Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines principles of quantum computing with classical machine learning techniques to solve complex computational problems more efficiently. With quantum algorithms promising exponential speed-ups for certain tasks, QML has the potential to revolutionize domains ranging from optimization and cryptography to drug discovery and artificial intelligence. This paper provides a comprehensive review of QML, exploring its foundational concepts, algorithmic advancements, hybrid quantum-classical architectures, and potential applications. Furthermore, the paper discusses current challenges, such as noise in quantum systems, scalability, and resource limitations, along with future research directions. Illustrative tables and figures are included to clarify the workflow and advantages of QML over classical methods.

Keywords: Quantum Machine Learning, Quantum Computing, Hybrid Algorithms, Variational Quantum Circuits, Quantum Neural Networks, Quantum Optimization