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Quantum Annealing

Quantum Annealing uses D-Wave's purpose build Quantum Processing Units (QPU) to solve QUBO optimization problems with the help of the adiabatic theorem of quantum mechanics. This implementation first applies D-Wave's minor embedding to map the provided problem onto the hardware graph of the desired sampler.

Compatible Backends

Backend Default
DWaveQpu

Initialization

Python
from luna_quantum.solve.parameters.algorithms.quantum_annealing.quantum_annealing import QuantumAnnealing

algorithm = QuantumAnnealing(
    backend=None,
    anneal_offsets=None,
    anneal_schedule=None,
    annealing_time=None,
    auto_scale=None,
    fast_anneal=False,
    flux_biases=None,
    flux_drift_compensation=True,
    h_gain_schedule=None,
    initial_state=None,
    max_answers=None,
    num_reads=1,
    programming_thermalization=None,
    readout_thermalization=None,
    reduce_intersample_correlation=False,
    reinitialize_state=None
)

Usage

from luna_quantum.algorithms import QuantumAnnealing

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