Statistical benchmark methods originally for discriminating boson sampling can quantify noises like partial distinguishability and loss, performing better with high-order correlators, while a new fast simulation scheme for noisy samples is introduced.
Solving Graph Problems Using Gaussian Boson Sampling
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Hybrid GBS with classical post-processing for DkSP achieves near-optimal solutions and ~4X sampling efficiency gains on community graphs while outperforming pure post-selection on sparse graphs.
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Evaluating noises of fast-simulated boson sampling with statistical benchmark methods
Statistical benchmark methods originally for discriminating boson sampling can quantify noises like partial distinguishability and loss, performing better with high-order correlators, while a new fast simulation scheme for noisy samples is introduced.