We explore how quantum computing (QC) can advance transportation optimization, with a focus on two high-impact areas: traffic signal control and vehicle electrification with grid integration. As transportation systems grow in complexity, classical optimization methods increasingly struggle to deliver scalable and efficient solutions, particularly for real-time environments. This work identifies key challenges within these two domains where QC may offer advantages, particularly in handling combinatorial decision spaces and dynamic constraints. We begin by outlining the limitations of classical approaches for traffic signal control optimization and electric vehicle charging coordination, highlighting where computational limitations arise. Previous quantum formulations are discussed to demonstrate how emerging quantum algorithms, including quantum annealing and hybrid algorithms, could be leveraged to reformulate and address these problems. We also evaluate the suitability of current quantum hardware and discuss the advantages that QC could bring to these domains. While acknowledging the present limitations of QC technologies, this poster emphasizes the importance of preparing quantum-compatible models today. By reviewing and establishing formulations that align with the strengths of quantum algorithms, researchers and practitioners can better position themselves to take advantage of QC advancements as they occur. This work aims to provide a practical, forward-looking perspective on the near-term potential of quantum computing in transportation optimization. Quantum Computing Transportation Optimization Traffic Signal Control Electric Vehicle Charging
We explore how quantum computing (QC) can advance transportation optimization, with a focus on two high-impact areas: traffic signal control and vehicle electrification with grid integration. As transportation systems grow in complexity, classical optimization methods increasingly struggle to deliver scalable and efficient solutions, particularly for real-time environments. This work identifies key challenges within these two domains where QC may offer advantages, particularly in handling combinatorial decision spaces and dynamic constraints. We begin by outlining the limitations of classical approaches for traffic signal control optimization and electric vehicle charging coordination, highlighting where computational limitations arise. Previous quantum formulations are discussed to demonstrate how emerging quantum algorithms, including quantum annealing and hybrid algorithms, could be leveraged to reformulate and address these problems. We also evaluate the suitability of current quantum hardware and discuss the advantages that QC could bring to these domains. While acknowledging the present limitations of QC technologies, this poster emphasizes the importance of preparing quantum-compatible models today. By reviewing and establishing formulations that align with the strengths of quantum algorithms, researchers and practitioners can better position themselves to take advantage of QC advancements as they occur. This work aims to provide a practical, forward-looking perspective on the near-term potential of quantum computing in transportation optimization. Quantum Computing Transportation Optimization Traffic Signal Control Electric Vehicle Charging
Abstract Traffic optimization remains a significant challenge in urban planning and transportation management. While efficient traffic optimization is crucial for enhancing urban mobility, reducing congestion, and promoting environmental sustainability, traditional computational methods often struggle with the complex, dynamic nature of traffic systems. Recent advances in quantum computing, particularly quantum annealing, offer promising new techniques that could revolutionize traffic flow optimization. This work systematically reviews the literature, starting with search term formulation and ending with the final set of articles. These articles are categorized into three groups: (1) traffic signal control, (2) traffic flow optimization, and (3) routing problems optimization (including vehicle routing problem and traveling salesman problem). The review critically examines current studies on quantum annealing-based traffic optimization, focusing on contributions, methods, solvers, problem suitability, key findings, benchmark fairness, and limitations. It identifies key challenges and provides recommendations for future research. Insights from this work offer researchers and practitioners a concise overview of current challenges and future directions in traffic optimization.
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.
Complex optimization problems, such as traffic routing and electric vehicle (EV) charging scheduling, are becoming increasingly challenging for intelligent transportation systems (ITSs), in particular as computational resources are limited and network conditions evolve frequently. This paper explores a quantum computing approach to address these issues by proposing a hybrid quantum-classical (HQC) workflow that leverages the variational quantum eigensolver (VQE), an algorithm particularly well suited for execution on noisy intermediate-scale quantum (NISQ) hardware. To this end, the EV charging scheduling and traffic routing problems are both reformulated as binary optimization problems and then encoded into Ising Hamiltonians. Within each VQE iteration, a parametrized quantum circuit (PQC) is prepared and measured on the quantum processor to evaluate the Hamiltonian’s expectation value, while a classical optimizer—such as COBYLA, SPSA, Adam, or RMSProp—updates the circuit parameters until convergence. In order to find optimal or nearly optimal solutions, VQE uses PQCs in combination with classical optimization algorithms to iteratively minimize the problem Hamiltonian. Simulation results exhibit that the VQE-based method increases the efficiency of EV charging coordination and improves route selection performance. These results demonstrate how quantum computing will potentially advance optimization algorithms for next-generation ITSs, representing a practical step toward quantum-assisted mobility solutions.
This work provides an updated report on the progress and prospects of quantum computing for advancing transport modeling. Despite recent developments in classical methods, their use in computationally intensive transport tasks such as traffic assignment, traffic signal control, logistics, and pedestrian modeling remains limited due to the combinatorial complexity and uncertainty associated with the real-world transport scenarios. Quantum optimization algorithms offer the potential for addressing transport problems, although most studies still remain at the proof-of-concept stage. In this work, we highlight key challenges in traffic and logistics optimization and investigate how quantum optimization tools could lead to novel and efficient solutions. Our perspective offers a clear and concise timeline for the development of quantum optimization techniques and their practical implementation aspects by highlighting key hardware/software challenges and proposed solutions. We also discuss the barriers to adoption within transport contexts and possible pathways to overcome current limitations to the application of quantum optimization techniques in real-world transport systems. Finally, we provide our perspective for the applications of quantum computing in addressing challenges pertaining to large crowd management and transport optimization during the Olympics and Paralympics 2032 Games which may coincide with the development of a large-scale quantum computer.
Quantum computing holds transformative potential for Intelligent Transportation Systems (ITS), offering solutions to complex and computationally intensive challenges in modern transportation networks. By leveraging quantum mechanics principles like superposition and entanglement, quantum algorithms can optimize traffic management, route planning, and logistics with unprecedented speed and accuracy. This can significantly enhance real-time traffic flow predictions, dynamic route optimization, and efficient resource allocation, particularly for electric vehicle networks, promoting sustainable and energy-efficient transportation solutions. However, integrating quantum computing into ITS faces challenges. Current quantum hardware is limited by issues like qubit instability, short coherence times, and high error rates, impeding large-scale applications. Additionally, developing hybrid systems that combine classical and quantum computing capabilities, and ensuring robust data privacy and security, are significant hurdles.
Quantum computing offers transformative potential to solve complex optimisation problems in transportation and logistics, particularly those that involve large combinatorial decision spaces such as vehicle routing, traffic control, and supply chain design. Despite theoretical promise and growing empirical interest, its adoption remains limited. This systematic literature review synthesises fifteen peer-reviewed studies published between 2015 and 2025, examining the application of quantum and quantum-inspired methods to transport optimisation. The review identifies five key problem domains (vehicle routing, factory scheduling, network design, traffic operations, and energy management) and categorises the quantum techniques used, including quantum annealing, variational circuits, and digital annealers. Although several studies demonstrate performance gains over classical heuristics, most rely on synthetic datasets, lack statistical robustness, and omit critical operational metrics such as energy consumption and queue latency. Four cross-cutting barriers are identified: hardware limitations, data availability, energy inefficiency, and organisational readiness. The review identifies limited real-world deployment, a lack of standardised benchmarks, and scarce cost–benefit evaluations, highlighting key areas where further empirical work is needed. It concludes with a structured research agenda aimed at bridging the gap between laboratory demonstrations and practical implementation, emphasising the need for pilot trials, open datasets, robust experimental protocols, and interdisciplinary collaboration.