Machine Learning in the Quantum Age: Classical vs. Quantum Algorithms Using Iris Data for Accuracy Analysis

Abstract

Quantum-enhanced techniques are widely employed to address machine learning difficulties. This work compares Support Vector Classifier (SVC) with Variational Quantum Classifier (VQC) on the Iris dataset, a typical machine learning benchmark. This study compares the performance, accuracy, and efficiency of two approaches: classical computing and quantum computing. The paper emphasizes the positive and negative aspects of each method, as well as practical data demonstrating quantum computing's potential for bettering machine learning tasks. This comparison offers helpful perspectives into the practical applications of quantum algorithms, providing an enhanced awareness of quantum machine learning's abilities and possibilities.