AI-Infused Quantum Machine Learning for Enhanced Supply Chain Forecasting

作者: Leela Manush Gutta , Balaji Dhamodharan , Pushan Kumar Dutta , Pawan Whig

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摘要: In the rapidly evolving landscape of supply chain management, the fusion of artificial intelligence (AI) and quantum machine learning (QML) holds immense promise for optimizing forecasting processes. This chapter delves into the synergistic potential of AI and QML techniques, presenting a comprehensive framework for harnessing their combined power in supply chain forecasting. By leveraging AI's ability to analyze vast datasets and extract meaningful insights alongside QML's capacity to process complex probabilistic distributions, organizations can achieve unprecedented accuracy in demand forecasting, inventory optimization, and risk mitigation. Through case studies and practical applications, this chapter elucidates how AI-infused QML models can revolutionize supply chain forecasting, driving efficiency, resilience, and competitiveness in the global marketplace.

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