The work explores the integration of quantum computing into logistics and supply chain management, emphasising its potential for use in complex optimisation problems. The discussion introduces quantum computing principles, focusing on quantum annealing and gate-based quantum computing, with the Quantum Approximate Optimisation Algorithm and Quantum Annealing as key algorithmic approaches. The paper provides an overview of quantum approaches to routing, logistic network design, fleet maintenance, cargo loading, prediction, and scheduling problems. Notably, most solutions in the literature are hybrid, combining quantum and classical computing. The conclusion highlights the early stage of quantum computing, emphasising its potential impact on logistics and supply chain optimisation. In the final overview, the literature is categorised, identifying quantum annealing dominance and a need for more research in prediction and machine learning is highlighted. The consensus is that quantum computing has great potential but faces current hardware limitations, necessitating further advancements for practical implementation.
The work explores the integration of quantum computing into logistics and supply chain management, emphasising its potential for use in complex optimisation problems. The discussion introduces quantum computing principles, focusing on quantum annealing and gate-based quantum computing, with the Quantum Approximate Optimisation Algorithm and Quantum Annealing as key algorithmic approaches. The paper provides an overview of quantum approaches to routing, logistic network design, fleet maintenance, cargo loading, prediction, and scheduling problems. Notably, most solutions in the literature are hybrid, combining quantum and classical computing. The conclusion highlights the early stage of quantum computing, emphasising its potential impact on logistics and supply chain optimisation. In the final overview, the literature is categorised, identifying quantum annealing dominance and a need for more research in prediction and machine learning is highlighted. The consensus is that quantum computing has great potential but faces current hardware limitations, necessitating further advancements for practical implementation.
Quantum computing presents a transformative opportunity for optimizing supply chain and logistics operations by addressing complex combinatorial challenges beyond the capabilities of classical computing. Traditional supply chain optimization relies on heuristic and mathematical models, which struggle with large-scale problems such as vehicle routing, warehouse management, demand forecasting, and last-mile delivery. Quantum computing, leveraging qubits and principles like superposition and entanglement, enables exponential computational power and efficiency. This paper explores quantum computing's applications in logistics, including quantum annealing for route optimization, fleet management, and supply chain network design. Companies like IBM, Google, and D-Wave are advancing quantum algorithms, with hybrid quantum-classical approaches emerging as practical solutions. While large-scale commercial adoption is still in its infancy, research demonstrates promising results in reducing operational costs, improving sustainability, and enhancing decision-making. The study synthesizes over 80 published works on quantum computing's role in logistics, providing insights into emerging technologies, challenges, and future potential. As the field progresses, early adoption and pilot programs will be crucial for businesses to gain a competitive edge in an increasingly complex global supply chain landscape. Quantum computing has the potential to redefine efficiency, cost reduction, and real-time adaptability in logistics and supply chain management.
The paper explores the current application and employments of Quantum Computing in Supply Chain Management (SCM) and its operations. The importance of quantum in the aspect of Supply chain is given also how it may complement the existing established classical methods. They include routing problem, maintenance model, and scheduling and organisation problems that come under the broad domain of supply chain management. Also, such conditions and requirements as long and short term supply chains with the emphasis on the healthcare sector are discussed. A literature review of the current works has been carried out and methods like QAOA, annealing by QUBO as well as stimulated annealing were investigated. The objective is to establish efficient that allows finding the solutions of diverse optimization challenges avoiding the existing limitations within this novel area, in which the state of quantum hardware does not seem to pose a problem; thus contributing to further research along with refinements.
The intersection of quantum computing with machine learning transforms the various effective technologies. It works with the integration of quantum computing into logistics through supply chain management to solve and use optimization problems. These technologies have the capability of data pre-processing and analysing logistics through various machine learning models. This chapter explores the various quantum computing principles that are used to solve complex problems. This chapter discussed quantum annealing, quantum applications, and quantum optimisation algorithms by key factor algorithms based on the different ML approaches used. It is based on fleet maintenance, loading cargo, and prediction with scheduling algorithms. Quantum computing, when hybridized with ML and logistic optimisation, could solve the problems related to classical computing problems in this chapter. In this proposed work, the hybrid approach with ML and quantum computing with enhanced industry-specific solutions in different aspects of quantum computing in sectors such as cybersecurity. In logistics, the optimization concepts help to define the problems through a hybridization method with their applications.
Abstract The quantum supply chain transforms logistics and supply chain management by harnessing quantum technologies to tackle longstanding challenges in optimization, resilience, and real-time decision-making. Through qubits and superposition, quantum computers deliver exponential speedups for complex problems including route optimization, unit load device (ULD) and pallet configuration, disruption management, and large-scale predictive modeling. This paper investigates the integration of quantum computing—especially quantum annealing—into supply chain operations. It highlights practical applications such as rapid multi-source data analysis, dynamic alternative routing during disruptions, and greater efficiency in high-velocity logistics. Grounded in established resilience frameworks and emerging technologies, the research illustrates how quantum solutions can significantly cut costs, boost adaptability, and strengthen global supply chain robustness. Drawing from studies on resilience strategies, automotive battery advancements, and intelligent transportation systems, this work positions quantum computing as a vital enabler for next-generation logistics while discussing opportunities, limitations, and the shift from theoretical promise to real-world implementation. Keywords Quantum Computing, Quantum Supply Chain; Logistics Optimization; Quantum Annealing; Supply Chain Resilience; Hybrid Quantum-Classical Algorithms; Unit Load Device Optimization; Disruption Management Quantum Routing.
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, which has the ability to solve the most challenging logistics and supply chain management problems, is the most fascinating advancement in computation for the ensuing ten years. The current state of quantum computing is reviewed in this study, along with recommendations for further research. The transformational potential of quantum 68 computing is explored in this study within the framework of supply chain management optimization. The necessity for cutting-edge computational tools grows more and more important as supply networks get more complex. With its capacity to analyze huge datasets and resolve challenging optimization issues, quantum computing presents a promising way to transform supply chain operations. This study examines how quantum computing might improve supply chain efficiency, address the complex problems associated with contemporary supply chains, and provide significant value to both enterprises and consumers. First, fundamental ideas related to quantum computing and computers are introduced. The dominant quantum technologies are then described. The third section includes instances of contemporary supply chain management and logistical advancements as well as a study of the quantum sector. Fourth, suggestions for further investigation are given. This review intends to inform and motivate the application of quantum computing to supply chain and logistics optimisation, machine learning, and artificial intelligence.