## Video URL

https://pirsa.org/22110052# Learning in the quantum universe

### APA

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

### MLA

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

### BibTex

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

Hsin-Yuan Huang California Institute of Technology (Caltech)

**Source Repository**PIRSA

**Collection**

**Talk Type**Scientific Series

**Subject**

## Abstract

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