Hardware-co-designed end-to-end differentiable reinforcement learning achieves robust >99.9% fidelity single-qubit gates in atomic quantum processors under realistic crosstalk and dynamic imperfections, outperforming PPO and SADE-Adam.
Quantumsupremacyusingaprogrammable superconducting processor.Nature, 574:505–510, 2019
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Hardware Co-Designed Optimal Control for Programmable Atomic Quantum Processors via Reinforcement Learning
Hardware-co-designed end-to-end differentiable reinforcement learning achieves robust >99.9% fidelity single-qubit gates in atomic quantum processors under realistic crosstalk and dynamic imperfections, outperforming PPO and SADE-Adam.