Quantum Approaches to Urban Logistics: From Core QAOA to Clustered Scalability

• URL: http://arxiv.org/abs/2512.10813v1
• Date: Thu, 11 Dec 2025 17:00:24 GMT
• Title: Quantum Approaches to Urban Logistics: From Core QAOA to Clustered Scalability
• 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,
• Abstract summary: The Traveling Salesman Problem (TSP) is a fundamental challenge in optimization, widely applied in logistics and transportation.<n>Traditional algorithms often struggle to produce high-quality solutions within reasonable timeframes.<n>This study investigates the potential of the Quantum Approximate Optimization Algorithm (QAOA), a hybrid quantum-classical method.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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