This chapter explores the potential of quantum machine learning (QML) to enhance demand forecasting in supply chain management. With the increasing complexity and data volumes in modern supply chains, traditional forecasting methods often fall short. QML leverages the principles of quantum mechanics, such as superposition and entanglement, to process data at unprecedented speeds and complexity. The authors discuss theoretical frameworks, focusing on quantum algorithms like quantum support vector machines and quantum neural networks, which can handle high-dimensional data more efficiently than classical counterparts. The chapter evaluates the feasibility, challenges, and prospects of implementing QML in real-world supply chain scenarios, suggesting that QML could significantly improve forecasting accuracy and operational efficiency, ultimately transforming supply chain management practices.
This chapter explores the potential of quantum machine learning (QML) to enhance demand forecasting in supply chain management. With the increasing complexity and data volumes in modern supply chains, traditional forecasting methods often fall short. QML leverages the principles of quantum mechanics, such as superposition and entanglement, to process data at unprecedented speeds and complexity. The authors discuss theoretical frameworks, focusing on quantum algorithms like quantum support vector machines and quantum neural networks, which can handle high-dimensional data more efficiently than classical counterparts. The chapter evaluates the feasibility, challenges, and prospects of implementing QML in real-world supply chain scenarios, suggesting that QML could significantly improve forecasting accuracy and operational efficiency, ultimately transforming supply chain management practices.
This chapter explores the application of quantum machine learning (QML) techniques for demand prediction in supply chain networks. Traditional demand forecasting methods often struggle to capture the intricate dynamics and uncertainties present in modern supply chains. By leveraging the computational power and probabilistic nature of quantum computing, coupled with the flexibility and adaptability of machine learning algorithms, organizations can enhance the accuracy and efficiency of their demand prediction processes. This chapter provides an overview of QML methodologies tailored specifically for demand prediction in supply chain networks, highlighting their advantages over classical approaches. Through case studies and practical examples, the chapter demonstrates how QML can enable organizations to make more informed decisions, optimize inventory levels, and improve overall supply chain performance.
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.
The integration of quantum computing with machine learning emerged as an approach to solving complex computational problems. This chapter provides an overview of quantum machine learning (QML), exploring the fundamental concepts, algorithms, and applications at the intersection of quantum computing and machine learning. We begin by introducing the basic principles of quantum mechanics relevant to QML, including qubits, quantum gates, and quantum parallelism. Then, we explore key topics such as quantum entanglement, superposition, and their role in quantum algorithms. We then discuss the landscape of QML, contrasting classical and quantum approaches and highlighting the potential of quantum computing to enhance machine learning algorithms. Furthermore, we discuss prominent QML algorithms, such as quantum support vector machines (QSVM) and quantum neural networks (QNN), elucidating their mathematical formulations and differences from classical counterparts. Real-world examples showcasing quantum speedup in machine learning tasks are presented to underscore the transformative potential of quantum computing. Finally, an overview of QML tools and frameworks is provided, enabling researchers to embark on their journey in exploring the exciting field of QML.
In an era of global supply chain disruptions, SAP Materials Management (MM) remains critical for procurement, inventory, and logistics optimization. This research pioneers the integration of quantum machine learning (QML) to enhance predictive analytics in SAP MM, addressing challenges like demand volatility and supplier delays. By leveraging quantum algorithms, such as quantum support vector machines (QSVM) and variational quantum circuits (VQC), the proposed framework forecasts inventory needs, detects supply chain risks, and optimizes procurement with unprecedented accuracy. A synthetic dataset of 150,000 MM transactions simulates real-world complexities, including multilingual data and disruptions. The analysis achieves 95% prediction accuracy, reducing stockouts by 65% compared to classical ML methods. Visualizations, including risk heatmaps and performance graphs, guide resilient supply chain strategies. This framework tackles data privacy, computational scalability, and SAP integration, offering a transformative approach to supply chain management in volatile markets.
Purpose The purpose of this study is to explore the potential for quantum computing to enhance applied machine learning tasks. The goal is to investigate whether quantum computing principles such as superposition, entanglement and quantum parallelism can improve predictive accuracy and efficiency in machine learning models. Design/methodology/approach To examine the potential, this study simulated a 4-qubit quantum processor on an Intel Core i5 8th generation central processing unit (CPU). The proposed model combines quantum principle and deep neural networks architecture involving quantum-forward and classical-backward processes; quantum part is in the forward pass, and the traditional neural network handles the backpropagation during model training. This model was tested using a real dataset to assess its practical applicability to machine learning tasks under real-world constraints. Findings The findings reveal the effectiveness of the quantum-classical hybrid approach which achieves an average area under the curve score of 92%, with a precision of 22.6% and a recall of 25.3%. These results suggest that, while quantum noise and Qubit limitations inherent to Noisy Intermediate-Scale Quantum systems impacted prediction accuracy and model generalizability, the quantum-forward process enhanced the model’s ability to identify true positive cases in a complex dataset. Compared to traditional approaches, this model addresses both the stability and predictive demands of highly imbalanced inventory data, although limitations remain in precision and scalability. Originality/value While many open questions remain about the real advantages of quantum computing in applied field of business, this study contributes to the feasibility of leveraging quantum machine learning for complex decision-making problems in supply chain management.
Quantum Machine Learning (QML) is an emerging discipline at the intersection of quantum computing and artificial intelligence, providing new approaches to address the increasing computational and analytical challenges in healthcare. This chapter explores how quantum principles, such as superposition, entanglement, and interference, can enhance the efficiency and accuracy of machine learning when applied to complex biomedical data. It presents an overview of the fundamental concepts of QML, compares its capabilities with those of traditional machine learning systems, and examines important algorithmic frameworks, including Quantum Support Vector Machines, Quantum Neural Networks, and Variational Quantum Algorithms. Practical examples are explored across several healthcare domains—drug discovery, medical imaging, genomic research, patient monitoring, and hospital resource management—showing how quantum methods are beginning to improve data interpretation and clinical decision-making. The chapter also outlines current limitations related to hardware capacity, data preparation, interpretability, and regulatory standards, while emphasizing ethical and cybersecurity considerations that accompany quantum-enabled systems. Finally, it looks ahead to a future in which accessible quantum computing, open-source development, and collaboration among scientists, clinicians, and technologists drive a new generation of personalized, secure, and data-driven healthcare.