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arXiv 2302.04883quant-ph

纯非马尔可夫演化

Pure non-Markovian evolutions

Dario De Santis

AI总结:

本文提出纯非马尔可夫演化概念,将初始噪声区分为非必要噪声与必要噪声,并证明含噪非马尔可夫动力学可由 PNM 核心经马尔可夫预处理模拟。

摘要:

非马尔可夫动力学的特征是信息回流,即演化中的开放量子系统会取回先前流失到环境中的部分信息。因此,非马尔可夫性的定义本身就意味着演化在初始时间段内存在噪声,否则回流便不可能发生。我们识别出两类初始噪声:第一类仅会降低系统的信息含量,而第二类对于非马尔可夫现象的出现必不可少。因此,所有非马尔可夫演化都可分为两类:同时表现出两类噪声的含噪非马尔可夫(noisy non-Markovian,NNM)演化,以及只实现必要噪声的纯非马尔可夫(pure non-Markovian,PNM)演化。我们通过对基本非马尔可夫特征进行时间分析来作出这一区分。首先,我们证明所有 NNM 动力学都可以通过对一个 PNM 核心进行马尔可夫预处理来模拟。我们量化了 PNM 演化在信息回流和非马尔可夫性度量方面带来的增益。同样,我们研究了纠缠破缺性质在该框架中的表现,并讨论了一种激活关联回流的技术。最后,我们通过研究若干著名动力学模型,展示了所得结果的适用性。

英文摘要:

Non-Markovian dynamics are characterized by information backflows, where the evolving open quantum system retrieves part of the information previously lost in the environment. Hence, the very definition of non-Markovianity implies an initial time interval when the evolution is noisy, otherwise no backflow could take place. We identify two types of initial noise, where the first has the only effect of degrading the information content of the system, while the latter is essential for the appearance of non-Markovian phenomena. Therefore, all non-Markovian evolutions can be divided into two classes: noisy non-Markovian (NNM), showing both types of noise, and pure non-Markovian (PNM), implementing solely essential noise. We make this distinction through a timing analysis of fundamental non-Markovian features. First, we prove that all NNM dynamics can be simulated through a Markovian pre-processing of a PNM core. We quantify the gains in terms of information backflows and non-Markovianity measures provided by PNM evolutions. Similarly, we study how the entanglement breaking property behaves in this framework and we discuss a technique to activate correlation backflows. Finally, we show the applicability of our results through the study of several well-know dynamical models.

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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