AI 中文总结
本综述介绍了用于解决开放量子系统非微扰动力学计算难题的张量网络方法，阐述了其异同以全面呈现该领域面貌。
AI 中文摘要
超越微扰处理（通常与马尔可夫主方程相关）的开放量子系统动力学描述是一项计算上具有挑战性的任务，原因在于记忆核存在不利的指数级缩放问题。近几十年来，在量子信息和凝聚态物理领域发展起来的张量网络，为克服以往的计算瓶颈提供了新的形式体系和工具集。该框架能够构建非微扰、数值精确的方法，以可控的数值精度描述开放量子系统的动力学。在本综述中，我们介绍这些方法并讨论它们的异同，以全面呈现该领域的面貌。
英文摘要
The description of open quantum system dynamics beyond the perturbative treatment (usually associated with Markovian master equations) is a computationally challenging task due to the unfavorable exponential scaling of memory kernels. Developed over recent decades in the context of quantum information and condensed matter, tensor networks provide both a new formalism and a toolbox for overcoming previous computational bottlenecks. This framework enables the formulation of non-perturbative, numerically exact methods for describing the dynamics of open quantum systems to controllable numerical accuracy. In this review, we present these methods and discuss their commonalities and differences to paint a comprehensive view of the field.
