Learning Beyond Stabilizer States

Explore the frontiers of quantum information in this lecture on learning algorithms for quantum states beyond stabilizer states. Delve into recent research by William Kretschmer and collaborators that extends learning capabilities to quantum states with varying degrees of "non-stabilizerness." Discover how these new algorithms scale in complexity based on the deviation from pure stabilizer states, with a focus on outputs from Clifford+T circuits and states with bounded stabilizer fidelity. Gain insights into the implications of these advancements for quantum computing and information theory, building upon the foundational work of Aaronson, Gottesman, and Montanaro on efficient learning algorithms for stabilizer states.