Abstract

Quantum machine learning (QML) is emerging as a transformative technology for business analytics, offering novel approaches to complex classification and prediction problems. This research seminar presents a practical introduction to quantum kernel methods and their applications in management science, designed specifically for economists and business researchers without requiring a physics background. We introduce the fundamental concepts of quantum computing and quantum kernels as advanced similarity measures, demonstrating how they can capture complex, nonlinear patterns in business data that traditional machine learning methods often miss. Our empirical study using Quantum Support Vector Machines (Q-SVM) achieves 86.09% recall, 76.47% precision, and 0.83 AUC in customer classification tasks, representing an 18% improvement over classical approaches. The seminar covers four key business applications: customer churn prediction, marketing campaign targeting, supply chain and operations research, and financial risk assessment. We also explore emerging QML models including Variational Quantum Classifiers (VQC), Quantum Approximate Optimization Algorithm (QAOA), Quantum Neural Networks (QNN), and Quantum Reservoir Computing (QRC), highlighting their potential for portfolio optimization, demand forecasting, and fraud detection. A practical implementation roadmap is provided, emphasizing that organizations can begin experimenting today using free simulation tools such as IBM Qiskit, PennyLane, and cloud-based quantum services. We conclude with cost-benefit considerations and strategic recommendations for business leaders seeking to build quantum literacy and gain competitive advantage as this technology matures.