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Pasqal Documentation

Simulated Annealing

SimulatedAnnealingSolver

Section titled “SimulatedAnnealingSolver”

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

Signature

Section titled “Signature”
class SimulatedAnnealingSolver(BaseClassicalSolver):
    def solve(self) -> QUBOSolution

Description

Section titled “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

Section titled “Fields”
Field Type Description
use_quantum bool Have to be False to uses 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).

Usage

Section titled “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"))


solver = QuboSolver(qubo, config)


solution = solver.solve()
print(solution)
QUBOSolution(bitstrings=tensor([[1., 1.]]), costs=tensor([-2.]), counts=None, probabilities=None, solution_status=)

Notes

Section titled “Notes”

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