Speaker
Hala Siddig Mohamed Elhag
(CQTA (Centre f. Quantum Techno. a. Application))
Description
In recent years, machine learning has emerged as a prominent subfield of artificial intelligence, finding widespread applications in various scientific domains. Concurrently, the promising potential of quantum computing has inspired the exploration of leveraging its computational advantages to construct quantum machine learning models. Building upon the remarkable success of classical Convolutional Neural Networks (CNNs) in image classification tasks, Quantum Convolutional Neural Networks (QCNNs) were introduced with the aim of surpassing their classical counterparts. In this study, we employ a QCNN model for the task of classifying jet images and rigorously assess its performance in comparison to a corresponding classical CNN model.
