New Frontiers in Machine Learning and Quantum

Learning in the quantum universe

Nov 23, 2022, 2:00 p.m.

1h 30m

PI/1-100 - Theatre (Perimeter Institute for Theoretical Physics)

Speaker

Hsin-Yuan (Robert) Huang (California Institute of Technology)

Description

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?

External references

• 22110093
• fc0b4d90-fd7a-4683-8926-79401a1a73fa
• 0b79b2ed-8edd-4bd0-839e-4bfd32aceec9