Authors: Professor Sangeeta Alagi, Priti Jagdale, Swati More
Abstract: Quantum Machine Learning (QML) is an emerging interdisciplinary field combining quantum computing’s xprinciples with classical machine learning (ML) algorithms. By leveraging quantum bits (qubits), superposition, and entanglement, QML aims to overcome the computational limitations of classical systems, potentially achieving exponential speedups in tasks like classification, optimization, and sampling. This paper explores the foundations of QML, recent advancements, popular algorithms, implementation frameworks, current challenges, and future research directions.
