Quantum Machine Learning

The course covers the following topics:

• 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 and quantum feature maps.
• Quantum Algorithms for Machine Learning; Quantum Classifiers, Quantum Kernel Methods, and Quantum Clustering.
• Quantum Variational Circuits, Quantum Neural Networks (QNNs), Quantum Convolutional Neural Networks (QCNNs), Quantum Federated Learning (QFL), Quantum Reinforcement Learning (QFL), Quantum Multimodal Learning.
• Challenges and future research directions in QML.
• Applications of QML in natural language processing, computer vision, healthcare, drug design, transportation, and intrusion detection.