DQAOA-GPT：用于组合问题的人工智能加速分布式量子优化
DQAOA-GPT: AI-Accelerated Distributed Quantum Optimization for Combinatorial Problems
- 1 National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- 2 Department of Physics \& Astronomy, University of Tennessee, Knoxville, TN, USA
- 3 NVIDIA Corporation, Santa Clara, CA, USA
- 4 IonQ Inc., Chattanooga, TN, USA
- 5 IonQ Inc., Seattle, WA, USA
- Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对组合优化问题求解难，介绍DQAOA-GPT混合框架，结合分布式量子近似优化算法与基于GPT的量子电路生成，直接为子问题生成高质量量子电路，对比传统DQAOA显著降低计算成本，为大规模组合优化提供基础。
AI中文摘要：
组合优化问题在许多科学和工程应用中至关重要，但因其搜索空间呈指数级增长，求解具有挑战性。变分量子算法为解决此类问题提供了途径，但其实际性能受限于量子电路重复评估和经典参数更新。本文介绍了DQAOA-GPT，这是一个混合框架，它将分布式量子近似优化算法（DQAOA，可将大优化问题分解为小问题）与基于GPT的量子电路生成相结合来解决子问题。该方法利用训练好的生成模型直接为分解后的子问题生成高质量量子电路。通过在多达100个决策变量的密集HUBO优化问题上与传统DQAOA对比评估，结果表明DQAOA-GPT显著降低计算成本且保持竞争力的解质量，子问题规模越大加速越明显。尽管本文聚焦基准规模验证，但该框架通过增加GPU资源和并行计算能力，为混合HPC-QC环境下的大规模组合优化提供了有前景的基础。
英文摘要：
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.
