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Explore quantum machine learning through this 21-minute conference talk that introduces Quantum Tilted Empirical Risk Minimization (QTERM), a novel regularization framework for learning quantum processes and states. Delve into the theoretical foundations of quantum learning models and discover how traditional empirical risk minimization principles adapt to quantum data challenges. Learn about the development of QTERM as an extension of classical tilted empirical risk minimization, specifically designed for quantum state learning applications. Examine three key theoretical contributions: upper bounds on QTERM's sample complexity that prove its learnability, new PAC generalization bounds for classical TERM, and agnostic learning guarantees for quantum hypothesis selection. Understand how this research advances the broader literature on complexity bounds for quantum state learning feasibility and the role of regularization techniques in quantum learning frameworks. Gain insights into the intersection of quantum computing and machine learning, including quantum sample complexity measures and generalization bounds that modify traditional learning paradigms when working with quantum data.
