Toward Heisenberg Limit without Critical Slowing Down via Quantum Reinforcement Learning.
Hang Xu, Tailong Xiao, Jingzheng Huang, Ming He, Jianping Fan, Guihua Zeng
Author Information
Hang Xu: 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.
Tailong Xiao: 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.
Jingzheng Huang: 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.
Ming He: Lenovo Research, AI Lab, Beijing 100094, People's Republic of China.
Jianping Fan: Lenovo Research, AI Lab, Beijing 100094, People's Republic of China.
Guihua Zeng: 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.
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.
