Towards Heisenberg limit without critical slowing down via quantum reinforcement learning

• URL: http://arxiv.org/abs/2503.02210v1
• Date: Tue, 04 Mar 2025 02:42:27 GMT
• Title: Towards Heisenberg limit without critical slowing down via quantum reinforcement learning
• Authors: Hang Xu, Tailong Xiao, Jingzheng Huang, Ming He, Jianping Fan, Guihua Zeng,
• Abstract summary: We propose a quantum reinforcement learning (QRL)-enhanced critical sensing protocol for quantum many-body systems.<n>We show that QRL-learned sequences reach the finite quantum speed limit and generalize effectively across systems of arbitrary size.<n>Our study highlights the efficacy of QRL in enabling precise quantum state preparation, thereby advancing scalable, high-accuracy quantum critical sensing.
• Score: 24.980216976860866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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