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arXiv 2602.09054quant-phcond-mat.stat-mech
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经典非马尔可夫动力学中由记忆诱导的可分性丧失引起的相对熵复兴

Relative Entropy Revivals Caused by Memory-Induced Loss of Divisibility in Classical Non-Markovian Dynamics

Hoshi University(星药科大学)

Koichi Nakagawa

AI总结:

本文研究经典随机弛豫中相对熵超调的最小动力学机制,发现记忆效应通过产生瞬态负有效速率破坏随机可分性,导致熵超调,并用两态和三态模型展示了相结构。

摘要:

相对熵复兴和负熵产生率是非马尔可夫动力学中记忆效应的标志性特征。本文探讨一个更具体的问题:经典随机弛豫中产生熵超调的最小动力学机制是什么?我们将熵超调定义为在最终回归平衡的弛豫过程中,相对熵相对于平稳分布的瞬态增加。从具有记忆核的有限态广义主方程出发,我们分析了控制观测动力学的有效时间局域生成元。回顾了标准马尔可夫动力学中相对熵的单调衰减作为参考性质。然后我们证明,记忆可以通过产生瞬态负有效速率来破坏随机可分性。一旦演化不再能分解为中间马尔可夫步骤,通常的相对熵收缩论证不再适用,熵超调就可能发生。因此,本文的贡献不在于引入另一种非马尔可夫性的见证,而在于识别出最小的结构机制及其相结构。通过两态和三态随机模型展示了该机制,其中内在弛豫时间和记忆时间之间的竞争将单调弛豫与超调分开。数值相图表明,当记忆在弛豫时间尺度上持续时,超调区域扩大。这些结果澄清了经典信息回流、负熵率行为和记忆诱导的可分性丧失之间的关系。它们也将本文基于机制的解释与先前主要将相对熵复兴或熵产生率作为非马尔可夫性指标的工作区分开来。

英文摘要:

Relative-entropy revivals and negative entropy-production rates are established signatures of memory effects in non-Markovian dynamics. The present paper addresses a more specific question: what minimal dynamical mechanism produces entropy overshoot in classical stochastic relaxation? We define entropy overshoot as a transient increase of the Kullback--Leibler divergence from a stationary distribution during a relaxation process that ultimately returns to equilibrium. Starting from finite-state generalized master equations with memory kernels, we analyze the effective time-local generator governing the observed dynamics. The standard monotonic decay of relative entropy is recalled as a reference property of reversible Markov dynamics. We then show that memory can destroy stochastic divisibility by producing transiently negative effective rates. Once the evolution ceases to be divisible into intermediate Markov steps, the usual relative-entropy contraction argument no longer applies, and entropy overshoot can occur. The contribution of the paper is therefore not the introduction of another witness of non-Markovianity, but the identification of a minimal structural mechanism and its phase structure. The mechanism is illustrated with two- and three-state stochastic models, where the competition between the intrinsic relaxation time and the memory time separates monotonic relaxation from overshoot. Numerical phase diagrams show that the overshoot region expands when memory persists on the relaxation timescale. These results clarify the relation between classical information backflow, negative entropy-rate behavior, and memory-induced loss of divisibility. They also distinguish the present mechanism-based interpretation from previous work that used relative-entropy revivals or entropy-production rates primarily as indicators of non-Markovianity.

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