用量子机器学习优化传感电路的纠缠分布，提升灵敏度与效率
Quantum Machine Learning for Optimizing Entanglement Distribution in Quantum Sensor Circuits
- 用强化学习在量子环境中优化纠缠布局
- 实现0.84-1.0的高量子费舍尔信息与纠缠熵，降低20%-86%电路深度和门数
- 适合关注量子传感与量子电路优化的研究者
在快速发展的量子计算领域，针对特定任务优化量子线路对提升性能和效率至关重要。近年来，量子传感已成为量子科学与技术中的重要研究分支，有望实现高灵敏度与高精度测量。纠缠是实现高灵敏度与测量精度的关键因素。本文提出一种利用量子机器学习技术优化量子传感器电路中纠缠分布的新方法。通过在量子环境中引入强化学习，旨在优化纠缠布局，以最大化量子费舍尔信息（QFI）和纠缠熵，这两者是衡量量子系统灵敏度与相干性的关键指标，同时最小化电路深度与门数量。基于Qiskit的实现整合了噪声模型与错误缓解策略，以模拟真实量子环境。结果表明，电路性能与灵敏度显著提升，在保持0.84-1.0的高QFI与纠缠熵的同时，电路深度和门数减少20%-86%。
原文摘要 · Abstract (English)
In the rapidly evolving field of quantum computing, optimizing quantum circuits for specific tasks is crucial for enhancing performance and efficiency. More recently, quantum sensing has become a distinct and rapidly growing branch of research within the area of quantum science and technology. The field is expected to provide new opportunities, especially regarding high sensitivity and precision. Entanglement is one of the key factors in achieving high sensitivity and measurement precision [3]. This paper presents a novel approach utilizing quantum machine learning techniques to optimize entanglement distribution in quantum sensor circuits. By leveraging reinforcement learning within a quantum environment, we aim to optimize the entanglement layout to maximize Quantum Fisher Information (QFI) and entanglement entropy, which are key indicators of a quantum system's sensitivity and coherence, while minimizing circuit depth and gate counts. Our implementation, based on Qiskit, integrates noise models and error mitigation strategies to simulate realistic quantum environments. The results demonstrate significant improvements in circuit performance and sensitivity, highlighting the potential of machine learning in quantum circuit optimization by measuring high QFI and entropy in the range of 0.84-1.0 with depth and gate count reduction by 20-86%.
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