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arXiv · 2604.20599

Distributed Quantum Optimization for Large-Scale Higher-Order Problems with Dense Interactions

Seongmin Kim·Vincent R. Pascuzzi·Travis S. Humble·Thomas Beck·Sanghyo Hwang·Tengfei Luo·Eungkyu Lee·In-Saeng Suh
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Abstract

Many real-world problems are naturally formulated as higher-order optimization (HUBO) tasks involving dense, multi-variable interactions, which are challenging to solve with classical methods. Quantum optimization offers a promising route, but hardware constraints and limitations to quadratic formulations have hampered their practicality. Here, we develop a distributed quantum optimization framework (DQOF) for dense, large-scale HUBO problems. DQOF assigns quantum circuits a central role in directly capturing higher-order interactions, while high-performance computing orchestrates large-scale parallelism and coordination. A clustering strategy enables wide quantum circuits without increasing depth, allowing efficient execution on near-term quantum hardware. We demonstrate high-quality solutions for HUBOs up to 500 variables within 170 seconds, significantly outperforming conventional approaches in solution quality and scalability. Applied to optical metamaterial design, DQOF efficiently discovers high-performance structures and shows that higher-order interactions are important for practical optimization problems. These results establish DQOF as a practical and scalable computational paradigm for large-scale scientific optimization.

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Seongmin Kim, Vincent R. Pascuzzi, Travis S. Humble, Thomas Beck, Sanghyo Hwang, Tengfei Luo, Eungkyu Lee, In-Saeng Suh. 2026-04-22. Distributed Quantum Optimization for Large-Scale Higher-Order Problems with Dense Interactions. https://arxiv.org/abs/2604.20599

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