Rydberg atom tweezer arrays can detect dark-photon dark matter with sensitivity to unexplored parameter space by scanning via Zeeman and diamagnetic shifts under external magnetic fields.
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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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Detecting dark matter using optically trapped Rydberg atom tweezer arrays
Rydberg atom tweezer arrays can detect dark-photon dark matter with sensitivity to unexplored parameter space by scanning via Zeeman and diamagnetic shifts under external magnetic fields.
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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.