Learning in the quantum universe


Huang, H. (2022). Learning in the quantum universe. Perimeter Institute. https://pirsa.org/22110052


Huang, Hsin-Yuan. Learning in the quantum universe. Perimeter Institute, Nov. 23, 2022, https://pirsa.org/22110052


          @misc{ pirsa_PIRSA:22110052,
            doi = {10.48660/22110052},
            url = {https://pirsa.org/22110052},
            author = {Huang, Hsin-Yuan},
            keywords = {Condensed Matter},
            language = {en},
            title = {Learning in the quantum universe},
            publisher = {Perimeter Institute},
            year = {2022},
            month = {nov},
            note = {PIRSA:22110052 see, \url{https://pirsa.org}}

Hsin-Yuan Huang California Institute of Technology (Caltech)

Talk Type Scientific Series


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, 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?

Zoom link:  https://pitp.zoom.us/j/97994359596?pwd=UlBwc2hoSkNzWlZvM1o1RWErU1U2QT09