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Parallel Tempering

Parallel Tempering uses multiple optimization procedures per temperature. During the cooling process, an exchange of replicas can take place between the parallel procedures, thus enabling higher energy mountains to be overcome.

Compatible Backends

Backend Default
DWave

Initialization

Python
from luna_quantum.algorithms import ParallelTempering

algorithm = ParallelTempering(
    backend=None,
    n_replicas=2,
    random_swaps_factor=1,
    max_iter=100,
    max_time=5,
    convergence=3,
    target=None,
    rtol=1e-05,
    atol=1e-08,
    fixed_temp_sampler_num_sweeps=10000,
    fixed_temp_sampler_num_reads=None
)

Usage

from luna_quantum.algorithms import ParallelTempering

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