# QAOA-GPT: Efficient Generation of Adaptive and Regular Quantum Approximate Optimization Algorithm Circuits

arXiv: https://arxiv.org/abs/2504.16350 · HTML: https://mlsanity.com/paper/2504.16350/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [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)
- [Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers](https://mlsanity.com/paper/2102.05566/) (2021)
- [Multilevel Combinatorial Optimization Across Quantum Architectures](https://mlsanity.com/paper/1910.09985/) (2019)
- [GroverGPT: A Large Language Model with 8 Billion Parameters for Quantum Searching](https://mlsanity.com/paper/2501.00135/) (2024)
- [Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver](https://mlsanity.com/paper/2501.16986/) (2025)
- [Classical symmetries and the Quantum Approximate Optimization Algorithm](https://mlsanity.com/paper/2012.04713/) (2020)
- [Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach](https://mlsanity.com/paper/2504.10733/) (2025)
- [MLQAOA: Graph Learning Accelerated Hybrid Quantum-Classical Multilevel QAOA](https://mlsanity.com/paper/2404.14399/) (2024)

## Built on by (most cited first, 8 of 8)

- [QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL](https://mlsanity.com/paper/2510.00967/) (2025)
- [Generative Quantum-inspired Kolmogorov-Arnold Eigensolver](https://mlsanity.com/paper/2605.04604/) (2026)
- [Symbolic Analysis of Grover Search Algorithm via Chain-of-Thought Reasoning and Quantum-Native Tokenization](https://mlsanity.com/paper/2505.04880/) (2025)
- [QAOA Parameter Transferability for Maximum Independent Set using Graph Attention Networks](https://mlsanity.com/paper/2504.21135/) (2025)
- [DQAOA-GPT: AI-Accelerated Distributed Quantum Optimization for Combinatorial Problems](https://mlsanity.com/paper/2607.20225/) (2026)
- [SCALAR: A Neurosymbolic Framework for Automated Conjecture and Reasoning in Quantum Circuit Analysis](https://mlsanity.com/paper/2605.10327/) (2026)
- [Leveraging LLM-Based Agentic Systems to Generate Quantum Applications for Test Optimization](https://mlsanity.com/paper/2607.00939/) (2026)
- [Learning to Prepare Molecular Ground States with Transformer Models](https://mlsanity.com/paper/2607.22468/) (2026)

## Abstract (arXiv)

Quantum computing has the potential to improve our ability to solve certain optimization problems that are computationally difficult for classical computers, by offering new algorithmic approaches that may provide speedups under specific conditions. In this work, we introduce QAOA-GPT, a generative framework that leverages Generative Pretrained Transformers (GPT) to directly synthesize quantum circuits for solving quadratic unconstrained binary optimization problems, and demonstrate it on the MaxCut problem on graphs. To diversify the training circuits and ensure their quality, we have generated a synthetic dataset using the adaptive QAOA approach, a method that incrementally builds and optimizes problem-specific circuits. The experiments conducted on a curated set of graph instances demonstrate that QAOA-GPT, generates high quality quantum circuits for new problem instances unseen in the training as well as successfully parametrizes QAOA. Our results show that using QAOA-GPT to generate quantum circuits will significantly decrease both the computational overhead of classical QAOA and adaptive approaches that often use gradient evaluation to generate the circuit and the classical optimization of the circuit parameters. Our work shows that generative AI could be a promising avenue to generate compact quantum circuits in a scalable way.