arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~

过去的冗余记录:统一量子达尔文主义与退相干历史

Redundant Records of the Past: Unifying Quantum Darwinism and Decoherent Histories

Philipp Strasberg

arXiv 2610.09845首次发表:更新:

发表机构

Instituto de Física de Cantabria (IFCA), Universidad de Cantabria–CSIC(坎塔布里亚物理研究所(IFCA),坎塔布里亚大学–西班牙国家研究委员会)

机构由 AI 辅助整理,请以论文原文为准。

AI 中文总结

本文统一量子达尔文主义与退相干历史,提出“过去冗余”框架,揭示冗余可由多种机制产生,并强调其对客观性的必要非充分性。

AI 中文摘要

在孤立量子系统中,存在两种不同的记录范式:退相干历史(决定哪些过去事件留下正式记录)和量子达尔文主义(决定哪些当前事件被冗余记录)。两者互不蕴含,且它们之间的联系鲜有研究。我们基于“只有关于过去事件的冗余记录才重要”这一洞见,统一了量子达尔文主义与退相干历史。所得到的“过去冗余”框架,在应用于当前时刻的事件时涵盖了量子达尔文主义,并通过 Bhattacharyya 系数(或保真度)将冗余与历史的退相干及概率求和规则联系起来。重要的是,多个例子表明,过去冗余是一种比量子达尔文主义所需的微调模型更为普遍的现象。因此,我们得到了一个更丰富的图景:冗余可由不同于量子达尔文主义的机制(如宏观事件或级联结构)引起,这强调了冗余是客观性的必要条件但非充分条件,并且总体上使得我们能够现实地研究这样一个问题:冗余在自然界中有多普遍?

英文摘要

There exist two different paradigms for records in isolated quantum systems: decoherent histories (which determines which past events leave formal records) and quantum Darwinism (which determines which current events get redundantly recorded). Neither one implies the other, and connections between the two are scarcely investigated. We unify quantum Darwinism and decoherent histories based on the insight that only redundant records about past events matter. The resulting framework of past redundancy encompasses quantum Darwinism when applied to events at the current time, and it connects redundancy to decoherence of histories and the probability sum rule via the Bhattacharyya coefficient (or fidelity). Importantly, various examples show that past redundancy is a more widespread phenomenon than the fine-tuned models necessary for quantum Darwinism suggest. We thus arrive at a richer picture in which redundancy can be caused by mechanisms different from quantum Darwinism (such as macroevents or cascade structures), which emphasizes that redundancy is necessary but not sufficient for objectivity, and which, overall, allows to realistically investigate the question: how generic is redundancy in nature?

CommentsI do not permit the use of this work for AI training without my explicit consent. 21 pages in total incl. 10 figures, 99 references, and 2.5 pages appendix. Comments are welcome!

论文原文

arXiv 摘要页 · PDF 原文 · HTML 原文

↑