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科学家揭示基于噪声耗散系统中的经典-量子对应关系
作者:小柯机器人 发布时间:2024/12/28 21:38:02

近日,华南理工大学物理系的姚尧及其研究团队取得一项新进展。经过不懈努力,他们揭示了基于噪声耗散系统中的经典-量子对应关系。相关研究成果已于2024年12月26日在国际知名学术期刊《物理评论A》上发表。

该研究团队研究了经典噪声与量子环境之间的对应关系。尽管已知经典噪声可以在纯退相干和无限高温耗散过程中映射到量子环境,但研究人员通过引入辅助系统和守恒概念,展示了有限温度耗散也如何用经典噪声来描述。

以二能级系统为例,研究人员构建了所谓的中心自旋模型,其耦合随经典噪声波动,然后获得了该模型的统计平均动力学,该动力学捕捉到了超越无限高温的耗散行为。通过调整辅助系统的数量和它们的初始状态,基于噪声的模型能够复现马尔可夫和非马尔可夫演化。

研究人员还发现,二能级系统的不同量由不同的模型参数控制,这表明所构建的模型是特定可观测量的有效模拟器,而非现实开放系统的等价形式。此外,该模型还适用于研究开放系统的热点机制,例如负温度和不对称等距猝灭。

附:英文原文

Title: Classical-quantum correspondence in noise-based dissipative systems

Author:Jiarui Zeng1,2, Guo-Hao Xu2, Weijie Huang2, and Yao Yao2,3,*

Issue&Volume: 2024-12-26

Abstract: We investigate the correspondence between classical noise and quantum environments. Although it is known that classical noise can be mapped to quantum environments for pure dephasing and infinite-temperature dissipation processes, we show how finite-temperature dissipation can be also described using classical noise by introducing auxiliary systems and conservation. Taking a two-level system as an example, we construct the so-called central spin model with its couplings fluctuating with the classical noise and then acquire its statistical-average dynamics which captures the dissipation beyond infinite temperature. By adjusting the number of auxiliary systems and their initial states, the noise-based model reproduces both Markovian and non-Markovian evolution. We also find that different quantities of the two-level system are governed by different model parameters, indicating that the constructed model is an effective simulator for specific observables rather than an equivalent form of a realistic open system. In addition, the model is also applicable to investigate topical mechanisms of open systems, e.g., negative temperatures and asymmetric equidistant quenches.

DOI: 10.1103/PhysRevA.110.062219

Source: https://journals.aps.org/pra/abstract/10.1103/PhysRevA.110.062219

期刊信息

Physical Review A:《物理评论A》,创刊于1970年。隶属于美国物理学会,最新IF:2.97
官方网址:https://journals.aps.org/pra/
投稿链接:https://authors.aps.org/Submissions/login/new