This paper describes a hybrid quantum-classical system for solving the Capacity-Constrained Vehicle Routing Problem (CVRP) using a real-world distribution network in Samarkand, Uzbekistan.The problem is formulated as a quadratic unconstrained binary optimization (QUBO) model, and the solution is implemented using the D-Wave quantum approximate optimization algorithm (QAOA) and quantum annealing. A modular software architecture is described that enables seamless execution on both the IBM Quantum platform and the D-Wave platform. Testing experiments on custom Samarkand instances and standard CVRP datasets showed that the D-Wave hybrid solver yields 1.86–2.34% better solutions than the OR-Tools heuristics on average instances.
| Mualliflar | Rabimov, Nodir, Назаров, Файзулло, Ахатов, Акмал |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2026-05-30 |
| Son | 2 |
| Betlar | 220-229 |
| Til | Rus |
квантовая оптимизация, Модели, линейные, аффинные, диапазоны, квантование, динамические диапазоны, датчик, Deep Learning, Глубокое обучение, Нейронные сети, Криптоанализ, Потоковое шифрование, RC4, RC4A, Trivium, TRIAD, Модель «черного ящика», Keystream, Обнаружение отклонений, Машинное обучение, Свёрточные нейронные сети (CNN), Рекуррентные нейронные сети (RNN), LSTM, Дифференциальный криптоанализ, Анализ побочных каналов, Криптография., Vehicle routing problem (VRP), unconstrained quadratic binary optimization (QUBO), quantum approximate optimization algorithm (QAOA), quantum annealing, hybrid quantum-classical computing, combinatorial optimization, NISQ
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