近日，美国斯坦福大学Lindenberg, Aaron M.团队研究了非马尔可夫量子点的非平衡熵产生和信息耗散。相关论文于2026年2月9日发表在《自然—物理学》杂志上。

将系统从一种状态驱动至另一状态所需做的功，既包含平衡态自由能差，也包含与不可逆性相关的能量耗散。随着物理过程（如计算）不断趋近其速度极限，准确计算这部分超额耗散变得日益关键。然而，在强驱动、含时变化的真实纳米尺度系统中，如何精确定量耗散（更具体而言是熵产生）仍是重大挑战。因此，此前研究大多局限于两类系统：含时驱动下的理想化马尔可夫系统，或恒定驱动下的非马尔可夫稳态系统。

研究组通过含时驱动，在非稳态、非马尔可夫材料中测量了轨迹层级熵产生的完整动力学过程。他们利用机器学习方法，提取出量子点在阶跃式控制协议下随机闪烁所产生的熵。该熵产生对应于材料中载流子分布演化时记忆效应的丧失。此外，该方法能同时量化淬火控制协议下的信息注入与能量耗散。这项工作展示了一种直观有效的方法，可用于可视化快速淬火后的耗散动力学，并为优化真实材料与器件控制中的能量成本铺平了道路。

附：英文原文

Title: Non-equilibrium entropy production and information dissipation in a non-Markovian quantum dot

Author: Shen, Yuejun, Chen, Chutian, Ma, Haoran, Saunders, Ashley P., Heide, Christian, Liu, Fang, Rotskoff, Grant M., Shi, Jiaojian, Lindenberg, Aaron M.

Issue&Volume: 2026-02-09

Abstract: The work required to drive a system from one state to another comprises both the equilibrium free energy difference and the dissipation associated with irreversibility. As physical processes—such as computing—approach fast limits, calculating this excess dissipation becomes increasingly critical. Yet, precisely quantifying dissipation, more specifically, entropy production, in strongly driven, time-dependent, realistic nanoscale systems remains a considerable challenge. Consequently, previous studies have largely been limited to either idealized Markovian systems under time-dependent driving or non-Markovian steady-state systems under constant driving. Here we measure the full dynamics of trajectory-level entropy production in a non-stationary, non-Markovian material arising from time-dependent driving. We use machine learning to extract the entropy produced by a quantum dot stochastically blinking under a stepwise control protocol. The entropy produced corresponds to the loss of memory in the material as the carrier distribution evolves. In addition, our approach quantifies both information insertion and dissipation under a quenched protocol. This work demonstrates a simple and effective approach for visualizing dissipation dynamics following a fast quench and serves as a stepping stone towards optimizing energy costs in the control of real materials and devices.

DOI: 10.1038/s41567-026-03177-8

Source: https://www.nature.com/articles/s41567-026-03177-8