Abstract

Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines machine-learning principles with quantum computing to investigate new approaches for data representation, feature transformation, optimization, classification, and pattern recognition. QML exploits quantum phenomena such as superposition, entanglement, interference, and high-dimensional state representations to construct computational models that may provide advantages for selected problems. Major approaches include quantum support vector machines, quantum kernel methods, variational quantum classifiers, quantum neural networks, quantum convolutional neural networks, quantum generative models, and hybrid quantum-classical learning architectures. This review examines the fundamental concepts, algorithmic structures, data-encoding strategies, application areas, implementation procedures, and practical limitations of QML. Particular attention is given to near-term quantum devices, where limited qubit connectivity, noise, circuit depth, measurement overhead, and trainability strongly influence performance. The article also presents graph-ready normalized comparative datasets covering algorithm characteristics, application domains, challenges, and representative architectures. The review indicates that QML has considerable research potential, but practical deployment depends on improved hardware reliability, efficient encoding, robust optimization, standardized benchmarks, and meaningful comparisons with strong classical baselines.