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.
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.
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.
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.
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.
Abstract Advances in quantum technologies have prompted renewed interest in computational models that extend beyond the capabilities of classical systems. Quantum computing, grounded in the principles of quantum mechanics, introduces novel mechanisms for information processing that challenge conventional assumptions in computer science. This paper critically examines the role of quantum computing across core computer science domains by integrating foundational theory with application-oriented research. Existing studies in cryptography, optimization, artificial intelligence, database search, and system simulation are analyzed to assess both demonstrated capabilities and practical constraints. The discussion emphasizes the increasing relevance of hybrid quantum–classical approaches in the current technological landscape, while identifying key limitations related to scalability, noise, and algorithmic feasibility. By consolidating theoretical insights with applied perspectives, this work provides a realistic assessment of the present state of quantum computing and outlines research directions essential for its effective integration into future computational systems.
Abstract Advances in quantum technologies have prompted renewed interest in computational models that extend beyond the capabilities of classical systems. Quantum computing, grounded in the principles of quantum mechanics, introduces novel mechanisms for information processing that challenge conventional assumptions in computer science. This paper critically examines the role of quantum computing across core computer science domains by integrating foundational theory with application-oriented research. Existing studies in cryptography, optimization, artificial intelligence, database search, and system simulation are analyzed to assess both demonstrated capabilities and practical constraints. The discussion emphasizes the increasing relevance of hybrid quantum–classical approaches in the current technological landscape, while identifying key limitations related to scalability, noise, and algorithmic feasibility. By consolidating theoretical insights with applied perspectives, this work provides a realistic assessment of the present state of quantum computing and outlines research directions essential for its effective integration into future computational systems.