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

TL;DR

QAOA-GPT is a novel generative AI framework that efficiently synthesizes quantum circuits for optimization problems, reducing computational overhead and enabling scalable quantum circuit generation.

Contribution

This work introduces QAOA-GPT, the first to use GPT models for direct quantum circuit synthesis for optimization problems, trained on adaptive QAOA-generated data.

Findings

QAOA-GPT generates high-quality circuits for unseen problem instances.

It significantly reduces classical computational overhead in QAOA.

The approach demonstrates the potential of generative AI in scalable quantum circuit design.

Abstract

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.