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

2026-03-25

AI Summary

This work addresses the performance degradation of existing vehicular networking approaches under network disruptions or resource contention, which often stems from decoupled optimization of communication and traffic control. To overcome this limitation, the authors propose QIVNOM—a quantum-inspired framework that jointly optimizes V2V/V2I communications and traffic signal control within a conventional edge–cloud architecture. The method innovatively introduces an entanglement-like regularization term to couple communication and mobility decisions, and integrates probabilistic hyperpositional encoding, spherical projected gradients, and annealed sampling to enable multi-objective optimization without quantum hardware. Strict latency and reliability constraints are rigorously enforced via Chebyshev scalarization and feasibility projection. Experimental results demonstrate that QIVNOM reduces end-to-end latency to 57.3 ms (a 20% improvement), maintains 96.8% reliability even during RSU failures, decreases average travel time to 12.8 minutes, and lowers congestion rates to 33%.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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 $5\times5$~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.

Problem

• vehicular network optimization
• intelligent decision making
• latency and reliability constraints
• smart cities
• connected and automated vehicles

Innovation

• quantum-inspired optimization
• joint communication-mobility control
• entanglement-style regularizer
• sphere-projected gradients
• Tchebycheff multi-objective scalarization