UR-044 Quantum Machine Learning for Science and Engineering

This research explores the comparative effectiveness of traditional machine learning algorithms and their quantum counterparts. Traditional and quantum implementations of algorithms including Support Vector Machines (SVM), logistic regression, Principal Component Analysis (PCA), random forest classifiers, neural networks, and convolutional neural networks (CNN) are evaluated and contrasted. Findings highlight that quantum algorithms can provide certain clear advantages in some models and data while exhibiting inferior performance in others. By assessing these nuances, this research helps contribute to the understanding of quantum machine learning algorithms and their potential applications for science, engineering, and industrial tasks.