In the study of closed many-body quantum systems, one is often interested in the evolution of a subset of degrees of freedom. On many occasions it is possible to approach the problem by performing an appropriate decomposition into a bath and a system. In the simplest case the evolution of the reduced state of the system is governed by a quantum master equation with a time-independent, i.e., Markovian, generator. Such evolution is typically emerging under the assumption of a weak coupling between the system and an infinitely large bath. Here we are interested in understanding to which extent a neural network function approximator can predict open quantum dynamics—described by time-local generators—from an underlying unitary dynamics. We investigate this question using a class of spin models, which is inspired by recent experimental setups. We find that indeed time-local generators can be learned. In certain situations they are even time independent and allow to extrapolate the dynamics to unseen times. This might be useful for situations in which experiments or numerical simulations do not allow to capture long-time dynamics and for exploring thermalization occurring in closed quantum systems.

中文翻译：

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开放式量子动力学的机器学习时域生成器

在封闭多体量子系统的研究中，人们经常对自由度子集的演化感兴趣。在许多情况下，可以通过将其适当分解为浴和系统来解决该问题。在最简单的情况下，系统的简化状态的演化是由具有时间无关的生成器（即马尔可夫生成器）的量子主方程控制的。这种演化通常是在系统与无限大镀液之间的弱耦合的假设下出现的。在这里，我们有兴趣了解神经网络功能逼近器可以在多大程度上根据基本的单一动力学来预测开放量子动力学（由时域生成器描述）。我们使用一类自旋模型来研究此问题，该模型受到最近实验设置的启发。我们发现确实可以学习时间本地生成器。在某些情况下，它们甚至与时间无关，并且可以将动态推断到看不见的时间。对于实验或数值模拟不允许捕获长时间动力学的情况以及探索封闭量子系统中发生的热化作用，这可能很有用。