Quantum Computing in Transportation Engineering: A Survey
1524-9050
Abstract
Quantum computing introduces new computational methods that address complex optimization and machine learning challenges in transportation engineering. This literature survey reviews fundamental quantum principles, including qubits, superposition, entanglement, and quantum gates, and examines their applications in transportation. Fundamental quantum algorithms, including Shor's algorithm, the Quantum Approximate Optimization Algorithm (QAOA), and Grover's search, are described for their applications in transportation, such as network analysis, traffic management, and vehicle routing. Quantum optimization techniques, including hybrid quantum-classical approaches and quantum annealing, have been explored for combinatorial challenges such as the Vehicle Routing Problem (VRP) and dynamic traffic signal control. Additionally, quantum machine learning models, such as Quantum Convolutional Neural Networks (QCNNs) and quantum clustering, present applications in traffic prediction and anomaly detection. Despite its potential, quantum computing in transportation faces challenges related to hardware limitations, error rates, and a lack of established quantum programming frameworks. Addressing these challenges necessitates progress in hardware scalability, error mitigation techniques, and algorithm refinement. As quantum computing continues to evolve, its integration into transportation engineering could improve computational efficiency and enable new capabilities in optimization, traffic management, and intelligent transportation systems. This survey outlines key developments, challenges, and future research directions in this emerging field.