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.
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.
The accelerating complexity of global supply chains, particularly among small and medium-sized enterprises (SMEs), presents challenges that exceed the capabilities of conventional computational models. Quantum computing, with its inherent ability to process vast solution spaces through superposition and entanglement, offers a transformative approach to supply chain optimization. This study evaluates the feasibility and potential impact of applying quantum and hybrid quantum–classical algorithms—such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing—to logistics scheduling, demand forecasting, and inventory management in SMEs. It critically analyzes algorithmic efficiency, scalability, and cost–benefit ratios within limited computational infrastructures. The research integrates insights from operations research, quantum algorithm design, and management science to identify viable entry points for SME adoption. Findings suggest that while quantum computing remains in the NISQ (Noisy Intermediate-Scale Quantum) stage, near-term applications through cloud-based hybrid platforms could significantly enhance decision accuracy, resource allocation, and resilience against supply chain disruptions.
This investigation gives introduction of 'PRABAX' Quantum Supply Chain Optimization Model, using quantum optimization techniques in decision making processes of supply chains. By using the combination of the latest developments of quantum computing with operational practices for routing, scheduling, inventory, demand forecasting, the model covers the prevalent challenges relating to scalability, uncertainty and resilience. A systematic literature review based on PRISMA guidelines for synthesising both classical and quantum optimisation strategies. The study introduces Quantum Annealing (QA), Quantum Approximate Optimization Algorithm (QAOA), Quantum Machine Learning (QML), and Hybrid Quantum - Classical Methodology into a coherent concept. To capture the dynamics of the context, such issues as mediating variables such as the choice of algorithm and refining a hybrid model and of moderating factors such as the maturity of hardware and data integrity, and the integration constancy are investigated. The implementation of QE within the 'PRABAX' framework ensures the improvement of the resilience of the supply chains, improve forecasting accuracy, and reduce the computational efficiency. Empirical demos suggest that hybrid workflows that incorporate quantum capabilities with the practical constraints of current available hardware provide the most feasible short term trajectory. The proposed framework helps managers and policy makers to understand the effective adoption pathways. It advises a phased implementation approach with initial short routinely hybrid pilot initiatives, followed by the medium-term system integration, and long run transition to full quantum native functioning. The 'PRABAX' model is both an extension to the theory of optimisation and a combination of quantum concepts, which places the idea of hybrid frameworks as an emerging paradigm in the field of supply chain literature. It bridges the gap between developments in computational science to established theories and models about resilience, dynamic capabilities and resource-based views, and represents quantum optimisation as an important strategic asset. This study represents the first fully established framework for the optimisation of quantum enabled supply chains that clearly outlines the hypotheses that can be tested as well as clear strategies for their adoption, helping to guide future academic research and practices.
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 for addressing complex optimization challenges in logistics and supply chain management.This paper explores the practical applications of quantum annealing and hybrid quantum-classical algorithms within a stratified architecture spanning physical hardware, algorithmic middleware, and decision-support layers.We demonstrate how these technologies enable significant improvements in unit load device (ULD) and pallet configuration, dynamic routing under disruption, and real-time multi-objective optimization.Through analysis of real-world industry pilots, including DHL's quantum-assisted route optimization achieving substantial carbon emission reductions, Volkswagen's fleet efficiency improvements, and hybrid quantum solutions at major ports such as the Port of Los Angeles, we show measurable gains in operational efficiency, cost reduction, payload utilization, and supply chain resilience.The study integrates these advancements with established resilience frameworks and the author's prior contributions in supply chain resilience, automotive battery technologies, and intelligent transportation systems.While hardware limitations in the noisy intermediate-scale quantum (NISQ) era persist, hybrid approaches provide a viable pathway for near-term adoption.This work highlights the transition of quantum technologies from theoretical promise to practical deployment, offering a roadmap for more efficient, resilient, and sustainable global supply chains.Future directions include scaling hybrid systems and expanding applications across multi-modal logistics networks
In this paper, we investigate a quantum approach to solve hard optimization problems of logistics, such as vehicle routing, warehouse scheduling, supply chain planning, etc. Employing quantum computation models, viz., Quantum Approximate Optimization Algorithm (QAOA) as well as hybrid quantum-classical computing framework, the proposed technique overcomes problems of scalability, constraint handling, and performance issues of previous work. The research demonstrates benchmarking on benchmarks, on logistics datasets, and across quantum platforms. We establish that the solution quality and the convergence efficiency were improved by considering the quantum annealing effect, showing that quantum computing is a revolutionary playing card in optimization for logistics.
Transportation, supply chainmanagement and energy systems involveoptimization problems that become verydifficult to solve as their scale and complexityincrease. This review examines three quantumcomputing approaches for addressingthese problems: the Quantum ApproximateOptimization Algorithm (QAOA), QuantumAnnealing (QA) and the Variational QuantumEigensolver (VQE). These quantum algorithmsapply the principles of quantum computing tosolve optimization problems more efficientlythan many classical methods. Quantumcomputing can be applied to tasks such asvehicle routing, facility location selection,production scheduling, inventory management,demand forecasting, warehouse operationoptimization, load balancing and energydistribution optimization. This review alsodiscusses the challenges that currently limitthe practical use of quantum computing andquantum algorithms, including quantum noise,limited qubit availability and maintainingsolution accuracy. Although quantumoptimization remains an emerging field,the studies reviewed show that it has strongpotential to address large-scale problems intransportation, supply chain management andenergy systems. Future research should focuson validating these methods using real-worlddata, comparing different quantum computingplatforms and evaluating their performance inlogistics, transportation, manufacturing andenergy systems to further advance quantumoptimization and quantum computing.