qanneal 0.1.1

Research-grade simulated quantum annealing toolkit

qanneal

Research-grade simulated quantum annealing toolkit (CPU-first, CUDA-ready).

What it is

• Classical simulated annealing (SA) and simulated quantum annealing (SQA) for dense and sparse Ising/QUBO models.
• C++ core with Python bindings (pybind11).
• Sweep-level tracing for reproducibility and diagnostics.
• MPI/SLURM scaffolding for multi-process runs.

---

Physics and Models

QUBO

• Binary variables: x_i ∈ {0,1}
• Energy: E(x) = Σ_i Σ_j Q_ij x_i x_j
• Diagonal Q[i,i] = linear terms, off-diagonal Q[i,j] = pairwise couplings.

Ising

• Spins: s_i ∈ {-1,+1}
• Energy: E(s) = Σ_i h_i s_i + Σ_ij J_ij s_i s_j + c
• QUBO→Ising mapping: x_i = (1 + s_i)/2

SQA (path-integral picture)

• Transverse-field Ising model mapped to M Trotter slices (imaginary time).
• Effective inter-slice coupling: J_perp = 0.5 * log(coth(βΓ/M))
• Two update phases per step: slice updates and worldline updates.

---

Install

macOS / Linux

./setup.sh

Windows (PowerShell)

.\setup.ps1

Windows (Command Prompt)

setup.bat

Pure pip (all platforms)

python -m pip install . --no-build-isolation

From PyPI (after first release)

python -m pip install qanneal

Windows prerequisites

• Visual Studio Build Tools with Desktop development with C++
• CMake (e.g., winget install Kitware.CMake)

---

Quickstart (QUBO → SQA)

import numpy as np
from qanneal import QUBO, SQASchedule, SQAAnnealer

Q = np.array([[1.0, -1.0],
              [-1.0, 2.0]], dtype=float)

qubo = QUBO(Q)
ising = qubo.to_ising()

betas = np.linspace(0.1, 4.0, 50).tolist()
gammas = np.linspace(5.0, 0.01, 50).tolist()
schedule = SQASchedule.from_vectors(betas, gammas)

annealer = SQAAnnealer(ising, schedule, trotter_slices=32, replicas=4, backend="cpu")
result = annealer.run(sweeps_per_beta=20, worldline_sweeps=5)
print(result.best_energy)

QUBO from sparse entries

from qanneal import QUBO

entries = [
    (0, 0, 1.0),   # linear term on x0
    (1, 1, 2.0),   # linear term on x1
    (0, 1, -1.5),  # coupling x0*x1
]

qubo = QUBO(entries, n=2)
ising = qubo.to_ising()

---

Core Parameters (SQA)

Schedule

• betas: inverse temperature values (cooling).
• gammas: transverse-field values (quantum fluctuations).
• steps: len(betas), must equal len(gammas).

Geometry

• trotter_slices: number of imaginary-time slices.
• replicas: independent replicas in a single run.

Monte Carlo

• sweeps_per_beta: slice-update sweeps per step.
• worldline_sweeps: worldline-update sweeps per step.

Tracing

• SQAStateTraceObserver.stride: record every N sweeps.

---

Observers and Traces

Classical SA

• MetricsObserver: energy + magnetization traces.
• StateTraceObserver: sweep-level states and energies.

SQA

• SQAMetricsObserver: energy + magnetization traces.
• SQAStateTraceObserver: full sweep-level state trace, per-replica energies, and phase (slice vs worldline).

See docs/sqa_trace_parameters.md for a full explanation.

---

Examples

python examples/python/sa_multi.py
python examples/python/sqa_basic.py
python examples/python/metrics_plot.py
python examples/python/parallel_tempering.py
python examples/python/sqa_trace_full.py

---

Build (C++ core)

cmake -S . -B build
cmake --build build
ctest --test-dir build

CMake presets

cmake --preset cpu-only
cmake --build --preset cpu-only
ctest --preset cpu-only

MPI build

cmake -S . -B build -DQANNEAL_ENABLE_MPI=ON
cmake --build build
mpirun -n 4 build/qanneal_mpi_example

SLURM scripts

• scripts/slurm/run_sa_mpi_srun.sh
• scripts/slurm/run_sa_mpi_mpirun.sh

---

Docs

• docs/overview.md
• docs/api.md
• docs/sqa_trace_parameters.md
• docs/latex/qanneal_technical_report.tex

---

Release (PyPI wheels)

1. Update version in pyproject.toml.
2. Tag and push:

git tag v0.1.0
git push origin v0.1.0

3. GitHub Actions builds wheels and publishes to PyPI.

---

License Apache-2.0 (see LICENSE). Portions derived from sqaod with attribution in NOTICE.