PME Quantum Seminar Series - Learning in the Quantum Universe

I will present recent progress in building a rigorous theory to understand how scientists, machines, and future quantum computers could learn models of our quantum universe. The talk will begin with an
experimentally feasible procedure for converting a quantum many-body system into a succinct classical description of the system and its classical shadow. Classical shadows can be applied to efficiently predict many properties of interest, including expectation values of local observables and few-body correlation functions. I will then build on the classical shadow formalism to answer two fundamental questions at the intersection of machine learning and quantum physics: Can classical machines learn to solve challenging problems in quantum physics? And can quantum machines learn exponentially faster than classical machines?<br /><br />

Host: Prof. Aash Clerk

<a href="https://uchicago.zoom.us/j/98141147190?pwd=T2ZsWkREVEFjUXFDaU1BeW82aHps…; Zoom Link</a>