Deep reinforcement learning with incremental updates optimizes Rydberg CNOT gates to 0.9991 average fidelity by discovering smooth pulses and an early-cutoff policy.
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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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Intelligent Optimal Control of Rydberg Gates with Incremental-Update Deep Reinforcement Learning
Deep reinforcement learning with incremental updates optimizes Rydberg CNOT gates to 0.9991 average fidelity by discovering smooth pulses and an early-cutoff policy.
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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.