Quantum-Inspired Machine Learning Models for Enhancing Business Intelligence: A Comparative Study

In the evolving landscape of business intelligence (BI), enterprises face challenges in processing large-scale, high-dimensional, and complex data for decision-making. Traditional machine learning (ML) algorithms—though widely used—often struggle with limitations related to feature interaction modeling, training time, and scalability in dynamic business environments. This paper explores the potential of quantum-inspired machine learning models to address these issues and enhance BI outcomes. Quantum-inspired ML techniques leverage mathematical principles from quantum computing—such as superposition, entanglement, and amplitude amplification—to improve pattern recognition and optimization tasks on classical hardware. In this comparative study, we analyze models like Quantum Support Vector Machines (QSVM), QBoost, and Quantum Approximate Optimization Algorithm (QAOA) against conventional algorithms including Random Forest, Logistic Regression, and SVM. Experiments are conducted on real-world datasets from the retail and finance domains, targeting applications such as customer churn prediction, financial risk classification, and fraud detection. The study uses tools like IBM Qiskit, PennyLane, and D-Wave’s Ocean SDK to simulate quantum-enhanced learning on classical systems. Evaluation metrics include accuracy, F1- score, execution time, and model interpretability. Results show that quantum-inspired models offer improved performance, particularly in capturing nonlinear relationships and reducing feature engineering effort. This research contributes to bridging the gap between classical analytics and next-generation quantum computing, offering a forward-looking roadmap for businesses aiming to integrate AI-driven intelligence with quantum-aware architectures.