QUANTUM MACHINE LEARNING: INTEGRATING QUANTUM COMPUTING PRINCIPLES INTO NEURAL NETWORK OPTIMIZATION

Abstract

Quantum Machine Learning (QML) merges quantum computation with machine learning to explore whether quantum devices can accelerate or improve learning tasks. This article reviews theoretical foundations and practical approaches for integrating quantum principles into neural network optimization: data encoding, quantum neural network (QNN) architectures, quantum kernel methods, hybrid quantum–classical optimization, gradient techniques, training pathologies (barren plateaus), noise mitigation strategies, software frameworks, representative applications, and research directions. Emphasis is placed on methods relevant to near-term Noisy Intermediate-Scale Quantum (NISQ) devices and on practical recommendations for researchers.