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arXiv 2303.01779cond-mat.stat-mech
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广义流体动力学中的可积杂质输运与纠缠

Transport and entanglement across integrable impurities from Generalized Hydrodynamics

Colin Rylands, Pasquale Calabrese

AI总结:

本文扩展广义流体动力学以纳入可积量子杂质模型,提出Bethe-Boltzmann型方程,研究二分淬火中的杂质散射对密度、电流及纠缠熵的影响。

摘要:

量子杂质模型(QIMs)在物理学中无处不在。作为简化的玩具模型,它们为理解更复杂的强关联系统提供了关键见解,同时它们本身也是对许多实验平台的准确描述。在平衡态下,它们的物理性质已被充分理解,并已成为当今使用的许多强大理论工具(无论是数值的还是解析的)的试验场。它们的非平衡物理性质则研究较少且理解不足。然而,最近通过广义流体动力学(GHD)的发展在非平衡可积量子系统中取得的进展,加上许多典型QIMs实际上可积的事实,为增强我们对这些系统的理解提供了一个诱人的机会。我们通过扩展GHD框架以纳入可积相互作用QIMs,朝这一方向迈出了一步。我们提出了一组Bethe-Boltzmann型方程,这些方程包含了杂质散射的影响,并讨论了包括熵产生在内的新方面。然后,这些杂质GHD方程被用于研究一个二分淬火,其中在二分位置处包含了一个相关的背散射杂质。研究了密度和电流分布作为杂质强度的函数,并推导了纠缠熵和全计数统计的表达式。

英文摘要:

Quantum impurity models (QIMs) are ubiquitous throughout physics. As simplified toy models they provide crucial insights for understanding more complicated strongly correlated systems, while in their own right are accurate descriptions of many experimental platforms. In equilibrium, their physics is well understood and have proven a testing ground for many powerful theoretical tools, both numerical and analytical, in use today. Their non-equilibrium physics is much less studied and understood. However, the recent advancements in non equilibrium integrable quantum systems through the development of generalized hydrodynamics (GHD) coupled with the fact that many archetypal QIMs are in fact integrable presents an enticing opportunity to enhance our understanding of these systems. We take a step towards this by expanding the framework of GHD to incorporate integrable interacting QIMs. We present a set of Bethe-Boltzmann type equations which incorporate the effects of impurity scattering and discuss the new aspects which include entropy production. These impurity GHD equations are then used to study a bipartioning quench wherein a relevant backscattering impurity is included at the location of the bipartition. The density and current profiles are studied as a function of the impurity strength and expressions for the entanglement entropy and full counting statistics are derived.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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