Simulated Annealing

SimulatedAnnealingSolver

Simple classical solver class using Simulated Annealing. Designed to integrate with the solver factory.

Signature

class SimulatedAnnealingSolver(BaseClassicalSolver):
    def solve(self) -> QUBOSolution

Description

This solver uses a Simulated Annealing to probabilistically explore the solution space. It is suitable for approximating solutions on medium-sized QUBO instances. Computation is entirely classical and based on the SimulatedAnnealingSolver. The output is fully compatible with the QUBOSolution structure used in the qubo-solver package.

Fields

Field	Type	Description
use_quantum	bool	Have to be False to use a classical solver.
classical_solver_type	str	Set to "simulated_annealing" to use Simulated Annealing as the solving method.
max_iter	int	Maximum number of iterations to perform for simulated annealing or tabu search.
sa_initial_temp	float	Starting temperature (controls exploration).
sa_final_temp	float	Minimum temperature threshold for stopping.
sa_alpha	float	Cooling rate - should be slightly below 1 (e.g., 0.95–0.99).
sa_time_limit	float	Maximum execution time for simulated annealing, in seconds. If infinite, no time limit is applied.

Usage

from qubosolver import QUBOInstance
from qubosolver.solver import QuboSolver
from qubosolver.config import SolverConfig, ClassicalConfig

qubo = QUBOInstance(coefficients=[[-2.0, 1.0], [1.0, -2.0]])
config = SolverConfig(
    use_quantum=False,
    classical=ClassicalConfig(
        classical_solver_type="simulated_annealing",
        max_iter=1000,
        sa_time_limit=300.0,
    ),
)

solver = QuboSolver(qubo, config)

solution = solver.solve()
print(solution)

QUBOSolution(bitstrings=tensor([[0, 1]], dtype=torch.int32), costs=tensor([-2.], dtype=torch.float64), counts=tensor([1]), probabilities=tensor([1.]), solution_status=)

Notes

Recommended for local, classical solving when exact optimization is not required.

Made with Material for MkDocs (external)