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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In finite-depth random linear optical circuits, entanglement grows at most diffusively and robust circuit complexity scales similarly, with depth bounds ensuring near-maximal subsystem entanglement and closeness to Haar unitaries.
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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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Entanglement and circuit complexity in finite-depth random linear optical networks
In finite-depth random linear optical circuits, entanglement grows at most diffusively and robust circuit complexity scales similarly, with depth bounds ensuring near-maximal subsystem entanglement and closeness to Haar unitaries.