Abstract:

Regarding rising global complexity and demand volatility, conventional supply chain optimization strategies are running computational constraints. The transforming possibilities of hybrid quantum computing—a synergistic merger of classical and quantum computing—to address challenging supply chain problems with hitherto unheard-of speed and accuracy are investigated in this work. We propose a hybrid model using quantum algorithms including the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) for solving NP-hard problems including dynamic routing, inventory optimization, and demand forecasting while leveraging classical algorithms for data preparation and logistics planning. By means of simulations and case studies, the model shows notable gains in optimization accuracy, computing speed, and flexibility to real-time changes. Particularly in cases involving big datasets and high-dimensional optimization, results show that hybrid quantum approaches outperform classical-only methods. This work opens the path for more robust, flexible, and efficient worldwide logistics networks by establishing hybrid quantum computing as a viable horizon for next-generation supply chain systems.