A randomized quench protocol enables the first fully analog measurement of infinite-temperature OTOCs on Rydberg atom arrays, revealing information propagation lightcones.
Reinforcement learning for quantum technology
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Treating the replay buffer as a central lever in RL for quantum circuit optimization yields 4-32x sample efficiency gains, up to 67.5% faster episodes, and 85-90% fewer steps to accuracy on noisy molecular and compilation tasks.
Reinforcement learning optimizes ion shuttling on trapped-ion quantum chips and reduces operations by up to 36.3% versus heuristics across multiple architectures.
Reinforcement learning stabilizes more than 4 dB of fixed-axis spin squeezing under continuous nonlinear Zeeman evolution in the f=21/2 manifold of 161Dy, yielding a single-atom sensitivity of 13.9 pT/sqrt(Hz) that is 3 dB beyond the standard quantum limit.
citing papers explorer
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Information Propagation in Rydberg Arrays via Analog OTOC Calculations
A randomized quench protocol enables the first fully analog measurement of infinite-temperature OTOCs on Rydberg atom arrays, revealing information propagation lightcones.
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Replay-buffer engineering for noise-robust quantum circuit optimization
Treating the replay buffer as a central lever in RL for quantum circuit optimization yields 4-32x sample efficiency gains, up to 67.5% faster episodes, and 85-90% fewer steps to accuracy on noisy molecular and compilation tasks.
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Reinforcement learning for ion shuttling on trapped-ion quantum computers
Reinforcement learning optimizes ion shuttling on trapped-ion quantum chips and reduces operations by up to 36.3% versus heuristics across multiple architectures.
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Learning Unified Control of Intrinsic Nonlinear Spin Dynamics in Atomic Qudits for Magnetometry
Reinforcement learning stabilizes more than 4 dB of fixed-axis spin squeezing under continuous nonlinear Zeeman evolution in the f=21/2 manifold of 161Dy, yielding a single-atom sensitivity of 13.9 pT/sqrt(Hz) that is 3 dB beyond the standard quantum limit.