发表机构
Pacific Northwest National Laboratory; Harvard University; Massachusetts Institute of Technology; Argonne National Laboratory; University of Oxford（太平洋西北国家实验室; 哈佛大学; 麻省理工学院; 阿贡国家实验室; 牛津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出随机编译量子蒙特卡洛（RC-QMC）框架，通过对近似族平均抑制系统误差，在热态估计和开放系统动力学模拟中相比标准方法实现计算优势。
AI 中文摘要
量子蒙特卡洛（QMC）算法是模拟量子系统最强大的经典方法之一，然而其精度常常受到所用近似（如Trotter化）中系统误差的限制。在此，我们引入随机编译量子蒙特卡洛（RC-QMC）作为一个通用框架，通过对一族近似进行平均而非依赖单一固定近似来抑制这些系统误差。该策略基于量子计算中的随机编译概念，后者通过对量子门进行采样来抑制误差，且基本不增加额外计算成本。因此，在将目标态估计到所需精度水平时，我们的框架相比标准QMC方法实现了计算优势。我们在两个关键的蒙特卡洛算法上展示了这一优势：（1）用于估计热态的路径积分量子蒙特卡洛方法，以及（2）用于模拟开放系统动力学的量子轨迹方法。总体而言，这些结果代表了量子与经典算法的交叉融合，易于推广到其他QMC方法，并暗示了在经典模拟中更广泛的应用。
英文摘要
Quantum Monte Carlo (QMC) algorithms are among the most powerful classical methods for simulating quantum systems, yet their accuracy is often limited by the systematic errors in the approximations used, such as Trotterization. Here we introduce randomly compiled quantum Monte Carlo (RC-QMC) as a general framework that suppresses these systematic errors by averaging over a family of approximations rather than relying on a single fixed one. This strategy is grounded in the concept of randomized compiling from quantum computing, which suppresses errors by sampling over quantum gates, at essentially no additional computational cost. Consequently, our framework achieves a computational advantage over standard QMC methods when estimating a target state to a desired level of accuracy. We illustrate this advantage on two key Monte Carlo algorithms: (1) path integral quantum Monte Carlo for estimating thermal states, and (2) the quantum trajectories method for simulating open system dynamics. In aggregate, these results represent a cross-fertilization of quantum and classical algorithms, are readily generalizable to other QMC methods, and suggest wider applications in classical simulation.
