一句话：提出 MarQSim 编译框架，用马尔可夫链与最小费用流统一量子哈密顿模拟编译中的确定性与随机性优化。
量子哈密顿量模拟是量子算法设计的基础，但其编译优化面临挑战：现有方法难以协调确定性与随机化编译，缺乏合适中间表示，且难以保证正确性。本文提出 MarQSim 编译框架，利用基于马尔可夫链的 Hamiltonian Term Transition Graph 同时获得确定性与随机化编译的好处，并构建最小费用流模型来调节转移矩阵：在强制正确性的同时适配不同优化目标。实验表明，对于多种量子哈密顿量，MarQSim 能生成更高效的量子电路，并保持模拟精度。
Quantum Hamiltonian simulation, fundamental in quantum algorithm design, extends far beyond its foundational roots, powering diverse quantum computing applications. However, optimizing the compilation of quantum Hamiltonian simulation poses significant challenges. Existing approaches fall short in reconciling deterministic and randomized compilation, lack appropriate intermediate representations, and struggle to guarantee correctness. Addressing these challenges, we present MarQSim, a novel compilation framework. MarQSim leverages a Markov chain-based approach, encapsulated in the Hamiltonian Term Transition Graph, adeptly reconciling deterministic and randomized compilation benefits. Furthermore, we formulate a Minimum-Cost Flow model that can tune transition matrices to enforce correctness while accommodating various optimization objectives. Experimental results demonstrate MarQSim’s superiority in generating more efficient quantum circuits for simulating various quantum Hamiltonians while maintaining precision.
@article{paperbot3146,
title = {MarQSim: Reconciling Determinism and Randomness in Compiler Optimization for Quantum Simulation},
author = {Xiuqi Cao and Junyu Zhou and Yuhao Liu and Yunong Shi and Gushu Li},
journal = {Proceedings of the ACM on Programming Languages},
volume = {9},
number = {PLDI},
year = {2025},
doi = {10.1145/3729269}
}