Quantum Learning

Quantum learning explores using quantum mechanics to enhance machine learning algorithms, aiming to improve efficiency and solve currently intractable problems. Research focuses on developing and analyzing quantum neural networks (QNNs), variational quantum circuits (VQCs), and quantum algorithms for tasks like classification and state tomography, often incorporating techniques like quantum shadow tomography and curriculum learning to optimize resource usage. These advancements hold significant potential for accelerating machine learning in various fields, particularly where classical methods face limitations due to data size or computational complexity, as demonstrated by applications in healthcare and materials science. However, challenges remain in establishing clear quantum advantages over classical approaches for many practical problems, and ensuring the security and reliability of quantum learning models in cloud-based settings.