Event time:
Friday, November 18, 2022 - 12:00pm to 1:00pm
Audience:
YQI Researchers
Location:
YQI Seminar Room
Event description:
Learning in the quantum universe
I will present recent progress in building a mathematical theory for understanding how scientists, machines, and future quantum computers could learn models of our inherently quantum universe. The talk will review an experimentally feasible procedure for learning a succinct classical representation, the classical shadow, of a quantum state. Classical shadows can be applied to predict efficiently many properties of interest, including expectation values of few-body observables and subsystem entanglement entropy. The theoretical insights obtained from classical shadow tomography enable us to rigorously answer two fundamental questions at the intersection of machine learning and quantum physics: Can classical machines learn to solve challenging problems in quantum physics? Can quantum machines learn exponentially faster than classical machines?
Livestream the event on zoom (Yale login required)