{"ID":2830348,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.10813","arxiv_id":"2512.10813","title":"Quantum Approaches to Urban Logistics: From Core QAOA to Clustered Scalability","abstract":"The Traveling Salesman Problem (TSP) is a fundamental challenge in combinatorial optimization, widely applied in logistics and transportation. As the size of TSP instances grows, traditional algorithms often struggle to produce high-quality solutions within reasonable timeframes. This study investigates the potential of the Quantum Approximate Optimization Algorithm (QAOA), a hybrid quantum-classical method, to solve TSP under realistic constraints. We adopt a QUBO-based formulation of TSP that integrates real-world logistical constraints reflecting operational conditions, such as vehicle capacity, road accessibility, and time windows, while ensuring compatibility with the limitations of current quantum hardware. Our experiments are conducted in a simulated environment using high-performance computing (HPC) resources to assess QAOA's performance across different problem sizes and quantum circuit depths. In order to improve scalability, we propose clustering QAOA (Cl-QAOA), a hybrid approach combining classical machine learning with QAOA. This method decomposes large TSP instances into smaller sub-problems, making quantum optimization feasible even on devices with a limited number of qubits. The results offer a comprehensive evaluation of QAOA's strengths and limitations in solving constrained TSP scenarios. This study advances quantum optimization and lays groundwork for future large-scale applications.","short_abstract":"The Traveling Salesman Problem (TSP) is a fundamental challenge in combinatorial optimization, widely applied in logistics and transportation. As the size of TSP instances grows, traditional algorithms often struggle to produce high-quality solutions within reasonable timeframes. This study investigates the potential o...","url_abs":"https://arxiv.org/abs/2512.10813","url_pdf":"https://arxiv.org/pdf/2512.10813v1","authors":"[\"F. Picariello\",\"G. Turati\",\"R. Antonelli\",\"I. Bailo\",\"S. Bonura\",\"G. Ciarfaglia\",\"S. Cipolla\",\"P. Cremonesi\",\"M. Ferrari Dacrema\",\"M. Gabusi\",\"I. Gentile\",\"V. Morreale\",\"A. Noto\"]","published":"2025-12-11T17:00:24Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cs.LG\"]","methods":"[]","has_code":false}