Distributed Quantum Optimization for Large-Scale Higher-Order Problems with Dense Interactions
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Kim, Seongmin Pascuzzi, Vincent R. Humble, Travis S. Beck, Thomas Hwang, Sanghyo Luo, Tengfei Lee, Eungkyu Suh, In-Saeng |
| author_facet | Kim, Seongmin Pascuzzi, Vincent R. Humble, Travis S. Beck, Thomas Hwang, Sanghyo Luo, Tengfei Lee, Eungkyu Suh, In-Saeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20599 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Distributed Quantum Optimization for Large-Scale Higher-Order Problems with Dense Interactions Kim, Seongmin Pascuzzi, Vincent R. Humble, Travis S. Beck, Thomas Hwang, Sanghyo Luo, Tengfei Lee, Eungkyu Suh, In-Saeng Quantum Physics Computational Engineering, Finance, and Science Distributed, Parallel, and Cluster Computing 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. |
| title | Distributed Quantum Optimization for Large-Scale Higher-Order Problems with Dense Interactions |
| topic | Quantum Physics Computational Engineering, Finance, and Science Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2604.20599 |