Ph 220 - Quantum Learning Theory

This course covers quantum learning theory, a contemporary field at the intersection of quantum mechanics, quantum computing, statistical learning theory, and machine learning. The fundamental questions explored include: how to efficiently learn quantum many-body systems? When can quantum machines learn and predict better than classical machines? What physical phenomena can quantum machines learn and discover? The course aims to develop rigorous theoretical foundations for understanding how scientists, machines, and future quantum computers can learn and discover new phenomena in our quantum-mechanical universe.