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pqblocks-anneal-schedule

Quantum-inspired instance-adaptive simulated annealing with statistics-driven schedule generation 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_schedule
Command line library only
Version 0.1.0 (Alpha)
Python >=3.11
Native dependency none
Repository PQBlocks/annealing-schedule
Licence Apache-2.0

What is pqblocks-anneal-schedule?

Quantum-inspired instance-adaptive simulated annealing with statistics-driven schedule generation for Ising and QUBO optimization problems.

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-schedule

uv add pqblocks-anneal-schedule

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

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

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_schedule import (
    compute_instance_stats,
    generate_schedule,
    simulated_annealing,
)

# Define a 4-spin Ising ring
J = np.array([
    [0.0, -1.0,  0.0,  1.0],
    [-1.0,  0.0, -1.0,  0.0],
    [0.0, -1.0,  0.0, -1.0],
    [1.0,  0.0, -1.0,  0.0]
], dtype=float)
h = np.zeros(4, dtype=float)

# Extract graph statistics and synthesize adaptive schedule
stats = compute_instance_stats(J, h)
schedule = generate_schedule(stats, n=4, max_degree=2, budget_steps=64)

# Execute simulated annealing
result = simulated_annealing(J, h, schedule, seed=42, num_steps=64, num_runs=1)

print(f"Best Energy: {result.best_energy:.6f}")
print("Best Config:", result.best_config)

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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  • pqblocks-qiea: Quantum-Inspired Evolutionary Algorithm (QIEA) with Q-bit representations and quantum rotation gates for Ising and…
  • pqblocks-qubo: Unified mathematical representation and converter library for Quadratic Unconstrained Binary Optimization (QUBO) and…
  • pqblocks-qubo-bench: Benchmarking harness and standard instance loaders for quantum-inspired optimization algorithms against classical…