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原文摘要

Solving large-scale Capacitated Vehicle Routing Problems with Time Windows (CVRPTW) remains challenging because strict operational constraints and scale limit both exact and heuristic methods. We present a hybrid classical-quantum architecture combining Mixed Integer Linear Programming (MILP) with vehicle-level Quadratic Unconstrained Binary Optimization (QUBO), solved using Toshiba SQBM+ and QAOA on Rigetti Ankaa-3 and IQM Emerald quantum processors. The decomposition avoids the variable growth and constraint-enforcement difficulties of monolithic CVRPTW QUBO formulations. We evaluate the framework on five realistic benchmarks derived from London traffic and location data, spanning 10--70 customers, using a validation pipeline enforcing feasibility and monotone improvement over classical baselines. SQBM+ achieves verified fleet-distance reductions of 2.80% (7,927 m) for the 25-customer instance and 0.22% (1,186 m) for the 55-customer instance. QAOA experiments establish a current hardware boundary for reliable constraint satisfaction at subproblems of up to 4 customers (16 qubits). Diagnostic results identify assignment-level time-window tightness, rather than local route optimization, as the dominant scalability bottleneck. The results demonstrate a practical near-term role for hybrid classical-quantum optimization while establishing empirical limits for current quantum hardware.