Implementation of Quantum Machine Learning on Educational Data

This study is the first to implement quantum machine learning (QML) on educational data to predict alumni results. This study aims to show that we can design and implement QML algorithms for this application case and compare their accuracy with those of classical ML algorithms. We consider three target variables in a high-dimensional dataset with approximately 100 features and 25,000 instances or samples: whether an alumnus will secure a CEO position, alumni salary, and alumni satisfaction. These variables were selected because they provide insights into the effect of education on alumni careers. Due to the computational limitations of running QML on high-dimensional data, we propose to use principal component analysis for dimensionality reduction, a barycentric correction procedure for instance reduction, and two quantum-kernel ML algorithms for classification, namely quantum support vector classifier (QSVC) and Pegasos QSVC. We observe that currently one can implement quantum-kernel ML algorithms and achieve results comparable to those of classical ML algorithms. For example, the accuracy of the classical and quantum algorithms is 85% in predicting whether an alumnus will secure a CEO position. Although QML currently offers no time or accuracy advantages, these findings are promising as quantum hardware evolves. © 2025 by SCITEPRESS ¿ Science and Technology Publications, Lda.

Implementation of Quantum Machine Learning on Educational Data Academic Article in Scopus