AI 中文总结
研究三能级Δ系统中面元相位的量子传感，采用在监督学习框架中训练的多层感知器，依据不同驱动条件下STIRAP布居转移效率来准确估计面元相位，展示了相干控制与机器学习结合在量子传感中的作用。
AI 中文摘要
我们提出了一种机器学习赋能的方法用于面元相位的量子传感，面元相位是三能级Δ系统中出现的一个规范不变量。该相位深刻影响系统动力学，打破相干布居俘获并引发动力学的非平凡相位依赖性。我们证明，在监督学习框架中训练的多层感知器（MLP），能根据在不同驱动条件下测量的受激拉曼绝热通道（STIRAP）布居转移效率准确估计面元相位，这些效率是实验可获取的可观测量。我们的结果突出了相干控制与机器学习的结合如何在闭环量子系统中实现有效的相位识别，为量子技术，特别是包括合成规范场的量子传感应用开辟了新前景。
英文摘要
We propose a machine-learning-empowered approach to the quantum sensing of the plaquette phase, a gauge-invariant quantity arising in three-level $Δ$ systems. This phase profoundly affects the system dynamics, breaking coherent population trapping and inducing a non-trivial phase dependence of the dynamics. We demonstrate that a multi-layer perceptron (MLP), trained in a supervised-learning framework, can accurately estimate the plaquette phase from STImulated Raman Adiabatic Passage (STIRAP) population transfer efficiencies measured under different driving conditions, which provide experimentally accessible observables. Our results highlight how the combination of coherent control and machine learning (ML) enables effective phase identification in closed-loop quantum systems, opening new perspectives for quantum technologies, specifically quantum sensing applications including synthetic gauge fields.
Comments16 pages, 4 figures, 2 tables
