Quantum Neural Networks (QNNs) represent a novel computational
paradigm that merges the principles of quantum computing with the
architecture of artificial neural networks. Through the quantum
phenomena of superposition, entanglement, and interference, QNNs
enable parallel computation in high-dimensional Hilbert spaces,
offering the potential to surpass the representational limits of
classical models. This review provides a comprehensive overview of
the theoretical foundations and architectures of QNNs, including
Quantum Perceptrons, Variational Quantum Circuits (VQCs),
Quantum Convolutional Neural Networks (QCNNs), and Quantum
Recurrent Neural Networks (QRNNs). Furthermore, it discusses
hybrid quantum–classical training mechanisms and key challenges
such as barren plateaus, decoherence, and sampling complexity. The
review also highlights recent applications of QNNs in medical
diagnostics, materials science, and financial forecasting,
demonstrating their potential to accelerate computation and improve
predictive accuracy. Finally, future research directions are discussed
in relation to computational efficiency, model interpretability, and
integration with next-generation quantum hardware.