Quantum Machine Learning: Bridging the Gap Between Quantum Computing and Artificial Intelligence: An Overview

Abstract

Abstract: Quantum Machine Learning (QML) at the intersection of quantum computing and artificial intelligence (AI) is explored, emphasizing its role in connecting these domains. The transformative potential of QML in enhancing classical machine learning and the introduction of the Variational Quantum Classifier (VQC) algorithm (Ref. 4) are highlighted. Fundamental quantum principles, quantum feature maps, and the VQC's use of parameterized quantum circuits are discussed (Refs. 1, 3). The paper addresses practical implementation, optimization techniques, and the VQC's performance through empirical evaluations (Ref. 4). Implications of QML extend to diverse applications (Ref. 5), positioning it as a bridge between quantum computing and AI to unlock transformative possibilities.