Provably efficient machine learning for quantum many-body problems

Date

Feb 24, 2025

Time

4:30 PM - 5:30 PM

Speaker

Prof. Richard Kueng

Affiliation

Johannes Kepler Universität Linz

Series

MPI-PKS Kolloquium

Language

en

Main Topic

Physik

Other Topics

Physik

Description

Classical machine learning (ML) provides a potentially powerful approach to solving challenging quantum many-body problems in physics and chemistry. However, the advantages of ML over traditional methods have not been firmly established. In this work, we prove that classical ML algorithms **can** efficiently learn to predict important properties of a quantum many-body system. In particular, ML can provably predict ground-state properties of gapped Hamiltonians after learning from other Hamiltonians in the same quantum phase of matter. Our proof technique combines signal processing with quantum many-body physics and also builds upon the recently developed framework of classical shadows. I will try to convey the ideas and also present numerical experiments that confirm our theoretical findings. This colloquium talk is based on joint work with Hsin-Yuan (Robert) Huang, Giacomo Torlai, Victor Albert and John Preskill, see [Huang et al., Provably efficient machine learning for quantum many-body problems, Science 2022]

Last modified: Feb 24, 2025, 7:37:08 AM