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Interpretable Quantum Least Square,One-Class Support Vector Machine: Applications to Banking and Cyber Security

Rohan Potukuchi, Vadlamani Ravi

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.2139/ssrn.7383925

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Cyber-physical control systems are critical infrastructures designed around highly responsive feedback loops that are measured and manipulated by hundreds of sensors and controllers. Anomalous data, such as from cyber-attacks, greatly risk the safety of the infrastructure and human operators. With recent advances in the quantum computing paradigm, the application of quantum in anomaly detection can greatly improve identification of cyber-attacks in physical sensor data. In this paper, we explore the use of strong pre-processing methods and a quantum-hybrid Support Vector Machine (SVM) that takes advantage of fidelity in parameterized quantum circuits to efficiently and effectively flatten extremely high dimensional data. Our results show an F-1 Score of 0.86 and accuracy of 87% on the HAI CPS dataset using an 8-qubit, 16-feature quantum kernel, performing equally to existing work and 14% better than its classical counterpart.
Ensemble Learning, a technique that combines multiple base models to enhance performance and reduce variance, is a well-established approach in machine learning. In recent years, quantum machine learning has emerged as a field aiming to accelerate classical machine learning through quantum algorithms. In this paper, we introduce a quantum ensemble learning framework, with a particular focus on utilizing the parallel amplitude amplification (parallel AA) technique when employing a quantum support vector machine (QSVM) as the base learner. This approach reduces complexity compared to classical ensemble techniques, making it a promising advancement in the field of quantum machine learning. It demonstrates significant improvements in time complexity and maintains competitive accuracy when compared to classical bagging, including support vector machine (SVM) and least squares support vector machine (LS-SVM).
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