发表机构
School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述梳理AI与量子计算在自适应组合优化中的三类范式，发现AI辅助量子优化证据更强，量子改进学习型优化器多局限于小规模模拟，最高层级尚无令人信服的结果，强调需合理分配经典与量子资源。
AI 中文摘要
近期量子组合优化方法受限于量子比特数量、电路保真度、采样成本以及约束编码的难度，而机器学习正越来越多地被用于配置和控制量子优化工作流。我们将此类工作流称为自适应工作流：从问题表述、惩罚项设置到采样预算、后端选择，乃至是否调用量子处理器，这些通常预先固定的决策，现在由学习得到的策略做出，这些策略会根据问题实例、求解进展或硬件状态进行调整。本综述考察了三大范式：人工智能用于量子优化、量子用于人工智能驱动的优化、以及人工智能-量子协同优化，并按所学习的决策而非应用领域来组织文献。对119篇论文的结构化综述（其中67篇进行了详细编码）显示，人工智能辅助量子优化的证据远强于反向方向：学习已可减少量子评估次数、改进初始化、支持分解与惩罚控制，并缓解噪声影响；而量子计算可改进学习型优化器的证据，目前大多仍局限于小规模模拟。实验控制较为薄弱：在57项研究中，有25项未设置经典基线；在应用了消融研究的24项研究中，有10项完全隔离了量子贡献；中位实验使用了17个量子比特。我们引入了M0-M5证据层级，从模拟到匹配资源下的实际优势，发现在最高层级尚无广泛令人信服的结果。我们认为，规模化正日益成为一个系统问题：问题不仅在于是否能在量子处理器上运行，还在于如何在优化过程中分配经典与量子资源。本综述面向量子计算、机器学习与运筹学领域的研究者。
英文摘要
Near-term quantum approaches to combinatorial optimization are limited by qubit counts, circuit fidelity, sampling cost, and the difficulty of encoding constraints, while machine learning is increasingly used to configure and control quantum optimization workflows. We call such workflows adaptive: decisions conventionally fixed in advance, from formulation and penalties to shot budgets, backends, and whether to invoke a quantum processor at all, are made by learned policies that respond to the instance, the progress of the solve, or the hardware. This review examines three paradigms, AI for quantum optimization, quantum for AI-driven optimization, and AI-quantum co-optimization, and organizes the literature by the decision being learned rather than by application. A structured review of 119 papers, 67 coded in detail, shows that the evidence is considerably stronger for AI-assisted quantum optimization than for the reverse direction: learning already reduces quantum evaluations, improves initialization, supports decomposition and penalty control, and mitigates noise, whereas evidence that quantum computation improves learned optimizers remains largely confined to small-scale simulation. Experimental controls are thin: 25 of 57 studies include no classical baseline, the quantum contribution is fully isolated in 10 of 24 studies where an ablation applies, and the median experiment uses 17 qubits. We introduce an M0-M5 evidence hierarchy, from simulation to matched-resource practical advantage, and find no broadly convincing result at the highest level. We argue that scaling is increasingly a systems problem: the question is not only whether a problem fits on a quantum processor, but how classical and quantum resources should be allocated across the optimization process. The review is aimed at researchers in quantum computing, machine learning, and operations research.
Comments34 pages, 5 figures, 7 tables. Review article. Supplementary evidence matrix (coded data for all 119 papers) included as an ancillary file
