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Stream/Area:
-
Subject:
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Paper ID:
IJSREM52469
Volume:
9 |
9 |
Sep 2025
Pages:
1-10
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Manager-Technical Support
Operations, PTC Software Inc. India
The transportation and logistics sector is undergoing a profound digital transformation driven by advances in artificial intelligence, Internet of Things (IoT), cloud computing, and data analytics. However, the increasing complexity of supply chains, vehicle routing, fleet management, warehouse operations, and traffic optimization presents computational challenges that often exceed the capabilities of classical computing systems. Quantum computing has emerged as a disruptive technology capable of solving complex optimization, simulation, and decision-making problems significantly faster than conventional approaches. This paper presents a comprehensive review of quantum computing applications in transportation and logistics, examining the current state of research, industrial implementations, challenges, and future directions. A systematic literature review methodology based on the PRISMA framework was employed to analyze recent studies published between 2018 and 2026. The review identifies key application areas including vehicle routing optimization, supply chain management, traffic flow optimization, warehouse automation, demand forecasting, and multimodal transportation planning. Findings indicate that quantum algorithms such as Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), Quantum Annealing, and Grover’s Search Algorithm demonstrate significant potential in addressing NP-hard optimization problems inherent in logistics systems. Industrial initiatives by IBM, D-Wave, Volkswagen, DHL, Airbus, and Amazon highlight the growing commercialization of quantum technologies. Despite promising developments, challenges related to hardware limitations, error correction, scalability, workforce readiness, and integration with existing infrastructure remain substantial barriers to adoption. The paper proposes a conceptual framework for quantum-enabled logistics ecosystems and outlines future research opportunities. The study contributes theoretically by synthesizing fragmented literature, managerially by guiding strategic adoption decisions, technologically by identifying emerging architectures, and sustainably by highlighting potential reductions in emissions and resource consumption through optimized logistics operations.
KEYWORDS
Quantum computing represents one of the most transformative technological developments of the twenty-first century. The transportation and logistics sector, characterized by complex optimization challenges, stands to benefit significantly from quantum-enabled solutions. This review demonstrates that quantum computing has already shown promise in vehicle routing, supply chain optimization, traffic management, warehouse operations, and demand forecasting. Industrial initiatives from leading organizations indicate growing commercial interest and practical feasibility. However, substantial challenges remain, including hardware limitations, error correction requirements, integration complexity, and workforce skill shortages. The near future is likely to be dominated by hybrid quantum-classical approaches that combine the strengths of both computational paradigms. As quantum technology matures, its impact on transportation and logistics could be profound, enabling unprecedented levels of efficiency, sustainability, and operational intelligence. Organizations that proactively explore quantum applications today may gain significant competitive advantages in the future digital economy.
1. Ajagekar, A., Humble, T., & You, F. (2020). Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems. Computers & Chemical Engineering, 132, 106630. 2. Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017). Quantum machine learning. Nature, 549(7671), 195–202. 3. Deutsch, D. (1985). Quantum theory, the Church-Turing principle and the universal quantum computer. Proceedings of the Royal Society A, 400(1818), 97–117. 4. Farhi, E., Goldstone, J., & Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv Preprint arXiv:1411.4028. 5. Feld, S., Roch, C., Gabor, T., Seidel, C., Neukart, F., Galter, I., Mauerer, W., & Linnhoff-Popien, C. (2019). A hybrid solution method for the capacitated vehicle routing problem using a quantum annealer. Frontiers in ICT, 6, 13. 6. Feynman, R. P. (1982). Simulating physics with computers. International Journal of Theoretical Physics, 21(6–7), 467–488. 7. Grover, L. K. (1996). A fast quantum mechanical algorithm for database search. Proceedings of STOC, 212–219. 8. A. Gulhane, “Industry 5.0 and Intelligent Logistics: Transforming Supply Chain Operations through Human-Centric Automation,” International Journal of Engineering Science and Advanced Technology (IJESAT), vol. 24, no. 12, pp. 287–296, Dec. 2024. 9. A. Gulhane, “Internet of Things, Artificial Intelligence, and Quantum Computing: A Convergent Framework for Smart Logistics Ecosystems,” International Journal of Engineering Science and Advanced Technology (IJESAT), vol. 24, no. 12, pp. 297–317, Dec. 2024. 10. A. Gulhane, “Digital Twin Technology for Building Resilient and Adaptive Global Supply Chains: A Review,” International Research Journal of Modernization in Engineering Technology and Science (IRJMETS), vol. 6, no. 12, pp. 4287–4296, Dec. 2024.
Publication Date
9/26/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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