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

Our new paper "QAOA-GPT: Efficient Generation of Adaptive and Regular Quantum Approximate Optimization Algorithm Circuits" is now out! We introduce QAOA-GPT, a generative AI framework that uses GPT-style model to synthesize quantum circuits for solving combinatorial optimization problems and demonstrate it on MaxCut. Unlike traditional quantum approximate optimization algorithms and adaptive approaches, which rely on slow, iterative optimization, QAOA-GPT generates full circuits in a single forward pass, greatly improving scalability and efficiency. Importantly, this approach does not rely on prompt engineering or fine-tuning—the model is trained entirely from scratch using a custom circuit representation and graph-conditioned architecture.

It was a great pleasure collaborating on this project with an amazing team Marwa Farag and Yuri Alexeev from NVIDIA, and Kyle Sherbert and Karunya Shirali from Virginia Tech - thank you all for making this project a great experience! And huge congratulations to PhD student Ilya Tyagin for leading this work!

Check our paper at https://arxiv.org/abs/2504.16350