基于机器学习的氮-空位中心非马尔可夫动力学表征
Machine Learning-Based Characterisation of the Non-Markovian Dynamics of a Nitrogen-Vacancy Centre
AI总结：
本研究首次通过实验展示基于机器学习从NV中心拉比动力学重构反应坐标谱密度参数，其性能经基准测试可可靠复现NV动力学，部分参数估计方差与最大似然估计相当。
AI中文摘要：
量子系统与环境的相互作用可通过谱密度函数表征，了解其结构对优化量子传感协议等量子技术应用至关重要。本研究首次实验演示了基于机器学习从氮-空位（NV）中心拉比动力学中重构反应坐标谱密度参数的方法。与以往工作不同，本研究恢复了所有谱密度参数而非仅中心频率，并将神经网络的性能与克拉美罗界（Cramér-Rao bound）及最大似然估计器（maximum likelihood estimator）进行基准测试。结果表明，神经网络预测的模型可在估计窗口内可靠复现NV动力学，且部分参数的估计方差可与最大似然估计的方差相媲美。
英文摘要：
The interaction between a quantum system and its environment can be characterized by the spectral density function: knowing its structure is important for optimizing applications of quantum technologies such as quantum sensing protocols. In this work, we present the first experimental demonstration of a machine learning-based reconstruction of reaction-coordinate spectral density parameters from NV centre Rabi dynamics. Unlike the previous work, we recover all spectral density parameters rather than only the central frequency, and benchmark the performance of the neural network against the Cramér-Rao bound and maximum likelihood estimator. Our results demonstrate that the model predicted by the neural network can reliably reproduce the NV dynamics over the estimation window, and can produce estimates for some parameters with variances comparable to that of maximum likelihood.
