发表机构
JIJ Inc.; Rutherford Appleton Laboratory; Centre for Quantum Technologies, National University of Singapore（JIJ公司; 鲁瑟福·阿普尔顿实验室; 新加坡国立大学量子技术中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种图条件Transformer作为QAOA的内在优化器，直接嵌入混合量子-经典循环以预测参数更新，在MaxCut基准上验证了其可迁移性和有效性。
AI 中文摘要
量子近似优化算法（QAOA）是在含噪声中等规模量子硬件上进行组合优化的领先变分框架，但其实际性能在很大程度上取决于用于训练其变分参数的经典优化器。这种外层循环优化通常是非凸的、对初始化敏感的，并且在跨大型相关问题实例族重复执行时成本高昂。在这项工作中，我们提出了一种基于Transformer的QAOA内在优化框架，其中优化器本身被学习并直接嵌入到混合量子-经典循环中。所提出的图条件Transformer处理问题结构、当前QAOA参数、测量反馈和近期优化历史，以预测下一个变分参数更新，从而将逐实例的经典优化重构为摊销学习策略。我们开发了这种内在优化视角的数学表述，并在多种问题设置下基于QAOA的MaxCut基准上评估了该方法，与具有代表性的经典和学习的优化基线进行了比较。结果表明，基于Transformer的内在优化可以为改进混合量子优化算法的经典部分提供一种结构化的、可迁移的机制。
英文摘要
The Quantum Approximate Optimization Algorithm (QAOA) is a leading variational framework for combinatorial optimization on noisy intermediate-scale quantum hardware, but its practical performance depends strongly on the classical optimizer used to train its variational parameters. This outer-loop optimization is often nonconvex, initialization-sensitive, and costly when repeated across large families of related problem instances. In this work, we propose a Transformer-based intrinsic optimization framework for QAOA, in which the optimizer itself is learned and embedded directly into the hybrid quantum-classical loop. The proposed graph-conditioned Transformer processes problem structure, current QAOA parameters, measurement feedback, and recent optimization history to predict the next variational-parameter update, thereby reformulating instance-wise classical optimization as an amortized learned policy. We develop a mathematical formulation of this intrinsic-optimization perspective and evaluate the method on QAOA-based MaxCut benchmarks across multiple problem settings, with comparisons against representative classical and learned optimization baselines. The results demonstrate that Transformer-based intrinsic optimization can provide a structured and transferable mechanism for improving the classical component of hybrid quantum optimization algorithms.
