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Selected applications of typicality to real-time dynamics of quantum many-body systems
Zeitschrift für Naturforschung A ( IF 1.8 ) Pub Date : 2020-05-26 , DOI: 10.1515/zna-2020-0010
Tjark Heitmann 1 , Jonas Richter 1 , Dennis Schubert 1 , Robin Steinigeweg 1
Affiliation  

Abstract Loosely speaking, the concept of quantum typicality refers to the fact that a single pure state can imitate the full statistical ensemble. This fact has given rise to a rather simple but remarkably useful numerical approach to simulate the dynamics of quantum many-body systems, called dynamical quantum typicality (DQT). In this paper, we give a brief overview of selected applications of DQT, where particular emphasis is given to questions on transport and thermalization in low-dimensional lattice systems like chains or ladders of interacting spins or fermions. For these systems, we discuss that DQT provides an efficient means to obtain time-dependent equilibrium correlation functions for comparatively large Hilbert-space dimensions and long time scales, allowing the quantitative extraction of transport coefficients within the framework of, e. g., linear response theory (LRT). Furthermore, it is discussed that DQT can also be used to study the far-from-equilibrium dynamics resulting from sudden quench scenarios, where the initial state is a thermal Gibbs state of the pre-quench Hamiltonian. Eventually, we summarize a few combinations of DQT with other approaches such as numerical linked cluster expansions or projection operator techniques. In this way, we demonstrate the versatility of DQT.

中文翻译:

典型性在量子多体系统实时动力学中的选定应用

摘要 广义上讲,量子典型性的概念是指单个纯态可以模仿完整的统计系综。这一事实催生了一种相当简单但非常有用的数值方法来模拟量子多体系统的动力学,称为动力学量子典型性 (DQT)。在本文中,我们简要概述了 DQT 的选定应用,其中特别强调了低维晶格系统(如相互作用的自旋或费米子的链或梯子)中的输运和热化问题。对于这些系统,我们讨论了 DQT 提供了一种有效的方法来获得相对较大的希尔伯特空间维度和较长的时间尺度的时间相关的平衡相关函数,允许在以下框架内定量提取传输系数,例如,线性响应理论(LRT)。此外,还讨论了 DQT 还可用于研究由突然淬火情景导致的远离平衡的动力学,其中初始状态是预淬火哈密顿量的热吉布斯状态。最后,我们总结了 DQT 与其他方法的一些组合,例如数值链接聚类扩展或投影算子技术。通过这种方式,我们展示了 DQT 的多功能性。我们总结了 DQT 与其他方法的一些组合,例如数值链接聚类扩展或投影算子技术。通过这种方式,我们展示了 DQT 的多功能性。我们总结了 DQT 与其他方法的一些组合,例如数值链接聚类扩展或投影算子技术。通过这种方式,我们展示了 DQT 的多功能性。
更新日期:2020-05-26
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