用强化学习提升量子模拟抗噪能力，减少错误暴露时间。
Noise tolerance via reinforcement: Learning a reinforced quantum dynamics
- 通过强化机制让量子系统保持状态或按无噪路径演化
- 一比特和两比特系统在泡利噪声下均显著降低误差
- 无需复杂反馈，适合实际量子硬件部署
量子模拟性能高度依赖噪声缓解与纠错算法的效率。强化学习已成为提升学习与优化算法效率的有效策略。本文展示，经强化的量子动力学对环境噪声具有显著鲁棒性。研究中采用量子退火过程，通过强化使系统倾向于维持当前状态或遵循无噪演化路径。利用学习算法推导出该强化动力学的简洁近似，缩短总演化时间，从而减少系统暴露于噪声中的时长。此方法避免了实现量子反馈带来的复杂性。数值仿真验证了在单比特与双比特系统上，受强化的量子退火在泡利噪声下均表现出优异性能。
原文摘要 · Abstract (English)
The performance of quantum simulations heavily depends on the efficiency of noise mitigation techniques and error correction algorithms. Reinforcement has emerged as a powerful strategy to enhance the efficiency of learning and optimization algorithms. In this study, we demonstrate that a reinforced quantum dynamics can exhibit significant robustness against interactions with a noisy environment. We study a quantum annealing process where, through reinforcement, the system is encouraged to maintain its current state or follow a noise-free evolution. A learning algorithm is employed to derive a concise approximation of this reinforced dynamics, reducing the total evolution time and, consequently, the system's exposure to noisy interactions. This also avoids the complexities associated with implementing quantum feedback in such reinforcement algorithms. The efficacy of our method is demonstrated through numerical simulations of reinforced quantum annealing with one- and two-qubit systems under Pauli noise.
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