Quantum Machine Learning

The course covers the following modules:

• Introduction to Quantum Computing and Machine Learning; including state vectors, Hilbert space, quantum states, quantum entanglement and superposition, quantum gates, and quantum circuits. 
• Quantum machine learning (QML) basics; including representing classical data on quantum systems, quantum data encoding and embedding, quantum data representation, quantum feature maps, and tensor methods. 
• Quantum Algorithms for Machine Learning; Quantum Classifiers, Quantum Kernel Methods, and Quantum Clustering. 
• Basics of Quantum Variational Circuits, Quantum Neural Networks (QNNs), Quantum Convolutional Neural Networks (QCNNs), Quantum Natural Language Processing (QNLP), Quantum Computer Vision (QCV), Quantum Federated Learning (QFL), Quantum Reinforcement Learning (QRL), Quantum Reservoir Computing, Quantum Architecture Search (including reinforcement-learning-based search methods), Quantum Generative Models (QGANs, Quantum Diffusion Models), Quantum Multimodal Learning.
• Challenges and future research directions in QML
• Applications of QML in healthcare, drug design, transportation, and other real-world application.