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Population Annealing

Population Annealing uses a sequential Monte Carlo method to minimize the energy of a population. The population consists of walkers that can explore their neighborhood during the cooling process. Afterwards, walkers are removed and duplicated using bias to lower energy. Eventually, a population collapse occurs where all walkers are in the lowest energy state.

Compatible Backends

Backend Default
DWave

Initialization

Python
from luna_quantum.solve.parameters.algorithms.simulated_annealing.population_annealing import PopulationAnnealing

algorithm = PopulationAnnealing(
    backend=None,
    max_iter=20,
    max_time=2,
    fixed_temp_sampler_num_sweeps=10000,
    fixed_temp_sampler_num_reads=None
)

Usage

from luna_quantum.algorithms import PopulationAnnealing

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