开放量子动力学的机器学习时间局域生成器
Machine learning time-local generators of open quantum dynamics
摘要：
在封闭多体量子系统的研究中，人们常常对自由度子集的演化感兴趣。在许多情况下，可以通过对浴和系统进行适当的分解来解决问题。在最简单的情况下，系统的约化态的演化由具有时间无关的（即马尔可夫）生成元的量子主方程控制。这种演化通常是在系统和无限大的浴池之间弱耦合的假设下出现的。在这里，我们有兴趣了解神经网络函数逼近器在多大程度上可以从底层的么正动力学预测开放量子动力学（由时间局域生成器描述）。我们调查这个问题，使用一类自旋模型，这是最近的实验设置的启发。我们发现，时间本地发电机确实可以学习。在某些情况下，它们甚至与时间无关，并允许将动态推断到看不见的时间。这可能是有用的情况下，实验或数值模拟不允许捕捉长时间的动力学和探索热化发生在封闭的量子系统。
英文摘要：
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.
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