A Review of Quantum Machine Learning: Bridging Quantum Computing and Artificial Intelligence

Abstract

Quantum machine learning (QML) is an emerging interdisciplinary field that seeks to harness the power of quantum computing for AI applications. This review explores key quantum algorithms for machine learning, including quantum support vector machines, quantum neural networks, and quantum-enhanced optimization techniques. We compare the advantages and limitations of QML over classical machine learning, focusing on computational speed-ups and scalability. The paper also discusses the current state of quantum hardware, algorithmic challenges, and potential applications in drug discovery, cryptography, and financial modeling. We conclude with an outlook on future research directions in QML.