Towards Heisenberg limit without critical slowing down via quantum reinforcement learning

2025-03-04

AI Summary

Conventional adiabatic preparation of critical quantum many-body states suffers from critical slowing-down and degraded sensing precision. Method: This work proposes a novel non-adiabatic critical-state preparation paradigm leveraging quantum reinforcement learning (QRL), which autonomously optimizes gate sequences starting from simple product states to achieve high-fidelity local and global critical-state preparation under unknown external magnetic fields. Contribution/Results: To our knowledge, this is the first application of QRL to overcome critical slowing-down, simultaneously optimizing for finite quantum speed limits and compatibility with Pauli measurements. The method generalizes to arbitrary system sizes and maintains robustness against decoherence noise. Both theoretical analysis and numerical experiments demonstrate sensing precision scaling at the Heisenberg limit (∝N²) and even beyond—surpassing adiabatic protocols significantly.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Overcome adiabatic evolution limitations in quantum sensing
• Enhance critical sensing in quantum many-body systems
• Achieve Heisenberg limit in noisy environments

Innovation

• Quantum reinforcement learning for critical sensing
• Achieves Heisenberg and super-Heisenberg limits
• Robust in noisy environments with Pauli measurements