Title: Quantum support vector machine: a novel approach for predicting students' academic performance in higher education

Authors: Iti Batra; Subhranil Som; Pawan Whig

Addresses: Vivekananda Institute of Professional Studies – Technical Campus, New Delhi, India ' Bhairab Ganguly College, Kolkata, West Bengal, India ' Vivekananda Institute of Professional Studies TC, New Delhi, India

Abstract: This research paper investigates the impact of non-intellectual parameters on students' academic growth, emphasising the transformative role of data mining in education. Through psychometric analyses of students' behaviour and learning patterns, we aim to enhance academic performance. Previous studies, employing various mining techniques like neural networks, decision trees, KNN, naïve Bayes, and SVM, have yielded accuracy rates below 89%. In response, our study proposes a novel approach, leveraging the quantum support vector machine (QSVM) for data classification to predict learners' cumulative grade point index (CGPI). Results demonstrate a significant advancement with the radial basis function kernel in the QSVM model, achieving an impressive accuracy of approximately 98%. This breakthrough underscores the superior predictive capabilities of QSVM, marking a substantial improvement over existing methodologies. The findings suggest that QSVM holds great promise in accurately predicting academic performance based on psychological factors, paving the way for enhanced educational outcomes.

Keywords: non-intellectual parameters; academic performance; study habits; data mining; psychometric analyses; educational outcomes; psychological factors; neural networks; decision trees; K-nearest neighbours; KNN; naïve Bayes; support vector machines; SVM; quantum support vector machine; QSVM.

DOI: 10.1504/IJCVR.2026.156206

International Journal of Computational Vision and Robotics, 2026 Vol.17 No.3, pp.396 - 410

Received: 01 Feb 2024
Accepted: 15 May 2024
Published online: 08 Sep 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article