Quantum Machine Learning: A Review of Concepts, Algorithms, And Applications
Vanaparthi Kiranmai,
Sruthi Thanugundala,
Allvala Bhagyasree
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.
10.33425/3066-1226.1317
Published: 26 Sep, 2026