Quantum Classifiers

In recent years, researchers are looking into data transformations in the quantum information space to see whether they may improve robustness and performance. The evolution of quantum mechanics occurred because it could explain specific scenarios in which conventional formulas failed. As a result, it began to expand in analytical research domains such as machine learning, and it is now capable of functioning correctly, and in some circumstances better than classical machine learning. Classification is one of the crucial areas in Machine Learning (ML), and quantum classification analysis has started to gain prominence. In this paper, we focus on implementing four classification algorithms such as Support Vector Classification with Quantum Kernel (SVCQK), Quantum Support Vector Classifier (QSVC), Variational Quantum Classifier (VQC), and Circuit Quantum Neural Network Classifier (CQNNC). We also provide analytical results of case studies with various generated classifiable and semi-classifiable datasets. This study is to determine whether quantum information theory may shorten learning time or improve convergence when compared to traditional approaches.