Quantum Computing in Transportation Engineering: A Survey

Quantum computing introduces powerful computational paradigms capable of addressing optimization and machine learning challenges that are increasingly critical in transportation engineering. This paper presents a comprehensive survey of how quantum algorithms and models are being applied across transportation domains, including intelligent transportation systems, traffic management, vehicle routing, logistics, and autonomous systems. Unlike prior reviews that primarily introduce quantum theory, we systematically connect fundamental quantum principles-qubits, superposition, entanglement, and quantum gates-with specific transportation use cases. We synthesize recent findings showing that the Quantum Approximate Optimization Algorithm, Grover’s search, and hybrid quantum–classical solvers can improve performance in combinatorial problems such as vehicle routing and dynamic traffic signal control, while quantum machine learning models (e.g., Quantum Convolutional Neural Networks, Quantum Generative Adversarial Networks, and clustering algorithms) demonstrate promise in traffic prediction, anomaly detection, and autonomous vehicle perception. Our review highlights that although quantum hardware limitations (noise, scalability, connectivity) remain significant, empirical studies already suggest measurable improvements in solution quality and computational efficiency when compared to classical heuristics. The key contribution of this survey is to map the landscape of quantum computing in transportation engineering, identify application-specific advantages and current bottlenecks, and outline future research directions toward scalable, domain-specific quantum algorithms. By doing so, we provide transportation researchers and practitioners with a structured foundation to integrate quantum approaches into emerging mobility systems, with implications for efficiency, safety, sustainability, and equity.