A Comparative Study of Classical Machine Learning and Quantum Machine Learning Models
S Suvarna,
Chilukani Sravani Reddy,
Mohd Ishaq
Classical Machine Learning (CML) has become a mature computational technology supporting
classification, regression, clustering, forecasting, image analysis, natural language processing,
cybersecurity, healthcare, finance, and engineering applications. Quantum Machine Learning (QML)
extends this field by integrating machine-learning procedures with quantum computation, using quantum
states, quantum gates, parameterized circuits, quantum feature maps, and measurement operations. This
article presents a comparative study of classical and quantum machine-learning models with emphasis
on computational structure, representation, training mechanisms, resource requirements, performance
characteristics, scalability, and application suitability. Classical models considered include Support
Vector Machines, Random Forests, Multilayer Perceptrons, and conventional neural networks, while
representative QML approaches include quantum kernel classifiers, Variational Quantum Classifiers,
Quantum Neural Networks, and Quantum Convolutional Neural Networks. The study synthesizes findings
from recent QML literature and develops normalized numerical comparison datasets for graphical
analysis. The comparison indicates that classical machine learning currently benefits from mature
hardware, large-scale optimization libraries, efficient data processing, and established deployment
pipelines. QML offers alternative feature-space representations and hybrid computational structures, but
current performance is strongly affected by data encoding, circuit depth, noise, measurement overhead,
and trainability. The article identifies scenarios in which QML may be scientifically valuable while
emphasizing that practical evaluation requires fair comparison with well-tuned classical baselines and
complete accounting of computational resources.
10.33425/3066-1226.1314
Published: 26 Sep, 2026