Simulating the dynamics of open quantum systems coupled to non-Markovian
environments remains an outstanding challenge due to exponentially scaling
computational costs. We present an artificial intelligence strategy to overcome
this obstacle by integrating the neural quantum states approach into the
dissipaton-embedded quantum master equation in second quantization (DQME-SQ).
Our approach utilizes restricted Boltzmann machines (RBMs) to compactly
represent the reduced density tensor, explicitly encoding the combined effects
of system-environment correlations and nonMarkovian memory. Applied to model
systems exhibiting prominent effects of system-environment correlation and
non-Markovian memory, our approach achieves comparable accuracy to conventional
hierarchical equations of motion, while requiring significantly fewer dynamical
variables. The novel RBM-based DQME-SQ approach paves the way for investigating
non-Markovian open quantum dynamics in previously intractable regimes, with
implications spanning various frontiers of modern science.

利用神经量子态的方法，我们提供了一种人工智能策略来模拟耦合非马尔可夫环境的开放量子系统的动力学，通过将神经量子态方法与含衰减耦合的二次量化量子主方程（DQME-SQ）结合起来，我们能够在显著减少动力学变量的同时实现与传统的层次方程相当的精度，为研究以前难以处理的非马尔可夫开放量子动力学在各个现代科学前沿领域提供了新的思路。