发表机构
Institute for Theoretical Physics, University of Amsterdam; QuSoft, CWI; Toyota Motor Europe, Materials Engineering Division; Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit（阿姆斯特丹大学理论物理研究所; QuSoft，荷兰数学与计算机科学研究中心; 丰田欧洲汽车公司材料工程部; 自由大学化学与药学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究复杂化学系统多尺度建模中量子算法跨尺度组合问题，通过映射不同量子算法到各物理领域，探讨保持量子优势条件及相关问题，指出多尺度量子优势由算法层间信息传递结构主导。
AI 中文摘要
复杂化学系统的多尺度建模需要能在电子、原子、介观和连续介质尺度上连贯运行的算法。虽然已针对各尺度提出量子算法，但缺乏跨尺度边界组合这些算法的系统框架。本文确定了容错量子算法可能保持尺度特定量子优势的条件。将量子相位估计、吉布斯态制备的哈密顿量模拟、量子随机游走和量子偏微分方程求解器分别映射到电子结构、分子动力学、介观动力学和连续介质反应器物理上。关键是，这些对应关系并不意味着无条件的端到端量子优势；加速严重依赖于态制备、内存架构、矩阵条件和经典读出成本。通过\ce{CO}在\ce{Pt(111)}上氧化的量子层次结构说明了六个未解决的问题定义了这个组合问题。我们建议将跨尺度转移视为算法设计和非平衡统计力学界面处的量子通道组合问题，并询问尺度边界处的信息损失是多尺度建模固有的，还是仅仅是算法层之间有损经典转换的结果。由此产生的路线图表明，多尺度量子优势主要由算法层之间的信息传递结构决定，而不仅仅取决于单个尺度的性能。
英文摘要
Multiscale modeling of complex chemical systems requires algorithms that operate coherently across electronic, atomistic, mesoscopic, and continuum scales. While quantum algorithms have been proposed for each regime, no systematic framework exists to compose them across scale boundaries. Here, we identify the conditions under which fault-tolerant quantum algorithms might preserve scale-specific quantum advantages. We map quantum phase estimation, Hamiltonian simulation with Gibbs state preparation, quantum random walks, and quantum partial differential equation solvers onto electronic structure, molecular dynamics, mesoscopic kinetics, and continuum reactor physics, respectively. Crucially, these correspondences do not imply unconditional end-to-end quantum advantage; speedups depend heavily on state preparation, memory architectures, matrix conditioning, and classical readout costs. Six unresolved questions define this composition problem, illustrated via a quantum hierarchy for \ce{CO} oxidation over \ce{Pt(111)}. We propose viewing inter-scale transfer as a quantum channel composition problem at the interface of algorithm design and non-equilibrium statistical mechanics, and ask whether information loss at scale boundaries is intrinsic to multiscale modeling or merely a consequence of lossy classical transduction between algorithmic layers. The resulting roadmap suggests that multiscale quantum advantage is governed primarily by the structure of information transfer between algorithmic layers, rather than by performance at individual scales alone.
Comments37 pages, 1 figure
