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QBSolv-like QPU

The QBSolv-like QPU is a specialized solver designed to tackle complex problems by decomposing them into smaller, manageable subproblems. It then solves these individual parts using a Quantum Processing Unit (QPU).

This particular implementation achieves its QBSolv-like behavior by utilizing the hybrid.QPUSubproblemAutoEmbeddingSampler from the D-Wave Ocean SDK as the core sampler for these subproblems. This approach allows for efficient handling of large-scale problems by breaking them down and leveraging the strengths of quantum computation for the subcomponents.

Note

This solver is only available for commercial and academic licenses.

Compatible Backends

Backend Default
DWaveQpu

Initialization

Python
from luna_quantum.solve.parameters.algorithms.base_params.decomposer import Decomposer
from luna_quantum.solve.parameters.algorithms.base_params.quantum_annealing_params import QuantumAnnealingParams
from luna_quantum.solve.parameters.algorithms.quantum_annealing.qbsolv_like_qpu import QBSolvLikeQpu

algorithm = QBSolvLikeQpu(
    backend=None,
    decomposer_size=50,
    rolling=True,
    rolling_history=0.15,
    max_iter=100,
    max_time=5,
    convergence=3,
    target=None,
    rtol=1e-05,
    atol=1e-08,
    num_reads=100,
    num_retries=0,
    quantum_annealing_params=QuantumAnnealingParams(
        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
    ),
    decomposer=Decomposer(
        size=10,
        min_gain=None,
        rolling=True,
        rolling_history=1.0,
        silent_rewind=True,
        traversal='energy'
    )
)

Usage

Python
from luna_quantum.algorithms import QBSolvLikeQpu

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