Modeling in transportation is essential for planning and operating current and future mobility and logistics systems, with significant implications for modern economies. However, many of these modeling tasks involve computationally complex problems that challenge traditional models and forecasting techniques. Quantum computing offers a promising way to address these difficulties. This systematic literature review examines the state-of-the-art in applying quantum computing to transport modeling, including and classifying 40 studies, selected through a systematic search of three databases using predefined inclusion and exclusion criteria. We identify three main types of quantum computing techniques used: quantum algorithmic primitives, quantum optimisation methods, and quantum machine learning approaches. With respect to the transport modeling problems addressed, the reviewed works cover forecasting demand, systems’ operation economic variables, predicting network states, estimating vehicle dynamics, and queueing systems. A notable trend is the application of quantum walks and quantum neural networks for the prediction of network state variables. Our review also underscores key challenges and research gaps, including the lack of standardized benchmarks and theoretical guarantees, particularly for quantum machine learning, the underexploited use of quantum walks for vehicle dynamics estimation, and quantum annealing for network state forecasting. We conclude with future research directions aimed at closing these gaps and building stronger theoretical foundations for sustained progress.
Modeling in transportation is essential for planning and operating current and future mobility and logistics systems, with significant implications for modern economies. However, many of these modeling tasks involve computationally complex problems that challenge traditional models and forecasting techniques. Quantum computing offers a promising way to address these difficulties. This systematic literature review examines the state-of-the-art in applying quantum computing to transport modeling, including and classifying 40 studies, selected through a systematic search of three databases using predefined inclusion and exclusion criteria. We identify three main types of quantum computing techniques used: quantum algorithmic primitives, quantum optimisation methods, and quantum machine learning approaches. With respect to the transport modeling problems addressed, the reviewed works cover forecasting demand, systems’ operation economic variables, predicting network states, estimating vehicle dynamics, and queueing systems. A notable trend is the application of quantum walks and quantum neural networks for the prediction of network state variables. Our review also underscores key challenges and research gaps, including the lack of standardized benchmarks and theoretical guarantees, particularly for quantum machine learning, the underexploited use of quantum walks for vehicle dynamics estimation, and quantum annealing for network state forecasting. We conclude with future research directions aimed at closing these gaps and building stronger theoretical foundations for sustained progress.
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.
Transport research has significant potential to benefit from quantum computing. The rise of intelligent transport systems, autonomous vehicles, and the Internet of Things has created an unprecedented demand for efficient information processing and computational optimisation. Accordingly, transport engineers and scientists have explored the ever-improving capabilities of quantum computers in an effort to meet this demand. Motivated by this growing interest, this paper sets out three aims: (1) to introduce the fundamental aspects of quantum computing relevant to the transport domain; (2) to develop a pipeline for identifying transport-related problems which are suitable for quantum advantage, solving them, and assessing their performance fairly; (3) to provide a systematic review of the existing literature and a perspective. For the latter, a systematic search of the Scopus and IEEE Xplore databases, supplemented by additional citation sources, identified 91 studies for inclusion following PRISMA 2020 guidelines. While a diverse set of use cases have been proposed, we conclude that future research should prioritise problems where quantum computation offers a clearer practical benefit. To this end, we suggest promising directions to guide further work in this burgeoning subfield.
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.
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.
The rapid evolution of quantum technologies presents transformative opportunities for various industries, including the transport sector. As global supply chains grow increasingly complex and data-driven logistics become central to operational efficiency, quantum computing and quantum communication are emerging as potential enablers of next-generation transport systems. This research explores the intersection of quantum innovation and transportation, with a focus on enhancing efficiency, security, and decision-making processes in logistics and mobility. The study adopts a qualitative methodology grounded in an extensive literature review of scholarly publications, industry reports, and policy papers from the past decade. Sources were selected based on their relevance to quantum applications in transportation, including routing optimization, traffic flow prediction, secure communications, and fleet management. The review framework categorizes insights according to technological readiness, practical implementation, and strategic value in the transport sector. Findings from the literature suggest that quantum computing holds significant promise for solving large-scale optimization problems, such as dynamic route planning and freight allocation, which are currently limited by classical computational methods. Additionally, quantum key distribution (QKD) is highlighted as a secure communication solution for protecting sensitive data in autonomous and connected transport systems. Although real-world applications remain limited, pilot studies and simulations indicate measurable improvements in processing speed and security protocols. This research underscores the potential of quantum technologies to redefine competitive advantage in the transport sector. By integrating quantum solutions into existing digital infrastructure, companies may achieve faster, more secure, and more sustainable transport operations. The findings also call for interdisciplinary collaboration and proactive policy support to bridge the gap between theoretical potential and practical deployment.
Quantum computing is emerging as a powerful computational paradigm capable of solving infeasible problems for classical computers. This review explores the intersection of quantum computing and artificial intelligence (AI), focusing on how quantum algorithms can enhance AI applications. The purpose of this review is to provide a comprehensive analysis of current advancements in quantum machine learning (QML), quantum optimization, and quantum neural networks, highlighting their implications for data processing, cryptography, and decision-making systems. The scope of the literature includes peer-reviewed articles, conference papers, and technical reports from 2020 to 2025, focusing on quantum algorithms, applications in AI, and hardware limitations. Key findings indicate that quantum computing can significantly accelerate data processing tasks, optimize complex problem-solving techniques, and improve the efficiency of AI models. However, challenges such as quantum noise, error correction, and hardware scalability remain substantial barriers to widespread adoption. This review concludes with discussing future research directions, including hybrid quantum-classical systems and advanced quantum error correction methods. This study serves as a foundational reference for researchers and practitioners aiming to leverage quantum computing for AI-driven applications. 3. Keywords Quantum Computing, Artificial Intelligence, Quantum Machine Learning, Quantum Algorithms, Data Processing, Optimization, Quantum Neural Networks