Quantum-Enhanced Classification and Clustering Through Hybrid Quantum–Classical Learning on Synthetic Data

Quantum Machine Learning (QML) has emerged as a promising paradigm that combines principles of quantum computation with classical learning techniques to address challenges posed by complex, high-dimensional, and non-linear datasets. This work presents a systematic experimental study of quantum-enhanced classification and clustering using hybrid quantum–classical models evaluated on synthetically generated datasets. Quantum Support Vector Machines (QSVM) and Variational Quantum Classifiers (VQC) are implemented using the Qiskit framework and benchmarked against established classical algorithms. The synthetic datasets are carefully designed to control linearity, dimensionality, class overlap, and nonlinear structure, enabling a detailed performance comparison under both ideal and noisy simulation conditions. The experimental results indicate that quantum feature representations can enhance class separability in non-linear scenarios, while also highlighting the practical limitations imposed by noise sensitivity and circuit depth constraints inherent to Noisy Intermediate-Scale Quantum (NISQ) devices .