Towards Heisenberg limit without critical slowing down via quantum reinforcement learning
This work addresses the challenge of scalable and high-accuracy quantum sensing for quantum many-body systems, representing an incremental improvement over traditional methods.
The authors tackled the problem of preparing critical ground states for quantum sensing without the slowdown of adiabatic evolution by using quantum reinforcement learning to discover gate sequences, achieving Heisenberg and super-Heisenberg limits in accuracy even under noise.
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.