Kirchhoff-Institut für Physik, Universität Heidelberg, Im Neuenheimer Feld 227, 69120 Heidelberg, Germany.
Institut für Theoretische Physik, Universität zu Köln, 50937 Köln, Germany.
Phys Rev Lett. 2021 Dec 3;127(23):230501. doi: 10.1103/PhysRevLett.127.230501.
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 4×4 system and by applying it to the simulation of confinement dynamics in the presence of dissipation.
我们开发了一种变分方法，用于使用深度自回归神经网络模拟开放量子多体系统的动力学。通过采用时变变分原理，根据林德布拉德主方程动态调整混合量子态的压缩表示的参数。我们通过求解一维多达 40 个自旋的耗散量子海森堡模型和二维 4×4 系统来说明我们的方法，并将其应用于存在耗散时的限制动力学的模拟。
