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Simulated Annealing

Simulated Annealing is a probabilistic technique for approximating the global optimum of a given function. It performs well on problems where approximate global optima are more desirable than exact local optima.

Compatible Backends

Backend Default
DWave

Initialization

Python
from luna_quantum.solve.parameters.algorithms.simulated_annealing.simulated_annealing import SimulatedAnnealing

algorithm = SimulatedAnnealing(
    backend=None,
    num_reads=None,
    num_sweeps=1000,
    beta_range=None,
    beta_schedule_type='geometric',
    initial_states_generator='random',
    num_sweeps_per_beta=1,
    seed=None,
    beta_schedule=None,
    initial_states=None,
    randomize_order=False,
    proposal_acceptance_criteria='Metropolis'
)

Usage

from luna_quantum.algorithms import SimulatedAnnealing

algorithm = SimulatedAnnealing()
solve_job = algorithm.run(model, name="my-solve-job")