Reinforcement learning controls photonic circuits to prepare cubic-phase states at 96% success and directly generate quartic-phase gates with photon-number-resolving measurements.
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Binary-detector Gaussian boson sampling is proposed for sample-efficient graph classification, with an investigation into its connection to the Torontonian matrix function.
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Deep reinforcement learning for near-deterministic preparation of cubic- and quartic-phase gates in photonic quantum computing
Reinforcement learning controls photonic circuits to prepare cubic-phase states at 96% success and directly generate quartic-phase gates with photon-number-resolving measurements.
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Sample efficient graph classification using binary Gaussian boson sampling
Binary-detector Gaussian boson sampling is proposed for sample-efficient graph classification, with an investigation into its connection to the Torontonian matrix function.