Quantum Computing in Intelligent Transportation Systems: A Survey

TL;DR

This paper surveys the intersection of quantum computing and intelligent transportation systems (ITS), addressing how quantum hardware and algorithms can tackle data-intensive optimization in traffic management, routing, and autonomous driving. It outlines universal quantum computing models, key algorithms, and quantum ML paradigms, then reviews ITS-specific applications and case studies. The analysis identifies hardware decoherence, error correction, and software tooling as key barriers, and emphasizes the need for cross-disciplinary collaboration. The work provides a roadmap for near-term hybrid quantum-classical approaches and highlights the potential for QUBO/Ising formulations and QCNN-based perception to impact future ITS systems.

Abstract

Quantum computing, a field utilizing the principles of quantum mechanics, promises great advancements across various industries. This survey paper is focused on the burgeoning intersection of quantum computing and intelligent transportation systems, exploring its potential to transform areas such as traffic optimization, logistics, routing, and autonomous vehicles. By examining current research efforts, challenges, and future directions, this survey aims to provide a comprehensive overview of how quantum computing could affect the future of transportation.

Figures (3)

• Figure 1: Comparison of Classical and Quantum STGCN.
• Figure 2: Capacitated vehicle routing problem adapted to quantum annealing by transforming to a quadratic unconstrained binary problem feld2019hybridpelofske2022parallel.
• Figure 3: Comparison of Classical and Quantum RPN.