论文标题
通过神经普通微分方程学习量子耗散
Learning quantum dissipation by the neural ordinary differential equation
论文作者
论文摘要
量子耗散是由量子系统及其周围环境之间不可避免的耦合引起的，这被称为信息的量子处理中的主要障碍。除了其存在之外，如何从观察数据中追踪耗散是一个至关重要的话题，它可能会刺激举止抑制耗散。在本文中，我们建议使用神经普通微分方程从动力学观测中学习量子耗散，然后在两个开放的量子旋转系统（一个大型旋转系统和一个spin-1/2链）上进行具体证明这种方法。我们还研究了数据集的学习效率，该数据集为实验中的数据获取提供了有用的指导。我们的工作有望促进开放量子系统中有效的建模和脱干抑制。
Quantum dissipation arises from the unavoidable coupling between a quantum system and its surrounding environment, which is known as a major obstacle in the quantum processing of information. Apart from its existence, how to trace the dissipation from observational data is a crucial topic that may stimulate manners to suppress the dissipation. In this paper, we propose to learn the quantum dissipation from dynamical observations using the neural ordinary differential equation, and then demonstrate this method concretely on two open quantum-spin systems -- a large spin system and a spin-1/2 chain. We also investigate the learning efficiency of the dataset, which provides useful guidance for data acquisition in experiments. Our work promisingly facilitates effective modeling and decoherence suppression in open quantum systems.
