Repeated Reverse Simulated Annealing

Repeated Reverse Simulated Annealing finds the solution to a problem using an annealing process. Initially, random states are chosen in the solution landscape. Afterwards, as the temperature decreases, states are chosen that are more energetically favorable. At the end of the complete annealing process, the resulting states make up the solution.

Note

This solver is only available for commercial and academic licenses.

Compatible Backends

Backend	Default
DWave

Initialization

Python

from luna_quantum.solve.parameters.algorithms.simulated_annealing.repeated_reverse_simulated_annealing import RepeatedReverseSimulatedAnnealing

algorithm = RepeatedReverseSimulatedAnnealing(
    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',
    num_reads_per_iter=None,
    timeout=5.0,
    max_iter=10,
    target=None
)

Usage

from luna_quantum.algorithms import RepeatedReverseSimulatedAnnealing

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