Globalization, fluctuating demand, and the need for real-time responses have stretched global supply chains to their limits, which the traditional systems cannot handle. Quantum computing, through superposition, entanglement, and parallelism, offers transformational solutions to route optimization, inventory balancing, demand forecasting, and risk mitigation. Advanced quantum algorithms unlock unprecedented processing of vast datasets, predictive precision, and unparalleled efficiency in decision-making. However, high costs, limited access, and integration issues hamper this adoption. This chapter, therefore, focuses on how these challenges can be overcome through collaboration among technology providers, logistics experts, and academia. It follows with a proposed roadmap for integrating quantum computing into supply chain systems, followed by practical use cases and future research directions.
Abstract This chapter examines the growing convergence of AI and quantum computing in the context of global trade and economics, illustrating how novel predictive modeling techniques enable more accurate market forecasts and smarter policy interventions. Through an exploration of machine learning, deep learning, and quantum-enhanced optimization, readers gain insight into cutting-edge strategies for managing trade flows, reinforcing supply chain resilience, and resolving international trade disputes. The chapter highlights real-world applications, ethical considerations, and future research directions, ultimately positing that the synergy of AI and quantum technologies will pave the way for more equitable, transparent, and agile economic systems. Keywords: AI, quantum computing, predictive modeling, global trade, economics, deep learning, machine learning, policy optimization, supply chain, trade disputes, international economics, economic forecasting, future research, equitable systems, transparency.
Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. However, traditional machine-learning models struggle with large-scale datasets and complex relationships, hindering real-world data collection. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of handling large datasets. Our proposed model, QAmplifyNet, employs quantum-inspired techniques within a quantum-classical neural network to predict backorders effectively on short and imbalanced datasets. Experimental evaluations on a benchmark dataset demonstrate QAmplifyNet's superiority over classical models, quantum ensembles, quantum neural networks, and deep reinforcement learning. Its proficiency in handling short, imbalanced datasets makes it an ideal solution for supply chain management. To enhance model interpretability, we use Explainable Artificial Intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet seamlessly integrates into real-world supply chain management systems, enabling proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, providing superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management.