用强化学习优化60量子比特传感电路，提升灵敏度并大幅减少门数和深度。
Reinforcement Learning for Optimizing Large Qubit Array based Quantum Sensor Circuits
- 用张量网络（MPS）模拟结合强化学习，实现大规模量子电路优化。
- 量子费舍尔信息接近1，纠缠熵达0.8-1.0，电路深度与门数降低90%。
- 适合研究量子传感、量子算法设计及量子机器学习交叉方向的学者。
随着传感器中量子比特数量增加，量子电路的设计与控制复杂度呈指数级增长，手动优化已不可行。在大规模量子电路中优化纠缠分布对提升量子传感器的灵敏度和效率至关重要。本文提出将强化学习与基于张量网络的模拟（MPS）相结合，实现最多60量子比特的量子传感器电路可扩展优化。为提升仿真效率与可扩展性，采用矩阵乘积态（MPS）表示，而非传统态矢量或密度矩阵方法。强化学习智能体通过重构电路，以最大化量子费舍尔信息（QFI）和纠缠熵，同时减少门数与电路深度。实验结果显示，QFI值趋近1，纠缠熵保持在0.8–1.0区间，电路深度与门数最高减少90%。结果表明，量子机器学习与张量网络结合可在实际约束下有效优化复杂量子电路。
原文摘要 · Abstract (English)
As the number of qubits in a sensor increases, the complexity of designing and controlling the quantum circuits grows exponentially. Manually optimizing these circuits becomes infeasible. Optimizing entanglement distribution in large-scale quantum circuits is critical for enhancing the sensitivity and efficiency of quantum sensors [5], [6]. This paper presents an engineering integration of reinforcement learning with tensor-network-based simulation (MPS) for scalable circuit optimization for optimizing quantum sensor circuits with up to 60 qubits. To enable efficient simulation and scalability, we adopt tensor network methods, specifically the Matrix Product State (MPS) representation, instead of traditional state vector or density matrix approaches. Our reinforcement learning agent learns to restructure circuits to maximize Quantum Fisher Information (QFI) and entanglement entropy while reducing gate counts and circuit depth. Experimental results show consistent improvements, with QFI values approaching 1, entanglement entropy in the 0.8-1.0 range, and up to 90% reduction in depth and gate count. These results highlight the potential of combining quantum machine learning and tensor networks to optimize complex quantum circuits under realistic constraints.
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