Understanding Quantum Machine Learning Also Requires Rethinking Generalization

Explore a thought-provoking lecture on the challenges of understanding generalization in quantum machine learning. Delve into the experimental findings that reveal how quantum neural networks can accurately fit random states and random labeling of training data, defying traditional notions of small generalization error. Examine the implications of these results on complexity measures such as VC dimension and Rademacher complexity. Discover a theoretical construction demonstrating the ability of quantum neural networks to fit arbitrary labels to quantum states. Gain insights into the fundamental challenges facing conventional understanding of generalization in quantum machine learning and the need for a paradigm shift in model design. Presented by Carlos Bravo Prieto of Freie Universität Berlin at IPAM's Mathematical Aspects of Quantum Learning Workshop, this 44-minute talk offers a deep dive into the intersection of quantum computing and machine learning theory.