Quantum Machine Learning Unveiled: A Comprehensive Review

Quantum Machine Learning (QML) is a burgeoning field at the convergence of quantum computing and machine learning, with the potential to revolutionize traditional algorithms through principles of quantum mechanics. This article presents a thorough examination of foundational concepts in QML, elucidating qubits, quantum gates, superposition, and entanglement. It explores various QML algorithms, such as quantum neural networks, quantum support vector machines, and quantum clustering, which leverage quantum properties to tackle intricate computational tasks. Additionally, it explores the diverse applications of QML, including quantum chemistry, optimization, cryptography, and big data analysis. The article also discusses applications and various types of quantum machine learning libraries. Despite its promise, QML encounters challenges like scalability, noise, and error correction. Addressing these hurdles and realizing QML's full potential necessitates sustained research efforts and collaborative initiatives, poised to drive transformative progress across industries. This research, spanning four months and drawing insights from over ۲۰ reputable scholarly articles, offers a comprehensive investigation into QML.

Keywords:

Machine Learning , Quantum computing , superposition , Qubits , Quantum Algorithms