Extending QAOA-GPT to Higher-Order Quantum Optimization Problems

TL;DR

This paper extends the QAOA-GPT framework to higher-order optimization problems involving complex Hamiltonians, enabling efficient generation of near-optimal quantum circuits without classical optimization loops.

Contribution

It introduces a method to adapt QAOA-GPT for higher-order problems using graph embeddings, achieving high approximation ratios and scalable circuit generation.

Findings

Average approximation ratio exceeds 0.95

Generates circuits matching classical ADAPT-QAOA results

Maintains consistent parameters across circuit depths

Abstract

The recently proposed QAOA-GPT framework demonstrated that generative pre-trained transformers can learn mappings between problem graphs and optimized quantum circuits for the Quantum Approximate Optimization Algorithm (QAOA). In this work, we extend QAOA-GPT to Higher-Order Unconstrained Binary Optimization (HUBO) problems, focusing on spin-glass Hamiltonians that include cubic interaction terms. Using FEATHER graph embeddings to encode topological information, we train the model on graph-circuit pairs generated via ADAPT-QAOA and evaluate its performance on 8- and 16-qubit instances embedded on heavy-hex lattices. The generative model produces adaptive QAOA-like circuits and corresponding variational parameters in a single forward pass, bypassing the iterative classical optimization loop. The generated circuits achieve average approximation ratios exceeding 0.95, closely matching classically optimized ADAPT-QAOA results, while maintaining consistent parameter distributions across circuit depths. These results demonstrate that QAOA-GPT generalizes effectively to higher-order cost Hamiltonians and complex energy landscapes, establishing generative modeling as a scalable pathway toward autonomous variational circuit design and quantum algorithm discovery in the NISQ era.