic_hubbard_gpu: pass a concrete nonzero scale to keep the SCF loop GPU-resident.") + scale_eff = Float64(scale) * Float64(purification_scale_padding) + scale_eff == 0.0 && + error("scf_magnetic_hubbard_gpu: scale must be nonzero.") + if initial_up === nothing || initial_dn === nothing + rho_up, rho_dn = staggered_magnetic_initial(H0; background=background) + initial_up === nothing || (rho_up = initial_up) + initial_dn === nothing || (rho_dn = initial_dn) + else + rho_up, rho_dn = initial_up, initial_dn + end + + rho_up_gpu = _to_gpu_mps(rho_up) + rho_dn_gpu = _to_gpu_mps(rho_dn) + bg_gpu = _to_gpu_mps(constant_mps(collect(sites), background)) + H0_up_gpu = _to_gpu_mpo(H0_up.mpo) + H0_dn_gpu = _to_gpu_mpo(H0_dn.mpo) + Id_gpu = _to_gpu_mpo(MPO(collect(sites), "Id")) + U_gpu = U isa MPO ? _to_gpu_mpo(U) : nothing + + history = NamedTuple[] + density_up_mpo_gpu = nothing + density_dn_mpo_gpu = nothing + Hup_mpo_gpu = H0_up_gpu + Hdn_mpo_gpu = H0_dn_gpu + err = Inf + + function _result(converged::Bool, iters::Int) + density_up_mpo = density_up_mpo_gpu === nothing ? nothing : _to_cpu_mpo(density_up_mpo_gpu) + density_dn_mpo = density_dn_mpo_gpu === nothing ? nothing : _to_cpu_mpo(density_dn_mpo_gpu) + Hup = _copy_with_mpo(H0_up, _to_cpu_mpo(Hup_mpo_gpu); scale=scale_eff, center=0.0) + Hdn = _copy_with_mpo(H0_dn, _to_cpu_mpo(Hdn_mpo_gpu); scale=scale_eff, center=0.0) + return ( + converged=converged, + iterations=iters, + rms_error=err, + rho_up=_to_cpu_mps(rho_up_gpu), + rho_dn=_to_cpu_mps(rho_dn_gpu), + density_up_mpo=density_up_mpo, + density_dn_mpo=density_dn_mpo, + H_up=Hup, + H_dn=Hdn, + rho_up_gpu=rho_up_gpu, + rho_dn_gpu=rho_dn_gpu, + density_up_mpo_gpu=density_up_mpo_gpu, + density_dn_mpo_gpu=density_dn_mpo_gpu, + H_up_mpo_gpu=Hup_mpo_gpu, + H_dn_mpo_gpu=Hdn_mpo_gpu, + history=history, + ) + end + + for iter in 1:max_scf_iter + V_up_gpu = U isa MPO ? + _hartree_mpo_from_density_gpu(rho_dn_gpu, U_gpu, sites, bg_gpu; + maxdim=maxdim, cutoff=cutoff) : + _local_hartree_from_density_gpu(rho_dn_gpu, sites, U, bg_gpu; + maxdim=maxdim, cutoff=cutoff) + V_dn_gpu = U isa MPO ? + _hartree_mpo_from_density_gpu(rho_up_gpu, U_gpu, sites, bg_gpu; + maxdim=maxdim, cutoff=cutoff) : + _local_hartree_from_density_gpu(rho_up_gpu, sites, U, bg_gpu; + maxdim=maxdim, cutoff=cutoff) + + Hup_mpo_gpu = +(H0_up_gpu, V_up_gpu; maxdim=maxdim, cutoff=Float64(cutoff)) + Hdn_mpo_gpu = +(H0_dn_gpu, V_dn_gpu; maxdim=maxdim, cutoff=Float64(cutoff)) + + density_up_mpo_gpu = _mcweeny_purify_mpo_gpu(Hup_mpo_gpu, sites; + ϵF=fermi, scale=scale_eff, center=0.0, Id_gpu=Id_gpu, + maxdim=maxdim, cutoff=cutoff, maxiters=purif_maxiter, + tol=Float64(purif_tol)) + density_dn_mpo_gpu = _mcweeny_purify_mpo_gpu(Hdn_mpo_gpu, sites; + ϵF=fermi, scale=scale_eff, center=0.0, Id_gpu=Id_gpu, + maxdim=maxdim, cutoff=cutoff, maxiters=purif_maxiter, + tol=Float64(purif_tol)) + + rho_up_new_gpu = density_profile_from_dm_gpu(density_up_mpo_gpu, sites; + maxdim=maxdim, cutoff=cutoff) + rho_dn_new_gpu = density_profile_from_dm_gpu(density_dn_mpo_gpu, sites; + maxdim=maxdim, cutoff=cutoff) + + err_up = _rms_error_gpu(rho_up_new_gpu, rho_up_gpu) + err_dn = _rms_error_gpu(rho_dn_new_gpu, rho_dn_gpu) + err = sqrt((err_up^2 + err_dn^2) / 2) + particle_err = abs(real(tr(density_up_mpo_gpu)) - float(Nel_up)) + + abs(real(tr(density_dn_mpo_gpu)) - float(Nel_dn)) + + push!(history, (iter=iter, rms_error=err, rms_up=err_up, rms_dn=err_dn, + particle_error=particle_err)) + verbose && println("magnetic SCF (gpu) iter=$iter rms=$err particle_err=$particle_err") + + rho_up_mixed = +(mix * rho_up_new_gpu, (1.0 - mix) * rho_up_gpu; + maxdim=maxdim, cutoff=Float64(cutoff)) + rho_dn_mixed = +(mix * rho_dn_new_gpu, (1.0 - mix) * rho_dn_gpu; + maxdim=maxdim, cutoff=Float64(cutoff)) + + rho_up_gpu, rho_dn_gpu = rho_up_mixed, rho_dn_mixed + _gpu_gc!() + err < scf_tol && return _result(true, iter) + end + + return _result(false, max_scf_iter) +end + +# Thin 2D-grid wrapper around the shared geometry-aware planner (Utils.jl). +# Used by get_scf_magnetization_gpu; returns (centers, groups) of unit-cell +# indices laid out on a num_x × num_y grid (or x_groups override). +function _tb_spatial_groups_gpu(sites; + num_x::Int = 0, + num_y::Union{Nothing,Int} = nothing, + num_avg::Int = 1, + x_start::Int = 1, + x_end::Int = prod(dim(s) for s in sites), + x_groups = nothing, + box_half::Int = 0, + Lx::Union{Nothing,Int} = nothing) + L = length(sites) + plan = spatial_sampling_plan(L; + Lx = something(Lx, div(L, 2)), + grid = x_groups === nothing, + num_x = num_x, num_y = num_y, num_avg = num_avg, + x_start = x_start, x_end = x_end, + x_groups = x_groups, box_half = box_half) + return plan.centers, plan.groups +end + +""" + get_scf_magnetization_gpu(res; kwargs...) -> (values, centers, groups, n_up, n_dn) + +Sample the converged magnetic SCF density matrices on GPU and extract only the +final scalar values. If `res` carries GPU density MPOs from +`scf_magnetic_hubbard_gpu`, they are reused directly; otherwise the CPU density +MPOs are uploaded once. Each sampled point is evaluated in the same big-endian +real-space convention as `binary_to_MPS`. +""" +function get_scf_magnetization_gpu(res; + num_x::Int = 0, + num_y::Union{Nothing,Int} = nothing, + num_avg::Int = 1, + x_start::Int = 1, + x_end::Int = prod(dim(s) for s in res.H_up.sites), + x_groups = nothing, + box_half::Int = 0, + Lx::Union{Nothing,Int} = nothing) + up_mpo = hasproperty(res, :density_up_mpo_gpu) && res.density_up_mpo_gpu !== nothing ? + res.density_up_mpo_gpu : res.density_up_mpo + dn_mpo = hasproperty(res, :density_dn_mpo_gpu) && res.density_dn_mpo_gpu !== nothing ? + res.density_dn_mpo_gpu : res.density_dn_mpo + + up_mpo === nothing && + error("get_scf_magnetization_gpu: res.density_up_mpo is missing.") + dn_mpo === nothing && + error("get_scf_magnetization_gpu: res.density_dn_mpo is missing.") + + sites = res.H_up.sites + centers, groups = _tb_spatial_groups_gpu(sites; + num_x=num_x, num_y=num_y, num_avg=num_avg, x_start=x_start, x_end=x_end, + x_groups=x_groups, box_half=box_half, Lx=Lx) + + up_diag_gpu = density_profile_from_dm_gpu(up_mpo, sites) + dn_diag_gpu = density_profile_from_dm_gpu(dn_mpo, sites) + + n_up = Float64[ + sum(_eval_mps_bigendian_gpu(up_diag_gpu, x - 1) for x in grp) / length(grp) + for grp in groups + ] + n_dn = Float64[ + sum(_eval_mps_bigendian_gpu(dn_diag_gpu, x - 1) for x in grp) / length(grp) + for grp in groups + ] + values = (n_up .- n_dn) ./ 2 + _gpu_gc!() + return (values=values, centers=centers, groups=groups, n_up=n_up, n_dn=n_dn) +end + +""" + get_scf_bands_gpu(res, Ncheb, omega; kwargs...) -> (Ak, omega, ticks, labels) + +Compute spin-summed mean-field bands from a converged magnetic SCF result. This +is deliberately separate from `scf_magnetic_hubbard_gpu`: it initializes from +the CPU `res.H_up`/`res.H_dn`, then each `get_bands_gpu` call uploads once and +keeps the Chebyshev/QFT accumulation on GPU, extracting only scalars. +""" +function get_scf_bands_gpu(res, Ncheb::Int, omega; kwargs...) + rb_up = get_bands_gpu(res.H_up, Ncheb, omega; kwargs...) + rb_dn = get_bands_gpu(res.H_dn, Ncheb, omega; kwargs...) + Ak_up = rb_up isa NamedTuple ? rb_up.Ak : rb_up + Ak_dn = rb_dn isa NamedTuple ? rb_dn.Ak : rb_dn + return (Ak = Ak_up .+ Ak_dn, + omega = collect(omega), + ticks = rb_up isa NamedTuple ? rb_up.ticks : nothing, + labels = rb_up isa NamedTuple ? rb_up.labels : nothing) +end diff --git a/src/TensorBinding.jl b/src/TensorBinding.jl index 6ff6564..ad45d29 100644 --- a/src/TensorBinding.jl +++ b/src/TensorBinding.jl @@ -55,5 +55,6 @@ include("physics/QPI_tk.jl") include("physics/QFT_tk.jl") include("physics/Supercond_tk.jl") #include("RSI_tk.jl") +include("GPU_tk.jl") end diff --git a/src/core/Utils.jl b/src/core/Utils.jl index 36ed42d..3be3f85 100644 --- a/src/core/Utils.jl +++ b/src/core/Utils.jl @@ -321,6 +321,296 @@ function eval_mps(A::MPS, n::Int) return real(inner(psi, A)) end +# Block-integrated MPS element (reduce=:block): the sum of `A` over one coarse +# block, obtained by tracing out the within-block position bits (contracted with +# [1,1]) and pinning the kept top a/b block bits to the coarse pixel (ixp, iyp). +# Big-endian site order [iy_MSB..iy_LSB, ix_MSB..ix_LSB]: sites 1..Ly carry iy, +# Ly+1..L carry ix. See [`spatial_sampling_plan`](@ref) `reduce=:block`. +function _eval_block_mps(A::MPS, ixp::Int, iyp::Int, + a::Int, b::Int, Lx::Int, Ly::Int) + s = siteinds(A) + ElT = eltype(A[1]) + L = Lx + Ly + acc = ITensor(one(ElT)) + for i in 1:L + v_arr = zeros(ElT, dim(s[i])) + if i <= b # keep: iy block bit (b - i) + v_arr[((iyp >> (b - i)) & 1) + 1] = one(real(ElT)) + elseif i <= Ly # sum: iy within-block bit + v_arr .= one(real(ElT)) + elseif i <= Ly + a # keep: ix block bit (a - (i - Ly)) + v_arr[((ixp >> (a - (i - Ly))) & 1) + 1] = one(real(ElT)) + else # sum: ix within-block bit + v_arr .= one(real(ElT)) + end + acc *= A[i] * ITensor(v_arr, s[i]) + end + return real(scalar(acc)) +end + +""" + spatial_sampling_plan(L; Lx, grid, reduce, n_sub, num_x, num_y, num_avg, + x_start, x_end, xwin, ywin, x_groups, box_half, sublattice) + -> (; centers, groups, resolve_sublattice, n_sub, stride_x, stride_y, + grid, reduce, a, b) + +Geometry-aware real-space sampling plan shared by every spatial sampler +([`eval_mps_spatial`](@ref), `get_ldos_spatial`, `get_ldos_spatial_gpu`, +`get_scf_magnetization_gpu`). It decides **where** to sample, **how** each output +pixel reduces the cells under it (`reduce`), and — for multi-atom unit cells — +whether to **resolve** or **average** the sublattice. + +# The three sampling procedures (`reduce`) + +A spatial map of a `2^Lx × 2^Ly`-unit-cell system at a coarse output resolution +can reduce the cells beneath each pixel in three qualitatively different ways. +The right choice depends on whether the quantity is *smooth on the large scale* +(e.g. a Chern marker, an SCF density envelope) or a *thin feature on a flat +background* (e.g. in-gap edge/domain-wall LDOS, width ξ ≪ system size). + +1. **`:point` (default) — point / box sampling.** + Lay out `num_x[×num_y]` sample positions and read the profile *at* each one. + With `box_half > 0` each pixel is the **mean** over a `(2·box_half+1)²` + neighbourhood (smoothing). Cost ∝ (number of pixels) × (box cells). + + *Aliasing caveat.* The pixels probe only the cells they land on (± `box_half`). + On a grid coarser than a feature's width this **misses** thin features that + fall between pixels: a domain-wall LDOS channel of width ξ sampled at stride + `s ≫ ξ` is caught only on the rare pixel within `box_half` of it. Making the + box *tile* the plane (`box_half ≈ s/2`) closes the gaps but then evaluates + essentially every cell — i.e. full-resolution cost. Use `:point` for smooth + quantities or for a fully-resolved zoom (`grid=true` + a small window). + +2. **`:block` — block integration (gap-free coarse-graining).** + Partition the system into `num_x × num_y` equal blocks (`num_x = 2^a`, + `num_y = 2^b`, powers of two) and report, per pixel, the **sum** over its + whole block. This is computed by *tracing out the low-order position bits* + (contracting the within-block bits of the profile MPS with `[1,1]` and keeping + the `a + b` high-order block bits) — a partial contraction, **not** a per-cell + sweep, so the cost is independent of block size and scales to `Lx, Ly ≈ 14+`. + + Because every cell belongs to exactly one block, a thin feature **cannot fall + between pixels** — whichever blocks it threads light up, on an otherwise dark + (gapped) background. This is the tool for imaging edge / domain-wall networks + on a heavily downsampled map. Block centres are reported in `centers`; the + per-axis block widths are `stride_x = 2^(Lx-a)`, `stride_y = 2^(Ly-b)`. + +The fields `reduce`, `a`, `b` echo the chosen mode back to the caller; for +`:point` they are `(:point, 0, 0)`. + +# Sublattice resolve vs average + +For a multi-atom unit cell (`n_sub > 1`) the plan also decides whether to +**resolve** the sublattice (one output column per atom) or **average** it (one +value per unit cell, atoms traced out), via `sublattice`: + +- `:auto` (default) — **resolve** only at the atomic scale: consecutive samples + are adjacent unit cells (`:point` with `stride == 1` and `box_half == 0`). + Otherwise (coarse grid, `box_half > 0`, or any `:block` map) **average**, since + the intra-cell sublattice is below the sampling resolution. +- `:resolve` / `:average` force the choice. `n_sub == 1` is always `false`. + +# Layout (`:point` mode) + +`groups`/`centers` are 1-indexed unit-cell indices with `n = ix + iy·2^Lx`. + +- `grid=false` (default) — centers on a **1D linear** sweep of the row-major index + (`x_start`/`x_end`, `num_x` points, `num_avg` sub-probes per block). Stride + `dx = window ÷ num_x`. `Lx` is used only for the optional `box_half` neighbourhood. +- `grid=true` (needs `Lx`) — centers on a **2D xy grid** of `num_x × num_y` + points over the unit-cell window `xwin=(ix0,ix1)`, `ywin=(iy0,iy1)` (0-indexed; + default full system). Per-axis strides `Nx_win÷num_x`, `Ny_win÷num_y`. + +`x_groups` overrides the `:point` layout entirely; the stride is then unknown, so +`:auto` resolves (treats it as atomic) unless `box_half > 0`. +""" +function spatial_sampling_plan(L::Int; + Lx::Union{Nothing,Int} = nothing, + grid::Bool = false, + reduce::Symbol = :point, + n_sub::Int = 1, + num_x::Int = 0, + num_y::Union{Nothing,Int} = nothing, + num_avg::Int = 1, + x_start::Int = 1, + x_end::Int = 2^L, + xwin = nothing, + ywin = nothing, + x_groups = nothing, + box_half::Int = 0, + sublattice::Symbol = :auto) + sublattice in (:auto, :resolve, :average) || + error("spatial_sampling_plan: sublattice must be :auto, :resolve, or :average.") + reduce in (:point, :block) || + error("spatial_sampling_plan: reduce must be :point or :block.") + grid && Lx === nothing && + error("spatial_sampling_plan: grid=true requires Lx (the x-qubit count).") + + # ── :block — coarse-grain by tracing out the within-block position bits ──── + if reduce === :block + Lx === nothing && + error("spatial_sampling_plan: reduce=:block requires Lx.") + Ly = L - Lx + num_x > 0 || + error("spatial_sampling_plan: reduce=:block requires num_x > 0 (a power of two).") + nyv = num_y === nothing ? num_x : num_y + nyv > 0 || + error("spatial_sampling_plan: reduce=:block requires num_y > 0 (a power of two).") + a = round(Int, log2(num_x)) + b = round(Int, log2(nyv)) + 2^a == num_x || + error("spatial_sampling_plan: reduce=:block needs num_x a power of two (got $num_x).") + 2^b == nyv || + error("spatial_sampling_plan: reduce=:block needs num_y a power of two (got $nyv).") + (0 <= a <= Lx) || + error("spatial_sampling_plan: reduce=:block needs 1 <= num_x <= 2^Lx=$(2^Lx).") + (0 <= b <= Ly) || + error("spatial_sampling_plan: reduce=:block needs 1 <= num_y <= 2^Ly=$(2^Ly).") + Nx = 2^Lx + Wx = 2^(Lx - a) # block width in x (unit cells) + Wy = 2^(Ly - b) # block width in y + # Block centre cell, row-major over coarse pixels (ixp fastest): + # col = ixp + iyp*num_x + 1 + centers = Int[(ixp * Wx + Wx ÷ 2) + (iyp * Wy + Wy ÷ 2) * Nx + 1 + for iyp in 0:(2^b - 1) for ixp in 0:(2^a - 1)] + groups = [[c] for c in centers] # nominal; block eval does not use these + resolve = n_sub > 1 && sublattice === :resolve # block is large-scale → average + return (; centers, groups, resolve_sublattice=resolve, n_sub=max(n_sub, 1), + stride_x=Wx, stride_y=Wy, grid=true, reduce=:block, a, b) + end + + stride_x = 1 + stride_y = 1 + stride_known = true + + local centers::Vector{Int} + local groups::Vector{Vector{Int}} + + if x_groups !== nothing + groups = x_groups isa AbstractVector{<:AbstractVector} ? + [collect(Int, g) for g in x_groups] : [[Int(x)] for x in x_groups] + centers = Int[first(g) for g in groups] + stride_known = false # caller-supplied positions: stride is not defined + elseif grid + Nx = 2^Lx + Ny = 2^(L - Lx) + ix0, ix1 = xwin === nothing ? (0, Nx - 1) : (Int(xwin[1]), Int(xwin[2])) + iy0, iy1 = ywin === nothing ? (0, Ny - 1) : (Int(ywin[1]), Int(ywin[2])) + Nx_win = ix1 - ix0 + 1 + Ny_win = iy1 - iy0 + 1 + nx = num_x <= 0 ? Nx_win : min(num_x, Nx_win) + ny = num_y === nothing ? (num_x <= 0 ? Ny_win : min(nx, Ny_win)) : + (num_y <= 0 ? Ny_win : min(num_y, Ny_win)) + stride_x = Nx_win ÷ nx + stride_y = Ny_win ÷ ny + xcenters = nx <= 1 ? [ix0] : round.(Int, range(ix0, ix1; length=nx)) + ycenters = ny <= 1 ? [iy0] : round.(Int, range(iy0, iy1; length=ny)) + centers = Int[ix + iy * Nx + 1 for iy in ycenters for ix in xcenters] + groups = [[c] for c in centers] + else + window = x_end - x_start + 1 + nx = num_x <= 0 ? window : num_x + dx = max(window ÷ nx, 1) + stride_x = dx + dx_sub = max(1, dx ÷ num_avg) + centers = Int[x_start + (i - 1) * dx for i in 1:nx] + groups = [[ x_start + (i - 1) * dx + k * dx_sub + for k in 0:num_avg-1 + if x_start + (i - 1) * dx + k * dx_sub <= x_end ] + for i in 1:nx] + end + + # ── 2D box averaging (periodic wrap) ─────────────────────────────────────── + if box_half > 0 && Lx !== nothing + Nx = 2^Lx + Ny = 2^(L - Lx) + groups = [ + let uc0 = first(grp) - 1 + ix0 = uc0 % Nx + iy0 = uc0 ÷ Nx + unique([mod(ix0 + Δx, Nx) + mod(iy0 + Δy, Ny) * Nx + 1 + for Δy in -box_half:box_half for Δx in -box_half:box_half]) + end + for grp in groups + ] + end + + # ── Sublattice resolve / average decision ────────────────────────────────── + resolve = if n_sub <= 1 + false + elseif sublattice === :resolve + true + elseif sublattice === :average + false + else # :auto + box_half == 0 && + (stride_known ? (stride_x <= 1 && (grid ? stride_y <= 1 : true)) : true) + end + + return (; centers, groups, resolve_sublattice=resolve, n_sub=max(n_sub, 1), + stride_x, stride_y, grid, reduce=:point, a=0, b=0) +end + +""" + eval_mps_spatial(A::MPS; num_x, num_avg, x_start, x_end, x_groups, + box_half, Lx) -> (values, centers, groups) + +Higher-level spatial sampler for a profile MPS such as an SCF occupation/density +profile (`res.rho_up`). It mirrors `get_ldos_spatial`'s `num_x` / `num_avg` / +`x_groups` / `box_half` sampling-and-averaging API, but evaluates the MPS +directly with [`eval_mps`](@ref) instead of running a KPM recursion — so it is +cheap enough to sweep a very large system by sampling a grid of positions and +averaging, rather than evaluating all `2^L` sites. + +For each sampled group of (1-indexed) site coordinates the returned value is the +mean of `eval_mps(A, x-1)` over that group. With `box_half > 0` each sampled +position is expanded into a `(2·box_half+1)²` neighborhood on the 2D grid +(periodic wrap), exactly like `get_ldos_spatial`; this needs the 2D layout, taken +from `Lx` (defaults to `L÷2`, with `Ly = L - Lx`). + +# Keyword arguments +- `num_x` : number of sampled grid positions (default: all `2^L` sites). +- `num_avg` : sub-positions averaged per grid point along the 1D index (stride). +- `x_start`, `x_end` : 1-indexed sampling window (default `1 … 2^L`). +- `x_groups` : explicit groups — a vector of site indices (one per group) or a + vector of vectors (each averaged). Overrides `num_x`/`num_avg`/`x_start`/`x_end`. +- `box_half` : 2D neighborhood half-width for averaging (0 = no box averaging). +- `Lx` : number of x qubits for the 2D layout (default `L÷2`). + +# Returns +- `values` : `Vector{Float64}`, the averaged MPS value per group. +- `centers` : `Vector{Int}`, the 1-indexed center site of each sampled group. +- `groups` : `Vector{Vector{Int}}`, the site indices averaged over per group. + +For a 2D map, the center `(ix, iy)` of group `g` is +`ix = (centers[g]-1) % 2^Lx`, `iy = (centers[g]-1) ÷ 2^Lx`. +""" +function eval_mps_spatial(A::MPS; + num_x::Int = prod(dim(s) for s in siteinds(A)), + num_avg::Int = 1, + x_start::Int = 1, + x_end::Int = prod(dim(s) for s in siteinds(A)), + x_groups = nothing, + box_half::Int = 0, + Lx::Union{Nothing,Int} = nothing) + sites = siteinds(A) + L = length(sites) + + # ── Build groups + grid centers via the shared geometry-aware planner ────── + plan = spatial_sampling_plan(L; + Lx = (box_half > 0 && Lx === nothing) ? L ÷ 2 : Lx, + num_x = num_x, num_avg = num_avg, + x_start = x_start, x_end = x_end, + x_groups = x_groups, box_half = box_half) + centers = plan.centers + groups = plan.groups + + # ── Evaluate + average ───────────────────────────────────────────────────── + values = Float64[ sum(eval_mps(A, x - 1) for x in grp) / length(grp) + for grp in groups ] + return (values=values, centers=centers, groups=groups) +end + """ rms_error(a, b) -> Float64 diff --git a/src/solvers/KPM_tk.jl b/src/solvers/KPM_tk.jl index 213b150..9b016b0 100644 --- a/src/solvers/KPM_tk.jl +++ b/src/solvers/KPM_tk.jl @@ -154,15 +154,14 @@ Pathway 1 — MPO × MPO cache [legacy / rarely used] O(Ncheb × χ_T²). Prefer Pathways 3 or 4 unless the cache is reused for multiple downstream calls. Kept mainly for legacy compatibility. -Pathway 2 — MPS cache [exciton LDOS, fixed reference state] +Pathway 2 — MPS cache [legacy / fixed reference state] KPM_Tn(H, Ncheb; mode=:mps, psi0=ψ₀) # cache {T_n(H̃)|ψ₀⟩} MPS on H - → get_exciton_ldos(H, X, ω; …) # μₙ = ⟨X|T_n(H̃)|X⟩ reusing cache → get_ldos(H, ω; mode=:mps, psi0=ψ₀) # μₙ = ⟨ψ₀|T_n(H̃)|ψ₀⟩ → scalar Propagates a single reference MPS and stores the full trajectory {|φ_n⟩ = T_n(H̃)|ψ₀⟩} for repeated re-use across many energy queries on the - same state. Natural for exciton LDOS where many sites |X⟩ are probed - sequentially after building the cache once. + same state. Kept for advanced workflows; the public LDOS helpers below avoid + storing this cache. Pathway 3 — Online MPO × MPO [k-space and spatial spectral functions] get_bands(H, Ncheb, D, ωlist; …) # k-resolved A(k,ω), QFT-conjugated @@ -175,6 +174,8 @@ Pathway 3 — Online MPO × MPO [k-space and spatial spectral functions] Pathway 4 — Online MPO × MPS [single-particle default, most memory-efficient] get_ldos_online(H, Ncheb, X, ωlist; …) # LDOS at one site, all ω get_ldos_spatial(H, Ncheb, ωlist; mode=:mps; …) # per-position MPS recursion + get_exciton_ldos_spatial(H, Ncheb, ωlist; …) # bound-pair exciton LDOS + get_exciton_ldos(H, X, ωlist; …) # one-position wrapper get_dos_stochastic(H, Ncheb, ωlist; …) # stochastic trace DOS Propagates MPS states rather than full MPOs: only 3 MPS alive per sample/site. @@ -654,35 +655,62 @@ end Spatially-resolved LDOS, real-space analogue of `get_bands`. -**Return shape** +**Sampling procedures (`reduce`)** — full detail in [`spatial_sampling_plan`](@ref). -- **No sublattice DOF** (`H.sublattice_s === nothing`): `(Nω × ng)` where `ng = num_x`. -- **With sublattice DOF** (`H.sublattice_s` set): `(Nω × ng×n_sub)` where `n_sub = - dim(H.sublattice_s)`. Columns are interleaved in atom order matching the - corresponding `*_positions` function: `[A₀, B₀, A₁, B₁, …]` for 2-sublattice - lattices, `[A₀, B₀, C₀, …]` for 3-sublattice ones. Pair directly with - `plot_ldos_2d` which consumes this column layout without further transformation. +- `:point` (default) — read the LDOS *at* `num_x[×num_y]` sample cells; with + `box_half > 0` each pixel is the mean over a `(2·box_half+1)²` box. Cheap, but + a grid coarser than a feature's width **aliases** it (thin in-gap edge / + domain-wall channels can fall between pixels and be missed). +- `:block` — partition the system into `num_x × num_y` blocks (powers of two) and + report the **integral** over each block, computed by tracing out the + within-block position bits (a partial contraction, cost independent of block + size). Gap-free: every cell belongs to one block, so a thin feature on a gapped + background **cannot** be missed. Use it for large-scale maps of edge networks. + Output columns are row-major over coarse pixels (`col = ixp + iyp·num_x + 1`); + block centres come from the plan. `:mpo` mode only. -**Sublattice auto-detection** +**Return shape (sublattice geometry-awareness)** -`H.sublattice_s` is detected automatically. When set: -- `proj_sl=nothing` (default): a single Chebyshev pass fills *all* sublattice columns. -- `proj_sl=k`: only sublattice `k` columns are filled; all others are zero. - `sublat_proj=true` is accepted for API compatibility but is no longer required. +For a multi-atom unit cell (`H.sublattice_s` set) the layout depends on the +*sampling scale*, decided by [`spatial_sampling_plan`](@ref) from the local +stride (see `sublattice` below): + +- **resolved** (atomic scale — every unit cell probed, or `proj_sl=k`): + `(Nω × ng×n_sub)`, columns interleaved in atom order matching the + `*_positions` functions — `[A₀, B₀, A₁, B₁, …]` (2-sublattice), + `[A₀, B₀, C₀, …]` (3-sublattice). `proj_sl=k` fills only sublattice `k`. +- **averaged** (large scale — the grid skips unit cells): `(Nω × ng)`, the + sublattice is traced out into one value per unit cell (mean over the `n_sub` + atoms). + +With no sublattice DOF the shape is always `(Nω × ng)`, `ng = num_x`. + +**`sublattice` (resolve vs average)** + +- `:auto` (default) — resolve when sampling at full unit-cell resolution + (`stride == 1`, e.g. zooming a small window and probing every cell); average + whenever the grid is coarser or `box_half > 0`. +- `:resolve` — always emit per-atom columns. `:average` — always trace the + sublattice to one value per cell. `proj_sl=k` always resolves that one atom. **Sampling parameters** -- `num_x` : coarse position count (default `H.N` for full resolution). -- `num_avg` : sub-samples per coarse block for local averaging (default 1). -- `x_start`, `x_end` : 1-indexed position range (defaults: `1` and `H.N`). -- `x_groups` : explicit `Vector{Vector{Int}}` override. +- `num_x`/`num_y` : sample counts (per axis for `grid=true`; `num_x` is the total + for the default 1D linear sweep). Default `H.N` = full resolution. +- `num_avg` : sub-samples per coarse block for local averaging (1D, default 1). +- `x_start`, `x_end` : 1-indexed linear position range (1D layout). +- `grid` : `true` lays centers on a 2D `num_x × num_y` unit-cell grid. +- `xwin`, `ywin` : 0-indexed unit-cell `(lo, hi)` windows for `grid=true` (e.g. + zoom into a patch of a large system). +- `x_groups` : explicit `Vector{Vector{Int}}` override (treated as atomic). +- `box_half` : 2D neighbourhood half-width (averages, forces sublattice averaging). +- `reduce` : `:point` (sample/box) or `:block` (block-integrate; see above). **Modes** - `:mpo` (default) — single Chebyshev pass; evaluates all positions simultaneously. Cost `∝ Ncheb × (MPO×MPO)`, independent of `num_x` or `n_sub`. - `:mps` — independent MPS recursion per (position, sector) combination. - Use for systems where MPO×MPO is too expensive. **Other auxiliary DOF projections** (same interface as `get_bands`): `nambu_proj`/`proj_nambu`, `spin_proj`/`proj_s`, `layer_proj`/`proj_layer`. @@ -693,23 +721,35 @@ Examples # Standard 1D chain — shape (Nω × 8) ldos = get_ldos_spatial(H, 200, ωlist; num_x=8) -# Kagome: full resolution, all sublattices — shape (Nω × 3*H.N) -ldos_all = get_ldos_spatial(H_kg, 200, ωlist; num_x=H_kg.N) +# Honeycomb, large-scale map — sublattice averaged, shape (Nω × 64) +ldos_uc = get_ldos_spatial(H_hc, 200, ωlist; num_x=64) + +# Honeycomb, atomic zoom into a 50×50 patch — sublattice resolved (Nω × 50*50*2) +ldos_zoom = get_ldos_spatial(H_hc, 200, ωlist; grid=true, + xwin=(1000, 1049), ywin=(1000, 1049)) + +# Large 2^14×2^14 system: 128×128 block-integrated map — catches thin in-gap +# edge channels that point sampling would alias away. Shape (Nω × 128*128). +ldos_blk = get_ldos_spatial(H_big, 200, ωlist; reduce=:block, num_x=128, num_y=128) # Kagome: sublattice A only — only A columns filled, B/C columns zero ldos_A = get_ldos_spatial(H_kg, 200, ωlist; proj_sl=1, num_x=H_kg.N) - -# BdG chain: particle sector only -ldos_p = get_ldos_spatial(H_bdg, 200, ωlist; nambu_proj=true, proj_nambu=1) ``` """ function get_ldos_spatial(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; num_x::Int = H.N, + num_y = nothing, num_avg::Int = 1, mode::Symbol = :mpo, x_start::Int = 1, x_end::Int = H.N, x_groups = nothing, + grid::Bool = false, + xwin = nothing, + ywin = nothing, + box_half::Int = 0, + reduce::Symbol = :point, + sublattice::Symbol = :auto, kernel::Symbol = :jackson, lambda::Real = 4.0, maxdim::Int = 100, @@ -724,19 +764,33 @@ function get_ldos_spatial(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; sublat_proj::Bool = false, # kept for backward compat; auto-on when H.sublattice_s is set proj_sl = nothing) - # ── Build x_groups ──────────────────────────────────────────────────────── - groups = if x_groups !== nothing - x_groups isa AbstractVector{<:AbstractVector} ? - collect.(x_groups) : [[x] for x in x_groups] - else - window = x_end - x_start + 1 - dx = window ÷ num_x - dx_sub = max(1, dx ÷ num_avg) - [[ x_start + (i-1)*dx + k*dx_sub - for k in 0:num_avg-1 - if x_start + (i-1)*dx + k*dx_sub <= x_end ] - for i in 1:num_x] + # ── Geometry-aware sampling plan (unit-cell groups + sublattice decision) ── + if box_half > 0 || grid || xwin !== nothing || ywin !== nothing || reduce === :block + isnothing(H.geometry) && + error("get_ldos_spatial: box_half/grid/window/block sampling requires H.geometry to be set.") + length(H.geometry(1)) == 2 || + error("get_ldos_spatial: box_half/grid/window/block sampling is only supported for 2D systems.") end + reduce === :block && mode === :mps && + error("get_ldos_spatial: reduce=:block is only supported in mode=:mpo.") + Lx_uc = something(H.Lx, H.L ÷ 2) + Ly_uc = H.L - Lx_uc + n_sub_H = isnothing(H.sublattice_s) ? 1 : dim(H.sublattice_s) + plan = spatial_sampling_plan(H.L; + Lx = Lx_uc, + grid = grid, + reduce = reduce, + n_sub = n_sub_H, + num_x = num_x, num_y = num_y, num_avg = num_avg, + x_start = x_start, x_end = x_end, + xwin = xwin, ywin = ywin, + x_groups = x_groups, box_half = box_half, + sublattice = sublattice) + groups = plan.groups + is_block = plan.reduce === :block + block_a = plan.a + block_b = plan.b + nbx = 2^block_a _ensure_scale!(H) nambu_proj, spin_proj, layer_proj, sublat_proj = @@ -764,14 +818,19 @@ function get_ldos_spatial(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; end # ── Sublattice layout ───────────────────────────────────────────────────── - # When H.sublattice_s is set, the result always covers every atom: - # shape = (Nω, ng × n_sub), col = (ig-1)*n_sub + s - # This matches the atom ordering of honeycomb/kagome/lieb positions functions. - # proj_sl=k → fill only sublattice k (others stay 0) - # proj_sl=nothing → fill all sublattices (auto when sublat_proj=false too) - has_sublat = !isnothing(sublat_s_det) - n_sub = has_sublat ? dim(sublat_s_det::Index) : 1 - sl_fill = has_sublat ? + # When H.sublattice_s is set the geometry-aware plan decides the layout: + # • resolve (atomic scale, or proj_sl=k): one column per atom, + # shape (Nω, ng × n_sub), col = (ig-1)*n_sub + s (matches the + # honeycomb/kagome/lieb positions-function atom ordering; proj_sl=k + # fills only sublattice k, others stay 0). + # • average (large scale): the sublattice is traced out — one value per + # unit cell, shape (Nω, ng), col = ig (mean over the n_sub atoms). + has_sublat = !isnothing(sublat_s_det) + n_sub = has_sublat ? dim(sublat_s_det::Index) : 1 + # proj_sl=k pins a single sublattice → always resolved (that one column). + resolve_sl = has_sublat && (plan.resolve_sublattice || !isnothing(proj_sl)) + average_sl = has_sublat && !resolve_sl + sl_fill = has_sublat ? (isnothing(proj_sl) ? (1:n_sub) : (proj_sl:proj_sl)) : (1:1) @@ -784,7 +843,8 @@ function get_ldos_spatial(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; valid = [abs(ω) < 1.0 for ω in ω_vals] ng = length(groups) - n_cols = ng * n_sub + # Averaging collapses the n_sub atoms into one column per group. + n_cols = average_sl ? ng : ng * n_sub result = zeros(Float64, Nω, n_cols) L_tot = length(H.sites) @@ -814,8 +874,13 @@ function get_ldos_spatial(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; end # sector loop end # x - for s in sl_fill - result[:, (ig-1)*n_sub + s] = grp_accum[:, s] ./ length(grp) + if average_sl + # Trace out the sublattice: one column per unit cell (mean atom). + result[:, ig] = vec(sum(grp_accum; dims=2)) ./ (n_sub * length(grp)) + else + for s in sl_fill + result[:, (ig-1)*n_sub + s] = grp_accum[:, s] ./ length(grp) + end end n_done += length(grp) @@ -849,6 +914,21 @@ function get_ldos_spatial(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; local _layer_side = layer_side_det local _sublat_side = sublat_side_det + # Reduce a diagonal profile MPS to per-pixel scalars, returning (u, value) + # pairs where u is the 1-indexed output pixel (column unit): + # reduce=:point → mean of inner products over each group's cells, + # reduce=:block → integral over each coarse block (_eval_block_mps). + function spatial_vals_cpu(diag_n) + if is_block + return [(ixp + iyp * nbx + 1, + _eval_block_mps(diag_n, ixp, iyp, block_a, block_b, Lx_uc, Ly_uc)) + for iyp in 0:(2^block_b - 1) for ixp in 0:(nbx - 1)] + else + return [(ig, sum(real(inner(psi_dict[x], diag_n)) for x in grp) / length(grp)) + for (ig, grp) in enumerate(groups)] + end + end + function accumulate_Tn!(Tk, n) # Non-sublattice projections (nambu → spin → layer) after_nambu = nambu_proj ? @@ -871,28 +951,29 @@ function get_ldos_spatial(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; end if has_sublat - # Project per sublattice sector; each gets its own columns + # Project per sublattice sector. Resolved → each sector gets its + # own column; averaged (large scale) → all sectors fold into the + # single per-pixel column u (mean over the n_sub atoms). for Tl in after_layer, s in sl_fill Tp = project_aux(Tl, sublat_s_det::Index, s; side=_sublat_side) diag_n = ITensorMPS.truncate!(extract_diagonal_to_mps(Tp); cutoff=cutoff) - for (ig, grp) in enumerate(groups) - val = sum(real(inner(psi_dict[x], diag_n)) for x in grp) / length(grp) - col = (ig - 1) * n_sub + s + scale = average_sl ? 1.0 / n_sub : 1.0 + for (u, val) in spatial_vals_cpu(diag_n) + col = average_sl ? u : (u - 1) * n_sub + s for iω in 1:Nω valid[iω] || continue - accum[iω, col] += W[n, iω] * val + accum[iω, col] += W[n, iω] * val * scale end end end else - # No sublattice: one column per group (original behavior) + # No sublattice: one column per pixel (original behavior) for Tp in after_layer diag_n = ITensorMPS.truncate!(extract_diagonal_to_mps(Tp); cutoff=cutoff) - for (ig, grp) in enumerate(groups) - val = sum(real(inner(psi_dict[x], diag_n)) for x in grp) / length(grp) + for (u, val) in spatial_vals_cpu(diag_n) for iω in 1:Nω valid[iω] || continue - accum[iω, ig] += W[n, iω] * val + accum[iω, u] += W[n, iω] * val end end end @@ -931,8 +1012,8 @@ end """ get_dos_stochastic(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; - N_sample, N_bound, seed, normalize, - kernel, lambda, maxdim, cutoff, verbose, + N_sample, N_bound, seed, normalize, dos_weighting, + kernel, lambda, eta, m_order, maxdim, cutoff, verbose, nambu_proj, proj_nambu, spin_proj, proj_s, layer_proj, proj_layer, sublat_proj, proj_sl) -> Vector{Float64} @@ -941,10 +1022,13 @@ Stochastic full DOS via random trace estimation (MPS Chebyshev, 3 MPS per sample **Normalization** -- `normalize=false` (default): returns the **total** spectral weight `Tr[δ(ω−H)]`, - which grows as `D` (Hilbert space dimension). -- `normalize=true`: divides by `D`, giving a **per-state** DOS that is intensive - (independent of `L`) and directly comparable to `get_ldos_spatial` values. +- `dos_weighting=:trace` (default): returns the trace DOS. With + `normalize=false` this is the total spectral weight `Tr[δ(ω-H)]`; with + `normalize=true` it is divided by the traced Hilbert-space dimension. +- `dos_weighting=:sample`: returns the unweighted sample signal. For exciton + stratified runs this is `avg_full + avg_bound` (when `N_bound > 0`), with no + phase-space factor multiplying the continuum. This is intended for + visualising the bound peak; `normalize` is ignored in this mode. **Auxiliary DOF projections** @@ -967,6 +1051,15 @@ dedicates `N_bound` samples to the bound sector `|x,x⟩` and `N_sample` to the full Hilbert space, combining with proper weights: `DOS = N_phys × avg_bound + (D − N_phys) × avg_scatter`. `N_bound = 0` (default) = uniform sampling over all D states. +Set `dos_weighting=:sample` to inspect the sampled spectral signal before these +sector-size weights are applied. + +**Reconstruction kernel** + +`kernel=:hodc` uses the Higher-Order Delta Chebyshev contour reconstruction +(`eta`, `m_order` control it; `eta=0` → `1/(Ncheb+1)`), whose weights already +carry the full KPM normalisation. Other values are convolution kernels +(`:jackson` default, `:lorentz` with `lambda`, `:fejer`, `:dirichlet`). Examples -------- @@ -988,8 +1081,11 @@ function get_dos_stochastic(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; N_bound::Int = 0, seed::Union{Int,Nothing} = 42, normalize::Bool = false, + dos_weighting::Symbol = :trace, kernel::Symbol = :jackson, lambda::Real = 4.0, + eta::Real = 0.0, + m_order::Int = 4, maxdim::Int = 100, cutoff::Real = 1e-8, verbose::Bool = false, @@ -1003,6 +1099,8 @@ function get_dos_stochastic(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; sublat_proj::Bool = false, proj_sl = nothing) _ensure_scale!(H) + dos_weighting in (:trace, :sample) || + error("get_dos_stochastic: dos_weighting must be :trace or :sample.") I_mpo = MPO(H.sites, "Id") Ham_n = (1 / H.scale) * +(H.mpo, (-H.center) * I_mpo; cutoff=cutoff) @@ -1017,13 +1115,13 @@ function get_dos_stochastic(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; ω_vals = (collect(ω_phys_vals) .- H.center) ./ H.scale Nω = length(ω_vals) - W = _kpm_weight_matrix(Ncheb, ω_vals; kernel=kernel, lambda=lambda) + W, denom = _dos_weight_matrix(Ncheb, ω_vals; + kernel=kernel, lambda=lambda, eta=eta, m_order=m_order) valid = [abs(ω) < 1.0 for ω in ω_vals] rng = seed === nothing ? Random.default_rng() : Random.MersenneTwister(seed) accum_full = zeros(Float64, Nω) accum_bound = zeros(Float64, Nω) - norm = π^2 * Ncheb if any_aux_proj # ── Projected DOS: sample position states with fixed aux sectors ───── @@ -1049,9 +1147,13 @@ function get_dos_stochastic(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; result = zeros(Float64, Nω) for iω in 1:Nω valid[iω] || continue - result[iω] = D_eff * accum_full[iω] / (norm * sqrt(1 - ω_vals[iω]^2)) + if dos_weighting == :sample + result[iω] = accum_full[iω] / denom[iω] + else + result[iω] = D_eff * accum_full[iω] / denom[iω] + end end - normalize && (result ./= N_phys) + normalize && dos_weighting == :trace && (result ./= D_eff) return result end @@ -1080,15 +1182,17 @@ function get_dos_stochastic(H::TBHamiltonian, Ncheb::Int, ω_phys_vals; result = zeros(Float64, Nω) for iω in 1:Nω valid[iω] || continue - denom = norm * sqrt(1 - ω_vals[iω]^2) - if N_bound > 0 && is_exc + if dos_weighting == :sample + result[iω] = (accum_full[iω] + + ((N_bound > 0 && is_exc) ? accum_bound[iω] : 0.0)) / denom[iω] + elseif N_bound > 0 && is_exc result[iω] = ((D - N_phys) * accum_full[iω] + - N_phys * accum_bound[iω]) / denom + N_phys * accum_bound[iω]) / denom[iω] else - result[iω] = D * accum_full[iω] / denom + result[iω] = D * accum_full[iω] / denom[iω] end end - normalize && (result ./= N_phys) + normalize && dos_weighting == :trace && (result ./= D) return result end @@ -1136,7 +1240,7 @@ end """ get_ldos_hodc_from_mun(mun_list, N, E; eta=0.02, m_order=6) -> Real -HODC (High-Order Damping Correction) variant of `get_ldos_from_mun`. Uses a +HODC (Higher-Order Delta Chebyshev) variant of `get_ldos_from_mun`. Uses a contour-based kernel that gives sharper spectral features than the Jackson kernel, at the cost of `m_order` extra parameters. @@ -1204,6 +1308,45 @@ function get_hodc_weights(y_target, N, eta, zl, wl) return nu end +""" + _dos_weight_matrix(Ncheb, ω_vals; kernel, lambda, eta, m_order) + -> (W::Matrix, denom::Vector) + +Stochastic-DOS reconstruction weights `W[n, iω]` and per-ω normalisation +`denom[iω]` for a given KPM `kernel`. The DOS is recovered from the (sample- +averaged) Chebyshev moments `μ_n` as `Σ_n W[n,iω] μ_n / denom[iω]`. + +- Convolution kernels (`:jackson`, `:lorentz`, `:fejer`, `:dirichlet`): + `W` follows `_kpm_weight_matrix` and `denom = π²·Ncheb·√(1−ω²)`, matching + `get_ldos_from_mun`. +- `:hodc`: the contour weights `νₙ(ω)` from `get_hodc_weights` already carry the + full normalisation (`denom = 1`), matching `get_ldos_hodc_from_mun`. `eta=0` + falls back to `1/(Ncheb+1)`. + +Entries with `|ω| ≥ 1` are zeroed in `W` (outside the rescaled spectral support). +""" +function _dos_weight_matrix(Ncheb::Int, ω_vals; + kernel::Symbol = :jackson, + lambda::Real = 4.0, + eta::Real = 0.0, + m_order::Int = 4) + Nω = length(ω_vals) + if kernel == :hodc + eta_ = eta == 0.0 ? 1 / (Ncheb + 1) : eta + zl, wl = compute_hodc_params(m_order) + W = zeros(Float64, Ncheb, Nω) + for iω in 1:Nω + abs(ω_vals[iω]) >= 1.0 && continue + W[:, iω] .= get_hodc_weights(ω_vals[iω], Ncheb, eta_, zl, wl) + end + return W, ones(Float64, Nω) + else + W = _kpm_weight_matrix(Ncheb, ω_vals; kernel=kernel, lambda=lambda) + denom = [π^2 * Ncheb * sqrt(max(1 - ω^2, 0.0)) for ω in ω_vals] + return W, denom + end +end + # Returns complex weights π*(ν_HT - i*ν_δ) for the retarded Green's function. # ν_δ comes from -Im[...]/π (same as get_hodc_weights), # ν_HT comes from Re[...]/π (real part of the same rational sum — no extra cost). @@ -1436,20 +1579,13 @@ end """ - get_exciton_ldos(H::TBHamiltonian, X::Int, ω_phys; Ncheb, kernel, eta, m_order, - lambda, maxdim, cutoff, verbose) -> Real + _get_exciton_ldos_cached(H::TBHamiltonian, X::Int, omega; kwargs...) -> Real -Exciton local spectral weight at physical energy `ω_phys` for the exciton state -|X,X⟩, where `X ∈ {1, …, 2^L}` (1-indexed, consistent with `add_onsite!`). - -If `H._tn_mps_cache` already holds `Ncheb` Chebyshev states (from a prior -`KPM_Tn(H, Ncheb, X)` call) they are reused — call `KPM_Tn(H, Ncheb, X)` first -when sweeping over many energies for the same site. Otherwise the Chebyshev -states are built on the fly and cached for subsequent calls. - -Returns `0` when `|E| ≥ 1` (energy outside the rescaled spectral support). +Internal legacy cache-backed scalar exciton LDOS helper. Public exciton LDOS now +routes through `get_exciton_ldos_spatial`, which performs online MPS recursion +without storing a Chebyshev cache on `H`. """ -function get_exciton_ldos(H::TBHamiltonian, X::Int, ω_phys::Real; +function _get_exciton_ldos_cached(H::TBHamiltonian, X::Int, ω_phys::Real; Ncheb::Int = 200, kernel::Symbol = :jackson, lambda::Real = 4.0, @@ -1478,6 +1614,153 @@ function get_exciton_ldos(H::TBHamiltonian, X::Int, ω_phys::Real; end +""" + get_exciton_ldos_spatial(H, Ncheb, omega_phys_vals; X_list, X_groups, + num_x, num_avg, x_start, x_end, kernel, + lambda, eta, m_order, maxdim, cutoff, + verbose, printinfo) -> Matrix{Float64} + +CPU spatial exciton LDOS. For each bound exciton position `X` (electron = hole = +`X`, 1-indexed in `1:H.N`) this runs an online MPS Chebyshev recursion from +`|X,X>` and accumulates all requested energies in one pass. No Chebyshev cache is +stored on `H`. + +Rows are energies, columns are positions/groups. `X_list` selects positions +directly. `X_groups` (or alias `x_groups`) averages several bound-pair probes into +one output column. If no explicit positions are provided, `num_x` coarse groups +are generated over `x_start:x_end`, with `num_avg` subpositions per group. + +`kernel=:hodc` uses the HODC reconstruction (`eta`, `m_order`); otherwise the +standard KPM kernels are available (`:jackson`, `:lorentz`, `:fejer`, +`:dirichlet`). +""" +function get_exciton_ldos_spatial(H::TBHamiltonian, Ncheb::Int, omega_phys_vals; + X_list = nothing, + X_groups = nothing, + x_groups = nothing, + num_x::Int = H.N, + num_avg::Int = 1, + x_start::Int = 1, + x_end::Int = H.N, + kernel::Symbol = :jackson, + lambda::Real = 4.0, + eta::Real = 0.0, + m_order::Int = 4, + maxdim::Int = 100, + cutoff::Real = 1e-8, + verbose::Bool = false, + printinfo::Bool = false) + _ensure_scale!(H) + length(H.sites) == 2 * H.L || + error("get_exciton_ldos_spatial: H is not an exciton Hamiltonian (expected length(H.sites) == 2*H.L).") + + X_groups !== nothing && x_groups !== nothing && + error("get_exciton_ldos_spatial: pass only one of X_groups or x_groups.") + X_list !== nothing && (X_groups !== nothing || x_groups !== nothing) && + error("get_exciton_ldos_spatial: pass either X_list or grouped positions, not both.") + + group_arg = X_groups !== nothing ? X_groups : x_groups + groups = if group_arg !== nothing + group_arg isa AbstractVector{<:AbstractVector} ? + [collect(Int, grp) for grp in group_arg] : + [[Int(x)] for x in group_arg] + elseif X_list !== nothing + [[Int(x)] for x in X_list] + else + num_x > 0 || error("get_exciton_ldos_spatial: num_x must be positive.") + num_avg > 0 || error("get_exciton_ldos_spatial: num_avg must be positive.") + 1 <= x_start <= x_end <= H.N || + error("get_exciton_ldos_spatial: expected 1 <= x_start <= x_end <= H.N.") + window = x_end - x_start + 1 + num_x <= window || + error("get_exciton_ldos_spatial: num_x=$num_x exceeds sampling window length $window.") + dx = div(window, num_x) + dx_sub = max(1, div(dx, num_avg)) + [[x_start + (i - 1) * dx + k * dx_sub + for k in 0:num_avg-1 + if x_start + (i - 1) * dx + k * dx_sub <= x_end] + for i in 1:num_x] + end + + isempty(groups) && error("get_exciton_ldos_spatial: no spatial groups were selected.") + for grp in groups + isempty(grp) && error("get_exciton_ldos_spatial: empty spatial group.") + all(x -> 1 <= x <= H.N, grp) || + error("get_exciton_ldos_spatial: all positions must lie in 1:H.N.") + end + + I_mpo = MPO(H.sites, "Id") + Ham_n = (1 / H.scale) * +(H.mpo, (-H.center) * I_mpo; cutoff=cutoff) + + omega_vals = (collect(omega_phys_vals) .- H.center) ./ H.scale + Nomega = length(omega_vals) + W, denom = _dos_weight_matrix(Ncheb, omega_vals; + kernel=kernel, lambda=lambda, + eta=eta, m_order=m_order) + valid = [abs(omega) < 1.0 for omega in omega_vals] + + nX = length(groups) + Xs = first.(groups) + result = zeros(Float64, Nomega, nX) + + for (j, group) in enumerate(groups) + last_linkdim = 0 + accum_group = zeros(Float64, Nomega) + + for X in group + psi0 = mpsexciton(X, H.sites) + last_linkdim = _run_kpm_mps!(Ham_n, psi0, Ncheb, W, valid, accum_group; + weight=1.0 / length(group), + cutoff=cutoff, maxdim=maxdim) + end + + for iomega in 1:Nomega + valid[iomega] || continue + result[iomega, j] = accum_group[iomega] / denom[iomega] + end + + (verbose || printinfo) && (j % 5 == 0 || j == nX) && + println(" exciton ldos $j/$nX (X=$(Xs[j]), n_avg=$(length(group))) maxlinkdim=$last_linkdim") + end + + return result +end + +function get_exciton_ldos(H::TBHamiltonian, X::Int, omega_phys::Real; + Ncheb::Int = 200, + kernel::Symbol = :jackson, + lambda::Real = 4.0, + eta::Real = 0.0, + m_order::Int = 4, + maxdim::Int = 40, + cutoff::Real = 1e-8, + verbose::Bool = false) + ldos = get_exciton_ldos_spatial(H, Ncheb, [omega_phys]; + X_list=[X], kernel=kernel, + lambda=lambda, eta=eta, + m_order=m_order, maxdim=maxdim, + cutoff=cutoff, verbose=verbose) + return ldos[1, 1] +end + +function get_exciton_ldos(H::TBHamiltonian, X::Int, omega_phys_vals; + Ncheb::Int = 200, + kernel::Symbol = :jackson, + lambda::Real = 4.0, + eta::Real = 0.0, + m_order::Int = 4, + maxdim::Int = 40, + cutoff::Real = 1e-8, + verbose::Bool = false) + ldos = get_exciton_ldos_spatial(H, Ncheb, omega_phys_vals; + X_list=[X], kernel=kernel, + lambda=lambda, eta=eta, + m_order=m_order, maxdim=maxdim, + cutoff=cutoff, verbose=verbose) + return vec(ldos[:, 1]) +end + + """ ldos_exc_KPM_Tn(H, N, X; cutoff, maxdim) -> Vector