Logistics involves managing how resources are acquired, stored, and transported to their final destinations along the supply chain in a cost-effectively manner. Logistics problems are NP-hard combinational optimization problems involving searching for the best solution in a large solution space. Recent studies have demonstrated the capability of quantum methods to solve such combinatorial optimization problems. Consequently, this paper presents the application of quantum computing methods for two real-world logistics problems, namely, a) Employee transport route optimization and b) Agri-tech logistics optimization. The main goal of this paper is to present the evaluation of the results of the experiments executed on quantum gate-based variational algorithms and quantum annealers. Given the current limitation of quantum hardware, we had to simplify the problem and use case specific decomposition techniques such as distance based clustering to reduce the search space and size of the problem before executing them on quantum computers. The method has been elaborated in this paper. The results were benchmarked with classical exact solvers.
Logistics involves managing how resources are acquired, stored, and transported to their final destinations along the supply chain in a cost-effectively manner. Logistics problems are NP-hard combinational optimization problems involving searching for the best solution in a large solution space. Recent studies have demonstrated the capability of quantum methods to solve such combinatorial optimization problems. Consequently, this paper presents the application of quantum computing methods for two real-world logistics problems, namely, a) Employee transport route optimization and b) Agri-tech logistics optimization. The main goal of this paper is to present the evaluation of the results of the experiments executed on quantum gate-based variational algorithms and quantum annealers. Given the current limitation of quantum hardware, we had to simplify the problem and use case specific decomposition techniques such as distance based clustering to reduce the search space and size of the problem before executing them on quantum computers. The method has been elaborated in this paper. The results were benchmarked with classical exact solvers.
Current world trade is based and supported in a strong and healthy supply chain, where logistics play a key role in producing and providing key assets and goods to keep societies and economies going. Current geopolitical and sanitary challenges faced in the entire world have made even more critical the role of logistics and increased demands for tuning transport function to keep the supply chain up and running. The challenge is only increasing and growing for the future, thus tackling transport optimization provides both business and social value. Optimization problems are ubiquitous and they present a challenge due to its complexity, where they're typically NP-hard problems. Quantum Computing is a developing field, and the Quantum Annealing approach has proven to be quite effective in its applicability and usefulness to tackle optimization problems. In this work we treat the Vehicle Routing Problem, which is also a variation of a famous optimization problem known as the Traveling Salesman Problem. We aim to tackle the vehicle optimization problem from the last mile logistic scenario application, with a perspective from the classical and quantum approaches, and providing a solution which combines both, also known as hybrid solution. Finally, we provide the results of the analysis and proposal for the consideration of applications in a near term business case scenario.
Supply chain and logistics operations involve combinatorial optimisation problems -- vehicle routing, warehouse assignment, inventory scheduling, and network flow -- whose complexity scales exponentially with problem size, making exact classical solvers impractical for real-world instances. Quantum optimisation algorithms offer potential speedups through quantum parallelism and interference. This paper proposes the Quantum Supply Chain Optimisation Framework (QSCOF), evaluating QAOA, VQE, and quantum annealing across five canonical logistics problems on NISQ hardware and fault-tolerant models. Evaluated on 20 benchmark instances spanning 20-200 nodes, QSCOF demonstrates QAOA achieving 93.8% solution quality for 50-node vehicle routing on current hardware, quantum annealing delivering 28x speedup for 100-node warehouse assignment, and a Quantum Logistics Utility Index (QLUI) of 0.871. Quantum advantage thresholds and a phased adoption roadmap are provided.
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 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.
In planning and scheduling, large-scale optimization problems often have complex limits and a lot of factors, which makes them hard to answer with standard methods. Quantum annealing has become a hopeful way to deal with these problems because it can quickly look into a lot of possible solutions. This essay looks into how quantum annealing might be used to solve optimization problems in transportation and scheduling, with a focus on how it could be used for route planning, inventory management, and arranging workers. To do these optimization jobs, quantum annealing is used. This is a quantum computing method that gets the global minimum of a cost function. We use the D-Wave quantum annealer in this method. It turns the problem's limits and objective function into a quadratic unconstrained binary optimization (QUBO) model. The quantum annealer then works on this model to find the best answers. The suggested method combines traditional preparation methods with quantum annealing to get the best results for big schedule and transportation issues. We use quantum annealing to solve difficult multi-objective optimization problems, like reducing shipping time, cost, and resource use all at the same time. Preliminary results show that quantum annealing can make solutions much better and use computers much more efficiently than traditional methods for optimizing supplies and schedules. Quantum annealing works especially well with problems that have a lot of dimensions. It can provide scalable answers that sometimes work better than standard methods.
In the current competitive industrial environment, the study of NP-hard issues is becoming more and more significant. In logistics, the Vehicle Routing Problem (VRP) is one of the popular challenges, especially in cold chain logistics, which requires the management of perishable goods. This problem involves identifying the optimal paths for vehicles responsible for transporting goods to different locations. Due to its combinatorial nature and the complexity of finding optimal solutions, it is identified as an NP-hard problem that requires an unreasonable time frame to solve. Recently, quantum computing has proven to be a promising approach for addressing some of the complex challenges. Among quantum methodologies, quantum annealing (QA) has demonstrated the potential to accelerate computation times in solving optimization problems. By leveraging the principles of quantum tunneling and superposition, QA can explore solution spaces more efficiently. Therefore, this study explores the application of QA to address VRP in cold chain logistics, aiming to develop solutions that minimize travel distances during deliveries. The case study demonstrates that QA can achieve, on average, at least 85% of optimal performance while providing approximately 95% faster run times compared to classical genetic algorithms (GA). This contributes to greater efficiency in cold chain logistics operations.