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.
Classical algorithm for simulating experimental Gaussian boson sampling
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Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.
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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.
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Quantum-inspired tensor networks in machine learning models
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.