Quantum Machine Learning: Challenges and Innovations

This chapter explores the principles and applications of quantum machine learning, specifically focusing on supervised learning and classification techniques. It discusses the integration of classical and quantum algorithms, tackling challenges like data encoding, noise management, and the development of quantum neural networks. Through practical examples and theoretical discussions, the chapter emphasizes the potential advantages of quantum computing in enhancing traditional machine learning tasks while acknowledging existing limitations.