Ascent Science SQA plugin

This package implements simulated quantum annealing (SQA) with the standard Suzuki--Trotter path-integral mapping, single-spin Metropolis moves, and optional imaginary-time world-line cluster updates. It is a classical simulation of a transverse-field Ising model; it does not use a quantum processor.

For development from this repository, install the core SDK and plugin in editable mode:

python -m pip install -e ./sdk-python -e ./sdk-plugins/sqa

Install the 0.2.0 release with python -m pip install ascent-science-sqa==0.2.0.

import ascent

solver = ascent.quantum.SimulatedQuantumAnnealing(
    trotter_slices=32,
    sweeps=500,
    beta=5.0,
    gamma_start=5.0,
    gamma_end=0.01,
    num_reads=8,
    update_mode="hybrid",
    seed=42,
)

# Minimize x.T Q x for x in {0, 1}^n.
result = solver.solve_qubo([
    [-2.0, 1.0],
    [1.0, -2.0],
])
print(result.best_solution, result.best_value)

For an Ising model, solve_ising(J, fields=h) minimizes s.T @ J @ s + h.T @ s + offset, with spins in {-1, +1}. For a canonical problem object use solver.solve(QuboProblem(...)) or solver.solve(IsingProblem(...)).

SQA is stochastic and does not prove optimality. Results therefore have status FEASIBLE (or TIME_LIMIT if a supplied SolveOptions limit is reached).

update_mode="hybrid" combines local moves with Swendsen--Wang world-line cluster moves. In addition to the monotone best-so-far history, metrics expose instantaneous and mean replica energy, reduced effective action, magnetization, slice alignment, per-sweep acceptance, and per-read best values. These traces use Monte Carlo sweeps, not real quantum-hardware time.

Build a release

Use the same release flow as the ACO plugin:

sh scripts/build-release.sh

The wheel, source archive, and SHA256SUMS are written to dist/. If the default Python does not provide a recent setuptools/build installation, select another interpreter with ASCENT_SQA_BUILD_PYTHON=/path/to/python.