Xu Hang, Xiao Tailong, Huang Jingzheng, He Ming, Fan Jianping, Zeng Guihua
Shanghai Jiao Tong University, State Key Laboratory of Advanced Optical Communication Systems and Networks, Institute for Quantum Sensing and Information Processing, Shanghai 200240, People's Republic of China.
Hefei National Laboratory, Hefei 230088, People's Republic of China.
Phys Rev Lett. 2025 Mar 28;134(12):120803. doi: 10.1103/PhysRevLett.134.120803.
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 Letter, 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.
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。制备这些态的传统方法通常依赖绝热演化，这可能会削弱量子传感优势。在本信函中，我们为具有奇异相图的量子多体系统提出了一种量子强化学习（QRL）增强的临界传感协议。从乘积态开始并利用QRL发现的门序列，我们探索在存在未知外部磁场的情况下的传感精度，涵盖局部和全局情况。我们的结果表明，QRL学习的序列达到了有限量子速度极限，并能有效地推广到任意大小的系统，无论制备时间如何都能确保精度。即使在具有实际泡利测量的噪声环境中，该方法也能稳健地达到海森堡极限和超海森堡极限。我们的研究突出了QRL在实现精确量子态制备方面的有效性，从而推动了可扩展的、高精度的量子临界传感。
