近日，美国加州大学伯克利分校Chi-Fang Chen团队研究了高效量子热模拟。相关论文于2025年10月15日发表在《自然》杂志上。

量子计算机有望解决传统上难以解决的量子模拟问题。虽然现已开发了许多量子算法来模拟量子动力学，但一种通用的模拟低温量子现象的方法仍然未知。在经典设置中，从热分布中采样的模拟任务主要由马尔可夫链蒙特卡罗（MCMC）方法解决。

研究组提出了一种有效的量子热模拟算法，类似于MCMC方法，表现出详细的平衡，重视局部性，并作为开放量子系统中热化的玩具模型。MCMC方法的持久影响表明，它们的新结构可能在量子计算和物理科学及其他领域的应用中发挥同样重要的作用。

附：英文原文

Title: Efficient quantum thermal simulation

Author: Chen, Chi-Fang, Kastoryano, Michael, Brando, Fernando G. S. L., Gilyn, Andrs

Issue&Volume: 2025-10-15

Abstract: Quantum computers promise to tackle quantum simulation problems that are classically intractable1. Although a lot of quantum algorithms2,3,4 have been developed for simulating quantum dynamics, a general-purpose method for simulating low-temperature quantum phenomena remains unknown. In classical settings, the analogous task of sampling from thermal distributions has been largely addressed by Markov Chain Monte Carlo (MCMC) methods5,6. Here we propose an efficient quantum algorithm for thermal simulation that—akin to MCMC methods—exhibits detailed balance, respects locality and serves as a toy model for thermalization in open quantum systems. The enduring impact of MCMC methods suggests that our new construction may play an equally important part in quantum computing and applications in the physical sciences and beyond.

DOI: 10.1038/s41586-025-09583-x

Source: https://www.nature.com/articles/s41586-025-09583-x