Quantum Computing for Logistics and Supply Chain Optimization

Carlos Pérez (1), Ana Rodríguez (2), Luis Hernández (3)
(1) National Autonomous University of Mexico (UNAM), Mexico,
(2) Monterrey Institute of Technology (ITESM), Mexico,
(3) Autonomous University of San Luis Potosi, Mexico

Abstract

The background of this research is related to the challenges faced by the logistics and supply chain industry in optimizing the process of planning shipping routes and managing operational costs. The application of quantum computing technology offers the potential to solve complex problems that are difficult to solve with conventional methods. The purpose of this study is to evaluate the effectiveness of quantum computing in logistics and supply chain optimization by reducing delivery time and operational costs. This research method involves the use of secondary data from three major logistics companies and the application of quantum computing-based optimization algorithms to analyze their influence on operational efficiency. The results show that the application of quantum computing can reduce average delivery time by 10% and operational costs by up to 10%, with a significant increase in customer satisfaction. The conclusion of this study confirms that quantum computing technology has the potential to bring about major changes in the logistics and supply chain industry by improving efficiency and reducing operational costs. Further research is needed to develop more specific algorithms and test the application of these technologies on a larger scale.


 

Full text article

Generated from XML file

References

Ajagekar, A. (2021). Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems. Applied Energy, 303(Query date: 2024-12-07 00:48:28). https://doi.org/10.1016/j.apenergy.2021.117628

Ajagekar, A. (2022). Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality. Renewable and Sustainable Energy Reviews, 165(Query date: 2024-12-07 00:48:28). https://doi.org/10.1016/j.rser.2022.112493

Awan, U. (2022). Quantum computing challenges in the software industry. A fuzzy AHP-based approach. Information and Software Technology, 147(Query date: 2024-12-07 00:48:28). https://doi.org/10.1016/j.infsof.2022.106896

Bardin, J. C. (2021). Microwaves in Quantum Computing. IEEE Journal of Microwaves, 1(1), 403–427. https://doi.org/10.1109/JMW.2020.3034071

Bauer, G. R. (2021). Intersectionality in quantitative research: A systematic review of its emergence and applications of theory and methods. SSM - Population Health, 14(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.ssmph.2021.100798

Bazaras, D. (2024). OPTIMIZATION PROBLEM OF CHINA’S SUPPLY CHAIN TRANSPORTATION ISSUES IN EUROPEAN LOGISTICS. Economics and Environment, 90(3). https://doi.org/10.34659/eis.2024.90.3.800

Blunt, N. S. (2022). Perspective on the Current State-of-the-Art of Quantum Computing for Drug Discovery Applications. Journal of Chemical Theory and Computation, 18(12), 7001–7023. https://doi.org/10.1021/acs.jctc.2c00574

Bova, F. (2021). Commercial applications of quantum computing. EPJ Quantum Technology, 8(1). https://doi.org/10.1140/epjqt/s40507-021-00091-1

Boyer, O. (2024). The Agri-Food Supply Chain and Logistics Problems Optimization. Optimization in the Agri-Food Supply Chain: Recent Studies, Query date: 2024-12-07 08:38:06, 197–250. https://doi.org/10.1002/9781394316977.ch10

Emani, P. S. (2021). Quantum computing at the frontiers of biological sciences. Nature Methods, 18(7), 701–709. https://doi.org/10.1038/s41592-020-01004-3

Gill, S. S. (2022). Quantum computing: A taxonomy, systematic review and future directions. Software - Practice and Experience, 52(1), 66–114. https://doi.org/10.1002/spe.3039

Gupta, S. (2021). Significance of multi-objective optimization in logistics problem for multi-product supply chain network under the intuitionistic fuzzy environment. Complex and Intelligent Systems, 7(4), 2119–2139. https://doi.org/10.1007/s40747-021-00326-9

Herman, D. (2023). Quantum computing for finance. Nature Reviews Physics, 5(8), 450–465. https://doi.org/10.1038/s42254-023-00603-1

Jayarathna, C. P. (2021). Multi-objective optimization for sustainable supply chain and logistics: A review. Sustainability (Switzerland), 13(24). https://doi.org/10.3390/su132413617

Jurcevic, P. (2021). Demonstration of quantum volume 64 on a superconducting quantum computing system. Quantum Science and Technology, 6(2). https://doi.org/10.1088/2058-9565/abe519

Kavokin, A. (2022). Polariton condensates for classical and quantum computing. Nature Reviews Physics, 4(7), 435–451. https://doi.org/10.1038/s42254-022-00447-1

Khezeli, M. (2021). Simulation Based Optimization Model for Logistic Network in a Multi-Stage Supply Chain Network with Considering Operational Production Planning “truck Loading System and Transportation Network.” International Journal of Industrial Engineering and Production Research, 32(3). https://doi.org/10.22068/ijiepr.1109

Kim, Y. (2023). Evidence for the utility of quantum computing before fault tolerance. Nature, 618(7965), 500–505. https://doi.org/10.1038/s41586-023-06096-3

Kwon, S. (2021). Gate-based superconducting quantum computing. Journal of Applied Physics, 129(4). https://doi.org/10.1063/5.0029735

Leon, N. P. de. (2021). Materials challenges and opportunities for quantum computing hardware. Science, 372(6539). https://doi.org/10.1126/science.abb2823

Li, F. (2022). Optimization Design of Short Life Cycle Product Logistics Supply Chain Scheme Based on Support Vector Machine. Computational Intelligence and Neuroscience, 2022(Query date: 2024-12-07 08:38:06). https://doi.org/10.1155/2022/2311845

Liu, L. (2024). Order Allocation Optimization and Genetic Algorithm in Logistics Service Supply Chain. Lecture Notes in Electrical Engineering, 1132(Query date: 2024-12-07 08:38:06), 194–198. https://doi.org/10.1007/978-981-99-9538-7_28

Lu, S. (2022). Supply Chain Management Operation Mode and Optimization Path of Logistics Enterprises in the Era of Big Data. Lecture Notes on Data Engineering and Communications Technologies, 136(Query date: 2024-12-07 08:38:06), 1026–1033. https://doi.org/10.1007/978-3-031-05237-8_127

Madhavi, N. B. (2023). Supply Chain Management Using Bee Swarm Optimisation to Improve the Logistics in E- Commerce Era. 2023 International Conference on Disruptive Technologies, ICDT 2023, Query date: 2024-12-07 08:38:06, 165–168. https://doi.org/10.1109/ICDT57929.2023.10150921

Mangini, S. (2021). Quantum computing models for artificial neural networks. EPL, 134(1). https://doi.org/10.1209/0295-5075/134/10002

Matskul, V. (2021). Optimization of the cold supply chain logistics network with an environmental dimension. IOP Conference Series: Earth and Environmental Science, 628(1). https://doi.org/10.1088/1755-1315/628/1/012018

McFadden, D. (2021). Quantitative methods for analysing travel behaviour ofindividuals: Some recent developments. Behavioural Travel Modelling, Query date: 2024-12-01 09:57:11, 279–318.

Mosteanu, N. R. (2021). Fintech frontiers in quantum computing, fractals, and blockchain distributed ledger: Paradigm shifts and open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 1–19. https://doi.org/10.3390/joitmc7010019

Mueller, A. V. (2020). Quantitative Method for Comparative Assessment of Particle Removal Efficiency of Fabric Masks as Alternatives to Standard Surgical Masks for PPE. Matter, 3(3), 950–962. https://doi.org/10.1016/j.matt.2020.07.006

Mujal, P. (2021). Opportunities in Quantum Reservoir Computing and Extreme Learning Machines. Advanced Quantum Technologies, 4(8). https://doi.org/10.1002/qute.202100027

Nokkala, J. (2021). Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing. Communications Physics, 4(1). https://doi.org/10.1038/s42005-021-00556-w

Pan, C. (2021). Optimization of intelligent logistics supply chain management system based on wireless sensor network and RFID technology. Journal of Sensors, 2021(Query date: 2024-12-07 08:38:06). https://doi.org/10.1155/2021/8111909

Rasool, R. U. (2023). Quantum Computing for Healthcare: A Review. Future Internet, 15(3). https://doi.org/10.3390/fi15030094

Teo, K. H. (2021). Emerging GaN technologies for power, RF, digital, and quantum computing applications: Recent advances and prospects. Journal of Applied Physics, 130(16). https://doi.org/10.1063/5.0061555

Tu, S. (2021). Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: A novel method based on bifurcation fractal law. Catheterization and Cardiovascular Interventions, 97(Query date: 2024-12-01 09:57:11), 1040–1047. https://doi.org/10.1002/ccd.29592

Umoren, I. J. (2021). Healthcare Logistics Optimization Framework for Efficient Supply Chain Management in Niger Delta Region of Nigeria. International Journal of Advanced Computer Science and Applications, 12(4), 593–604. https://doi.org/10.14569/IJACSA.2021.0120475

Wu, Y. (2022). Erasure conversion for fault-tolerant quantum computing in alkaline earth Rydberg atom arrays. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-32094-6

Yang, L. (2024). Optimization of distribution route of low carbon cold chain logistics in Zhejiang supply and marketing system based on ant colony algorithm. ACM International Conference Proceeding Series, Query date: 2024-12-07 08:38:06, 470–476. https://doi.org/10.1145/3685088.3685171

Yue, F. (2022). Effects of monosaccharide composition on quantitative analysis of total sugar content by phenol-sulfuric acid method. Frontiers in Nutrition, 9(Query date: 2024-12-01 09:57:11). https://doi.org/10.3389/fnut.2022.963318

Zhu, F. (2024). Dung beetle optimization algorithm based on quantum computing and multi-strategy fusion for solving engineering problems. Expert Systems with Applications, 236(Query date: 2024-12-07 00:48:28). https://doi.org/10.1016/j.eswa.2023.121219

Authors

Carlos Pérez
carlosperez@gmail.com (Primary Contact)
Ana Rodríguez
Luis Hernández
Pérez, C., Rodríguez, A., & Hernández, L. (2024). Quantum Computing for Logistics and Supply Chain Optimization. Journal of Tecnologia Quantica, 1(6), 322–331. https://doi.org/10.70177/quantica.v1i6.1703

Article Details