论文标题
通过闭环学习控制对量子计量的探针优化
Probe optimization for quantum metrology via closed-loop learning control
论文作者
论文摘要
在实验上实现了标准量子计量方案承诺总是具有挑战性的精确度。最近,将其他控件应用于设计可行的量子计量方案。但是,这些方法通常不会考虑易于实施,从而提高了技术障碍,从而阻碍了其实现。在本文中,我们通过应用闭环学习控制来规避此问题,以提出一种用于量子计量学的实用控制的顺序方案。与量子Fisher信息有关的探针状态的纯度损失有效地测量了指导学习循环的适应性。我们通过数值分析和核磁共振(NMR)系统中的数值分析和原理实验来证实其可行性和比标准量子计量方案的某些优势。
Experimentally achieving the precision that standard quantum metrology schemes promise is always challenging. Recently, additional controls were applied to design feasible quantum metrology schemes. However, these approaches generally does not consider ease of implementation, raising technological barriers impeding its realization. In this paper, we circumvent this problem by applying closed-loop learning control to propose a practical controlled sequential scheme for quantum metrology. Purity loss of the probe state, which relates to quantum Fisher information, is measured efficiently as the fitness to guide the learning loop. We confirm its feasibility and certain superiorities over standard quantum metrology schemes by numerical analysis and proof-of-principle experiments in a nuclear magnetic resonance (NMR) system.