用强化学习自动设计量子退相干抑制脉冲序列，无需知道噪声详情。
Group-Theoretic Reinforcement Learning of Dynamical Decoupling Sequences
- 基于汤普森群F构建动作集，高效搜索最优脉冲序列。
- 在未知噪声谱下仍能学习出显著降低退相干的脉冲序列。
- 适合需要实时优化的量子计算系统，尤其抗噪声能力弱的场景。
动力学解耦通过精心设计的一系列瞬时电磁脉冲来缓解量子比特的相位退相干。尽管在特定噪声环境下存在解析解，但针对真实噪声谱的最优脉冲时序仍难求解。本文提出一种基于强化学习（RL）的量子比特脉冲序列设计方法。其新颖的动作集源自汤普森群 $F$，适用于可表示为有界序列的广义序列决策问题，使智能体高效穿越固有的非凸优化空间。实验表明，该RL智能体可在不依赖底层噪声谱信息的情况下，学习到有效抑制退相干的脉冲序列。该工作为退相干受限的量子比特实现最优动态解耦序列的实时学习提供了可能。算法的无模型特性意味着，即使存在未建模的物理效应（如脉冲误差或非高斯噪声），智能体仍可能最终学到最优脉冲序列。
原文摘要 · Abstract (English)
Dynamical decoupling seeks to mitigate phase decoherence in qubits by applying a carefully designed sequence of effectively instantaneous electromagnetic pulses. Although analytic solutions exist for pulse timings that are optimal under specific noise regimes, identifying the optimal timings for a realistic noise spectrum remains challenging. We propose a reinforcement learning (RL)-based method for designing pulse sequences on qubits. Our novel action set enables the RL agent to efficiently navigate this inherently non-convex optimization landscape. The action set, derived from Thompson's group $F$, is applicable to a broad class of sequential decision problems whose states can be represented as bounded sequences. We demonstrate that our RL agent can learn pulse sequences that minimize dephasing without requiring explicit knowledge of the underlying noise spectrum. This work opens the possibility for real-time learning of optimal dynamical decoupling sequences on qubits which are dephasing-limited. The model-free nature of our algorithm suggests that the agent may ultimately learn optimal pulse sequences even in the presence of unmodeled physical effects, such as pulse errors or non-Gaussian noise.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。