We develop a variational approach to simulating the dynamics of open quantum many-body systems using deep autoregressive neural networks. The parameters of a compressed representation of a mixed quantum state are adapted dynamically according to the Lindblad master equation by employing a time-dependent variational principle. We illustrate our approach by solving the dissipative quantum Heisenberg model in one dimension for up to 40 spins and in two dimensions for a $4\times 4$ system and by applying it to the simulation of confinement dynamics in the presence of dissipation.

中文翻译：

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具有人工神经网络的开放量子系统的时间相关变分原理

我们开发了一种使用深度自回归神经网络模拟开放量子多体系统动力学的变分方法。混合量子态的压缩表示的参数根据 Lindblad 主方程通过采用时间相关变分原理进行动态调整。我们通过在一个维度上求解耗散量子海森堡模型来说明我们的方法，最多 40 次自旋，在二维中求解一个$4\times 4$ 系统并将其应用于存在耗散的约束动力学模拟。