论文标题
学习动态系统：开放量子系统动态的示例
Learning dynamical systems: an example from open quantum system dynamics
论文作者
论文摘要
旨在从数据中学习动态系统的机器学习算法可用于预测，控制和解释观察到的动态。在这项工作中，我们说明了在开放量子系统动力学的背景下，使用一种算法，即Koopman操作员学习。我们将研究一个小型自旋链的动力学，再加上倾斜的大门，并展示Koopman操作员学习如何有效地学习密度矩阵的演变，而且还可以学习与系统相关的每个物理观察的方法。最后，通过利用学识渊博的Koopman运算符的光谱分解，我们展示了如何直接从数据中推断出基础动力学的对称性。
Machine learning algorithms designed to learn dynamical systems from data can be used to forecast, control and interpret the observed dynamics. In this work we exemplify the use of one of such algorithms, namely Koopman operator learning, in the context of open quantum system dynamics. We will study the dynamics of a small spin chain coupled with dephasing gates and show how Koopman operator learning is an approach to efficiently learn not only the evolution of the density matrix, but also of every physical observable associated to the system. Finally, leveraging the spectral decomposition of the learned Koopman operator, we show how symmetries obeyed by the underlying dynamics can be inferred directly from data.
