Quantum Machine Learning for Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) are critical to modern mobility, enhancing traffic efficiency, reducing congestion, enabling autonomous vehicle deployment, and improving road safety. However, despite their growing relevance, ITS still faces several technical challenges. These include the need for real-time processing of large-scale sensor data, accurate and adaptive traffic prediction, robust vehicle-to-infrastructure coordination, and reliable object detection under complex environmental conditions such as noise, shadows, or low visibility. Among the most pressing issues is anomaly detection—identifying irregular or potentially hazardous behavior in traffic flow, including unexpected vehicle movements and cyber-physical threats. Traditional machine-learning approaches often struggle to meet these demands due to inherent scalability, adaptability, and resilience limitations in dynamic and uncertain environments.

This project proposes the use of quantum machine learning (QML) and hybrid quantum-classical computing to enhance the performance, adaptability, and security of ITS. Quantum neural networks (QNNs) will be explored for advanced perception tasks, including traffic signal recognition and context-aware image analysis. Quantum-based anomaly detection models will be developed to identify atypical patterns in vehicle behavior and system interactions, even under noisy or adversarial conditions. Additionally, quantum optimization algorithms will be employed to solve complex routing and traffic coordination problems more efficiently than classical methods. Quantum generative models will also be used to enrich training datasets and improve simulation fidelity.

Together, these quantum-enabled techniques aim to significantly improve the responsiveness, reliability, and intelligence of next-generation transportation systems, paving the way for more secure, efficient, and autonomous mobility infrastructures.