QuantumQUBO Agent: Automating Quadratic Unconstrained Binary Optimization (QUBO) Formulation

Further analysis identifies iterative self-repair as the most important component contributing to improved performance. It comes from a paper posted to arXiv on 14 September 2026. Quadratic Unconstrained Binary Optimization (QUBO) is a central formulation for combinatorial optimization and has gained increasing attention due to its compatibility with quantum, hybrid quantum-classical, and quantum-inspired solvers. However, translating natural-language problem descriptions into correct QUBO formulations remains difficult, requiring the identification of binary variables, constraints, objective functions, penalty terms, and suitable penalty weights. This process is time-consuming and often demands substantial domain expertise. To address this challenge, we propose an end-to-end multi-agent framework that automatically generates QUBO formulations from natural-language problem descriptions, supported by structured or unstructured test cases. To evaluate its performance, We also introduce QUBOBench, a benchmark containing 100 combinatorial optimization problems across 12 application domains, curated from peer-reviewed literature, competitions, and canonical NP-hard problems. Experimental results show that our framework achieves 68% accuracy on QUBOBench, outperforming a direct single-call baseline by 22%. The data and code are open-sourced at https://quitttcat.github.io/QuantumQUBOAgent.

Why it counts

Further analysis identifies iterative self-repair as the most important component contributing to improved performance.

Sources

Primary source: primary source

What is not known

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