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pqblocks-anneal-sa

Simulated Annealing heuristic solver for Ising and QUBO optimization problems. Quantum-inspired classical optimization, not cryptography.

PyPI Area Status Python Licence Source

At a glance
Area Quantum-inspired
Import import pqblocks_anneal_sa
Command line pqblocks-anneal-sa
Version 0.1.0 (Alpha)
Python >=3.11
Native dependency none
Repository PQBlocks/simulated-annealing
Licence Apache-2.0

What is pqblocks-anneal-sa?

Classical Simulated Annealing heuristic solver for Ising spin-glass and QUBO optimization problems with automatic parameter derivation.

Domain Classification: Quantum-inspired classical optimisation: not cryptography, no security claim.

Not cryptography

Quantum-inspired classical optimisation: not cryptography, no security claim.

Install

pip install pqblocks-anneal-sa

uv add pqblocks-anneal-sa

pip install git+https://github.com/PQBlocks/simulated-annealing

uv add git+https://github.com/PQBlocks/simulated-annealing

Quickstart

Executed, not just written

This is the quickstart from the package README. It was run against the package source when this page was generated.

quickstart.py
import numpy as np
from pqblocks_anneal_sa import simulated_annealing

# 1. Define 4-spin Ising problem (couplings J and linear biases h)
J = np.array([
    [0.0, 1.0, -1.0, 0.0],
    [1.0, 0.0, 1.0, -1.0],
    [-1.0, 1.0, 0.0, 1.0],
    [0.0, -1.0, 1.0, 0.0],
])
h = np.array([0.5, -0.3, 0.2, -0.1])

# 2. Solve with auto-derived parameters
result = simulated_annealing(J, h, num_sweeps=100, num_reads=10, seed=42)

# 3. Retrieve best solution
best_idx = np.argmin(result["energies"])
print(f"Best energy: {result['energies'][best_idx]:.6f}")
print(f"Sample: {result['samples'][best_idx].tolist()}")

Examples

Runnable scripts in the repository:

Documentation and evidence

Every PQBlocks package ships the same evidence set, so each claim can be checked against tests, sources and measurements.

  • Overview

    What it does, install, usage and API.

  • For AI agents

    Correct imports, pitfalls and rules for coding assistants.

  • Security notes

    What it protects, hostile input, constant-time and audit status.

  • Testing

    Test suites, coverage, vectors and what is not covered.

  • Benchmarks

    Measured performance, including where it loses.

  • Evidence

    Primary sources and the method-to-code map.

  • Verification report

    Self-verification results, defects found and limits.

  • Use cases

    Worked scenarios and when to pick something else.

  • Changelog

    Every release and what changed.

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