DQAOA-GPT: AI-Accelerated Distributed Quantum Optimization for Combinatorial Problems

• URL: http://arxiv.org/abs/2607.20225v1
• Date: Wed, 22 Jul 2026 14:46:33 GMT
• Title: DQAOA-GPT: AI-Accelerated Distributed Quantum Optimization for Combinatorial Problems
• Abstract summary: Variational quantum algorithms offer a promising route for tackling exponentially large search spaces.<n>In this work, we introduce DQAOA-GPT, a hybrid framework that integrates the distributed quantum approximate optimization algorithm (DQAOA)<n>Rather than relying on iterative variational optimization, the proposed approach uses a trained generative model to directly generate high-quality quantum circuits for the sub-problems.
• Score: 1.0380578526859343
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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