用量子强化学习实现无退相干加速的海森堡极限传感
Towards Heisenberg limit without critical slowing down via quantum reinforcement learning
- 用量子强化学习自动发现最优门序列，避开传统绝热演化
- 在任意系统尺寸下达到有限量子速限，实现海森堡及超海森堡精度
- 对噪声和未知磁场鲁棒，适合实际量子传感场景
量子多体系统的临界基态已成为量子增强传感的重要资源。传统制备方法依赖绝热演化，可能削弱量子传感优势。本文提出一种基于量子强化学习（QRL）的临界传感协议，适用于具有奇异相图的量子多体系统。从产品态出发，利用QRL发现的门序列，在未知外磁场环境下探索局部与全局传感精度。结果表明，QRL学习到的序列可达到有限量子速限，并在任意系统尺寸下有效泛化，确保无论制备时间长短均保持高精度。该方法可在含实际泡利测量的噪声环境中稳健实现海森堡极限及超海森堡极限。研究证明QRL在精确量子态制备中的有效性，推动可扩展、高精度量子临界传感的发展。
原文摘要 · Abstract (English)
Critical ground states of quantum many-body systems have emerged as vital resources for quantum-enhanced sensing. Traditional methods to prepare these states often rely on adiabatic evolution, which may diminish the quantum sensing advantage. In this work, we propose a quantum reinforcement learning (QRL)-enhanced critical sensing protocol for quantum many-body systems with exotic phase diagrams. Starting from product states and utilizing QRL-discovered gate sequences, we explore sensing accuracy in the presence of unknown external magnetic fields, covering both local and global regimes. Our results demonstrate that QRL-learned sequences reach the finite quantum speed limit and generalize effectively across systems of arbitrary size, ensuring accuracy regardless of preparation time. This method can robustly achieve Heisenberg and super-Heisenberg limits, even in noisy environments with practical Pauli measurements. Our study highlights the efficacy of QRL in enabling precise quantum state preparation, thereby advancing scalable, high-accuracy quantum critical sensing.
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