这篇论文把GPT和量子优化搭在一起，搞了个DQAOA-GPT框架，算大型组合问题比传统方法快很多，成本也低，值得看看思路。
DQAOA-GPT是一种混合框架，将分布式量子近似优化算法(DQAOA)与GPT生成模型结合，用于求解组合优化问题。该方法将大规模优化问题分解为子问题，用训练好的生成模型直接产出高质量量子电路，避免传统迭代优化。在多达100个决策变量的密集HUBO问题上，DQAOA-GPT相比常规DQAOA显著降低计算成本，同时保持接近的解质量。子问题规模越大，加速效果越明显。
DQAOA-GPT: AI-Accelerated Distributed Quantum Optimization for Combinatorial Problems
While combinatorial optimization problems are central to many scientific and engineering applications, their solution remains challenging due to exponentially large search spaces. Variational quantum algorithms offer a promising route for tackling such problems, yet their practical performance is limited by repeated quantum circuit evaluations and classical parameter updates. In this work, we introduce DQAOA-GPT, a hybrid framework that integrates the distributed quantum approximate optimization algorithm (DQAOA), which decomposes a large optimization problem into smaller sub-problems, with GPT-based quantum circuit generation for solving those sub-problems. Rather than relying on iterative variational optimization, the proposed approach uses a trained generative model to directly generate high-quality quantum circuits for the decomposed sub-problems. As a benchmark, we evaluate DQAOA-GPT against conventional DQAOA on dense HUBO optimization problems with up to 100 decision variables. The results demonstrate that DQAOA-GPT significantly reduces computational cost while maintaining competitive solution quality, with larger acceleration observed for larger sub-problem sizes. Although this work focuses on benchmark-scale validation, the framework provides a promising foundation for larger-scale combinatorial optimization in hybrid HPC-QC environments through increased GPU resources and parallel computing capability.