Quantum Edge Computing for Data Analysis in Connected Autonomous Vehicles

Integrating quantum computing with edge computing presents an approach to managing the vast data generated by Connected Autonomous Vehicles (CAVs). This article introduces a novel framework that leverages quantum algorithms at the network edge to significantly enhance the efficiency and speed of data analysis for CAVs. By combining the principles of quantum computing with the distributed nature of edge computing, we propose a solution that not only addresses the latency and bandwidth challenges of conventional cloud computing models but also paves the way for real-time decision-making processes essential for autonomous vehicle operations. Our findings reveal the potential for quantum edge computing to transform the processing capabilities at the edge, offering a scalable, efficient, and faster method for data analysis that could significantly improve traffic management, urban mobility, and overall safety on the roads.