UNVEILING QUANTUM PRECISION: FEATURE SELECTION DYNAMICS IN MACHINE LEARNING

Abstract

The intersection of quantum computing and machine learning has sparked interest in harnessing the power of quantum mechanics to enhance various aspects of traditional machine learning algorithms. This paper explores the application of quantum computing in feature selection, a crucial step in the machine learning pipeline. We present a comprehensive study of existing methodologies, propose a novel quantum-enhanced feature selection system, and provide empirical results demonstrating the efficacy of our approach.