Quantum Inspired Vehicular Network Optimization for Intelligent Decision Making in Smart Cities

Connected and automated vehicles require city-scale coordination under strict latency and reliability constraints. However, many existing approaches optimize communication and mobility separately, which can degrade performance during network outages and under compute contention. This paper presents QIVNOM, a quantum-inspired framework that jointly optimizes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication together with urban traffic control on classical edge--cloud hardware, without requiring a quantum processor. QIVNOM encodes candidate routing--signal plans as probabilistic superpositions and updates them using sphere-projected gradients with annealed sampling to minimize a regularized objective. An entanglement-style regularizer couples networking and mobility decisions, while Tchebycheff multi-objective scalarization with feasibility projection enforces constraints on latency and reliability. The proposed framework is evaluated in METR-LA--calibrated SUMO--OMNeT++/Veins simulations over a 5times5 km urban map with IEEE 802.11p and 5G NR sidelink. Results show that QIVNOM reduces mean end-to-end latency to 57.3 ms, approximately 20\% lower than the best baseline. Under incident conditions, latency decreases from 79 ms to 62 ms (-21.5\%), while under roadside unit (RSU) outages, it decreases from 86 ms to 67 ms (-22.1\%). Packet delivery reaches 96.7\% (an improvement of +2.3 percentage points), and reliability remains 96.7\% overall, including 96.8\% under RSU outages versus 94.1\% for the baseline. In corridor-closure scenarios, travel performance also improves, with average travel time reduced to 12.8 min and congestion lowered to 33\%, compared with 14.5 min and 37\% for the baseline.