Extending QAOA-GPT to Higher-Order Quantum Optimization Problems

• URL: http://arxiv.org/abs/2511.07391v1
• Date: Mon, 10 Nov 2025 18:46:38 GMT
• Title: Extending QAOA-GPT to Higher-Order Quantum Optimization Problems
• Authors: Leanto Sunny, Abhinav Rijal, George Siopsis,
• Abstract summary: We extend QAOA-GPT to Higher-Order Unconstrained Binary Optimization problems.<n>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.<n>Results demonstrate that QAOA-GPT generalizes effectively to higher-order cost Hamiltonians and complex energy landscapes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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