Machine Learning Algorithms on Quantum Computers (Quantum Machine Learning – QML)

Quantum Machine Learning (QML) is emerging as a promising research field that integrates quantum computing principles with classical machine learning paradigms to achieve computational advantages unattainable by classical hardware. As quantum devices evolve from noisy intermediate-scale quantum (NISQ) systems toward fault-tolerant architectures, new algorithmic frameworks such as variational quantum circuits (VQCs), quantum kernel methods, and hybrid quantum-classical models have gained significant attention. This review explores state-of-the-art quantum machine learning algorithms, their mathematical foundations, hardware requirements, and practical applications. We analyze the strengths and limitations of key QML approaches, including Quantum Support Vector Machines, Quantum Neural Networks, and Variational Quantum Algorithms, while addressing challenges posed by noise, scalability, and optimization landscapes. Finally, we outline current research trends and provide insights into how quantum-enhanced learning may reshape the future of data processing and intelligent computing.