Quantum computing is expected to have transformative influences on many domains, but its practical deployments on industry problems are underexplored. We focus on applying quantum computing to operations management problems in industry, and in particular, supply chain management. Many problems in supply chain management involve large state and action spaces and pose computational challenges on classic computers. We develop a quantized policy iteration algorithm to solve an inventory control problem and demonstrative its effectiveness. We also discuss in-depth the hardware requirements and potential challenges on implementing this quantum algorithm in the near term. Our simulations and experiments are powered by IBM Qiskit and the qBraid system.
Quantum computing is expected to have transformative influences on many domains, but its practical deployments on industry problems are underexplored. We focus on applying quantum computing to operations management problems in industry, and in particular, supply chain management. Many problems in supply chain management involve large state and action spaces and pose computational challenges on classic computers. We develop a quantized policy iteration algorithm to solve an inventory control problem and demonstrative its effectiveness. We also discuss in-depth the hardware requirements and potential challenges on implementing this quantum algorithm in the near term. Our simulations and experiments are powered by IBM Qiskit and the qBraid system.
Quantum computing is poised to revolutionize various fields, yet its practical applications in industry-specific problems remain relatively unexplored.This discussion focuses on leveraging quantum computing to address challenges in operations management, particularly within supply chain management.Supply chain management often involves navigating extensive state and action spaces, presenting significant computational hurdles for classical computers.To address this, we develop a quantum policy iteration algorithm tailored for solving inventory control problems, demonstrating its efficacy through simulations.Additionally, we delve into the hardware prerequisites and potential obstacles for implementing this quantum algorithm in the near future.Our research leverages IBM Qiskit and the qBraid system for simulations and experimental validation.
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 research explores the application of quantum computing to manufacturing and supply chain optimization in an effort to increase productivity, reduce costs, and improve product quality. Quantum algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA), are developed and evaluated to solve complex optimization problems in these domains. Quantum computing approaches are contrasted with traditional optimization techniques to demonstrate the potential advantages of quantum algorithms in terms of solution quality and working time efficiency. Practical implementation considerations of data availability, algorithm scalability, and system integration are also discussed. This research shows that quantum algorithms can effectively optimize production scheduling, resource allocation, and supply chain management, resulting in shorter production schedules and improved operational performance. This research recognizes the limitations of current quantum hardware, the complexity of the problem domain, and the difficulty of implementation. Despite these limitations, this research lays the foundation for further investigation and innovation in quantum computing for manufacturing and supply chain optimization, highlighting the potential for long-term transformative effects on industrial operations.
The technology known as quantum computing has shown great promise and the ability to completely transform a number of industries, including dynamic allocation and inventory management. The use of quantum computing in these fields is examined in this work, with an emphasis on how well it can solve challenging optimisation problems in comparison to traditional computer techniques. It goes over how supply chain effectiveness, cost reduction, and inventory optimisation can all be achieved with quantum algorithms. The constraints and difficulties of integrating quantum computing into dynamic allocation and inventory management are also covered in the chapter, along with the requirement for specialised hardware and knowledge. The chapter's overall thesis is that, while quantum computing has great promise for improving inventory management and dynamic allocation procedures, further study and development are required to fully reap these advantages.
Background: This paper investigates hybrid quantum–classical optimization approaches for addressing core supply chain management (SCM) problems. A unified hybrid framework is implemented and evaluated across five representative domains: vehicle routing, scheduling, facility location, inventory optimization, and demand forecasting. Methods: The framework integrates quantum algorithms—namely the Quantum Approximate Optimization Algorithm (QAOA), Quantum Annealing (QA), and the Variational Quantum Eigensolver (VQE)—with classical constraint-handling and local refinement procedures in an iterative workflow. Quantum solvers are employed for global solution exploration, while classical optimization ensures feasibility and convergence stability. Results: Experiments conducted on standardized synthetic benchmarks demonstrate that the proposed hybrid framework consistently outperforms classical-only and quantum-only baselines, achieving 12–18% reductions in operational costs and 20–35% faster convergence. In routing and fulfilment tasks, quantum-generated candidate solutions provide effective warm starts for classical refinement. Robustness analysis based on stochastic SCM simulations further indicates lower performance variance under uncertainty. Conclusions: These results demonstrate that hybrid quantum–classical optimization constitutes a practical and scalable strategy for near-term SCM decision-making under current Noisy Intermediate-Scale Quantum (NISQ) hardware constraints.
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.