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Repeated Reverse Quantum Annealing

Repeated Reverse Quantum Annealing begins the annealing process from a previously initialized state and increases the temperature from there. Afterwards, the temperature is decreased again until the solution is found. This procedure is repeated several times with this particular solver.

Note

This solver is only available for commercial and academic licenses.

Compatible Backends

Backend Default
DWaveQpu

Initialization

Python
from luna_quantum.solve.parameters.algorithms.quantum_annealing.repeated_reverse_quantum_annealing import RepeatedReverseQuantumAnnealing

algorithm = RepeatedReverseQuantumAnnealing(
    backend=None,
    anneal_offsets=None,
    annealing_time=None,
    auto_scale=None,
    flux_biases=None,
    flux_drift_compensation=True,
    h_gain_schedule=None,
    max_answers=None,
    programming_thermalization=None,
    readout_thermalization=None,
    reduce_intersample_correlation=False,
    initial_states=None,
    n_initial_states=1,
    samples_per_state=1,
    beta_schedule=[0.5, 3],
    timeout=300,
    max_iter=10,
    target=None,
    check_trivial=True
)

Usage

from luna_quantum.algorithms import RepeatedReverseQuantumAnnealing

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