近日，瑞士苏黎世联邦理工学院Johannes Christmann团队研究了从量子增强到量子启发的蒙特卡罗。这一研究成果发表在2025年4月16日出版的《物理评论A》杂志上。

研究组对量子增强蒙特卡罗方法[Nature（London）619822（2023）]进行了全面分析，旨在确定算法的最佳工作点。他们观察到一个最优的混合哈密顿强度，并分析了总演化时间随系统大小的缩放。还探索了电路的扩展，包括使用含时哈密顿量和反向数字化退火。

此外，研究组建议在提案步骤中使用经典的近似量子模拟器，而不是原始的真实硬件实现。他们观察到，张量网络模拟器，即使在非收敛设置下，也可以保持比标准经典采样器更大的缩放优势。这可能会扩展量子增强蒙特卡罗作为量子启发算法的效用，甚至在大规模量子硬件部署之前。

附：英文原文

Title: From quantum-enhanced to quantum-inspired Monte Carlo

Author: Johannes Christmann, Petr Ivashkov, Mattia Chiurco, Guglielmo Mazzola

Issue&Volume: 2025/04/16

Abstract: We perform a comprehensive analysis of the quantum-enhanced Monte Carlo method [Nature (London) 619, 282 (2023)], aimed at identifying the optimal working point of the algorithm. We observe an optimal mixing Hamiltonian strength and analyze the scaling of the total evolution time with the size of the system. We also explore extensions of the circuit, including the use of time-dependent Hamiltonians and reverse digitized annealing. Additionally, we propose that classical, approximate quantum simulators can be used for the proposal step instead of the original real-hardware implementation. We observe that tensor-network simulators, even with unconverged settings, can maintain a scaling advantage over standard classical samplers. This may extend the utility of quantum-enhanced Monte Carlo as a quantum-inspired algorithm, even before the deployment of large-scale quantum hardware.

DOI: 10.1103/PhysRevA.111.042615

Source: https://journals.aps.org/pra/abstract/10.1103/PhysRevA.111.042615