Quantum Computing for Transportation and Logistics

Executive Summary

To assess the current state and future possibilities of quantum computing and the transportation and logistics industry, QED-C’s Use Cases Technical Advisory Committee led this study based on a workshop exploring the feasibility and impact of different use cases.1 Quantum computing (QC) offers intriguing solutions to supply chain, transportation, and logistics problems that classical computers cannot completely solve. It also offers the possibility of significantly faster computations, with applications in all modes of the transportation and logistics industry — air, land, and sea.

The challenges faced by the transportation and logistics industry include optimization of inventory across many facilities, route planning, minimization of manufacturing costs, last-mile delivery, factory and truck scheduling, dynamic pricing algorithms, fleet management and maintenance, sustainability and green logistics, energy systems, control of autonomous vehicles, and navigation within modern cities. The literature suggests that quantum computing offers advantages in three primary areas: optimization, machine learning, and simulation.

As part of this study, experts from both the transportation and logistics industry and the quantum communities convened at a workshop to identify use cases at the intersection of the two fields. The overwhelming majority of use cases identified were ultimately optimization problems, most of which came down to planning operations. In contrast, use cases applying a simulation (from a logistics industry perspective) approach were seen by experts as less feasible and impactful than most optimization problems. Several companies have demonstrated small-scale prototypes that apply QC algorithms to different classes of logistics problems.2

From analysis of the current state and the feasibility and impact of use cases of quantum computing for transportation and logistics applications, four use cases emerge as those that could have the greatest impact in the relatively near term:

  • Optimization of labor plans
  • Continuous route optimization
  • Optimization of warehousing
  • Demand forecasting

In addition, this report puts forward five recommendations for boosting development and adoption of QC technologies by the transportation and logistics industry:

  • Increase operational efficiency for businesses: Quantum computing offers possible solutions for increased business efficiencies via better routing programs, efficient cargo loading, optimized manufacturing processes, and optimal labor scheduling. However, QC adoption can be prohibitively expensive and risky, especially for smaller companies. To overcome this barrier, QC companies could offer discounted rates for small logistics companies to trial the technology and see the business efficiencies that can be gained. This would also provide valuable feedback and data to the QC company to improve their product. This cross-industry collaboration could even facilitate the development of quantum-enabled route planning and other optimization tools.

    Relatedly, Manufacturing USA is a network of institutes that each have a distinct technology focus but with a common goal: to secure the future of US manufacturing through innovation, education, and collaboration. Emerging technologies like quantum computers could impact all of the technologies, ranging from flexible electronics to biomaterials. The Manufacturing USA program should disseminate information about quantum computing and other emerging technologies across the network to ensure broad incorporation as advanced manufacturing processes are being developed.

  • Increase supply chain security and resilience: There are broad dependencies between the security and resilience of national and global supply chains. Two measures can support continued innovation in this area: (1) identification of the weakest links in supply chains and direction of enhancements to those areas, and (2) increased capabilities for contingency planning tools. QC technologies could analyze more data across more variables and constraints than can classical computers, enabling the development of more accurate and comprehensive forecasts and operating plans that better protect against supply chain threats. Government can boost these capabilities of quantum computers by creating testbeds and sandbox programs focused on demonstrations, proofs of concept, and pilots of near-term applications for supply chain management.

  • Address sustainability: Climate change is a top concern of companies and nations, and transportation-based emissions are a leading contributor. Any potential for increases in efficiencies that reduce emissions should be explored and developed. One of the most impactful uses of quantum computers in transportation and logistics is continuous route optimization, which can decrease emissions and fuel usage across transport methods. As companies increasingly look to cut their carbon footprint, they should consider the impact that QC technology adoption can have by helping them better optimize routes and processes for maximum fuel efficiency. As an added bonus for companies, using quantum computing to optimize this way will likely lead to cost savings as well.

  • Optimize government logistics missions: Government can be an early adopter of QC solutions in optimizing its own fleets and missions. For example, the US Postal Service could use QC technology to better plan fleet maintenance, schedule staff, and design more fuel and time efficient routes. By adopting QC technologies in its earlier stages, government can guarantee revenues to help sustain private QC companies.

  • Create a skilled workforce: QC technology is evolving quickly, creating demand for skilled workers who are able to contribute to the field. This includes opportunities for the end users of the technology, such as operating plan developers and route designers, to shape its development and key features and functions. However, most supply chain workers today are not well versed in quantum technology. Including QC education in industrial and supply chain engineering degree programs could increase understanding and adoption of this new technology. Training could also be extended to the existing transportation and logistics workforce by collaborating with professional organizations to provide knowledge, skills, and access to the latest QC tools. This training could be especially useful for the workers who focus on route planning, operating plan design, and forecasting, i.e., the tasks that could most benefit from quantum computers.

1 The study methodology, identified use cases, and workshop participants are presented in Appendices A, B, and C.
2 See, e.g., Sean J. Weinberg, Fabio Sanches, Takanori Ide, Kazumitzu Kamiya, and Randall Correll 2023. Supply chain logistics with quantum and classical annealing algorithms. Scientific Reports 13: 4770, doi: 10.1038/s41598-023-31765-8; Christopher D. B. Bentley, Samuel Marsh, André R. R. Carvalho, Philip Kilby, and Michael J. Biercuk. 2022. Quantum computing for transport optimization. arXiv, arXiv:2206.07313; and Crispin H. V. Cooper. 2022. Exploring Potential Applications of Quantum Computing in Transportation Modelling, IEEE Transactions on Intelligent Transportation Systems 23, no. 9: 14712–20, doi: 10.1109/TITS.2021.3132161.