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

arXiv: https://arxiv.org/abs/2607.20225 · HTML: https://mlsanity.com/paper/2607.20225/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models](https://mlsanity.com/paper/2005.07959/) (2020)
- [cuQuantum SDK: A High-Performance Library for Accelerating Quantum Science](https://mlsanity.com/paper/2308.01999/) (2023)
- [Multilevel Combinatorial Optimization Across Quantum Architectures](https://mlsanity.com/paper/1910.09985/) (2019)
- [QAOA-GPT: Efficient Generation of Adaptive and Regular Quantum Approximate Optimization Algorithm Circuits](https://mlsanity.com/paper/2504.16350/) (2025)
- [Quantum Annealing for Combinatorial Optimization: A Benchmarking Study](https://mlsanity.com/paper/2504.06201/) (2025)
- [Distributed Quantum Approximate Optimization Algorithm on a Quantum-Centric Supercomputing Architecture](https://mlsanity.com/paper/2407.20212/) (2024)
- [Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges](https://mlsanity.com/paper/2601.16816/) (2026)
- [GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems](https://mlsanity.com/paper/2506.10531/) (2025)
- [Distributed Quantum Optimization for Large-Scale Higher-Order Problems with Dense Interactions](https://mlsanity.com/paper/2604.20599/) (2026)

## Built on by (most cited first, 1 of 1)

- [Beyond QAOA: A Review of AI and Quantum Computing for Adaptive Combinatorial Optimization](https://mlsanity.com/paper/2610.11759/) (2026)

## Abstract (arXiv)

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.