Quantum AI
Full course description
This course explores the groundbreaking intersection of quantum computing and artificial intelligence, focusing on how quantum technologies can potentially revolutionize AI paradigms. The curriculum delves into quantum algorithms tailored for AI tasks, addressing complex problems that are currently intractable for classical computers. Students will gain an understanding of how quantum principles can enhance machine learning algorithms, improve optimization tasks, and facilitate data processing capabilities. Through theoretical lessons and practical laboratory sessions, students will learn about quantum mechanics fundamentals applicable to AI, quantum circuit design, and quantum algorithm development. Special emphasis will be placed on hybrid models that integrate classical and quantum computing techniques to solve real-world problems. The course will provide a mix of both theoretical and technical insights, as well as practical implementation details by using the main quantum programming languages and quantum software available.
Prerequisites
Desired prior knowledge: Linear Algebra, Classical Machine Learning
Prerequisites: Introduction to Quantum Computing for AI and Data Science
Recommended reading
Recommended literature: "Machine Learning with Quantum Computers" by M. Schuld, F. Petruccione, Second Edition
Additional literature: Research articles and papers will be provided throughout the course.