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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Formation of a bound state in the agent-noise energy spectrum restores QRL performance to the noiseless case for eigenstate solving under non-Markovian decoherence.
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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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Noise-Resilient Quantum Reinforcement Learning
Formation of a bound state in the agent-noise energy spectrum restores QRL performance to the noiseless case for eigenstate solving under non-Markovian decoherence.