A Quantum Support Vector Machine-Based Approach for Customer Churn Prediction

Abstract

Customer churn prediction is important in the banking sector for improving customer retention and reducing revenue loss. Traditional Support Vector Machines (SVM) face scalability challenges when processing high-dimensional and non-linear customer data. This study proposes a Quantum Support Vector Machine (QSVM) for churn prediction by combining classical preprocessing with quantum machine learning. The data is cleaned, encoded, scaled, and reduced using Principal Component Analysis (PCA). The resulting features are encoded into quantum states, and a quantum kernel is used for classification. The model is implemented using Python and Qiskit and evaluated on the Qiskit Aer simulator. Performance is assessed using accuracy, precision, recall, and F1-score. The proposed QSVM achieves approximately 80% accuracy, demonstrating strong non-churn prediction and moderate churn identification. The results indicate the potential of quantum machine learning for banking churn prediction. Keywords : Customer Churn Prediction, Quantum Support Vector Machine (QSVM), Quantum Machine Learning, Banking, Principal Component Analysis (PCA), Quantum Kernel, Qiskit, Qiskit Aer, Machine Learning, Customer Retention