Evaluating the Potential of Quantum Computing for Supply Chain Optimization in SMEs

Abstract

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.