Distributed Quantum Optimization for Large-Scale Higher-Order Problems with Dense Interactions
This addresses practical optimization challenges in fields like optical metamaterial design by enabling scalable quantum solutions for complex problems, though it is incremental in advancing quantum optimization techniques.
The paper tackled large-scale higher-order optimization problems with dense interactions by developing a distributed quantum optimization framework (DQOF), achieving high-quality solutions for up to 500 variables within 170 seconds and outperforming conventional methods in quality and scalability.
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.