Machine Learning Detection of Quantum Many-Body Localization Phase Transition

Ron Ziv, Antonio Rubio-Abadal, Anna Keselman, Ronen Talmon, Immanuel Bloch, Mordechai Segev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We propose a scheme for detection of quantum many-body phase transitions using unsupervised machine learning. We validate it on simulated ID Bose-Hubbard model, and then use it on an experimental 2D system undergoing many-body localization.

Original languageEnglish
Title of host publication2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings
ISBN (Electronic)9781957171050
StatePublished - 2022
Event2022 Conference on Lasers and Electro-Optics, CLEO 2022 - San Jose, United States
Duration: 15 May 202220 May 2022

Publication series

Name2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings

Conference

Conference2022 Conference on Lasers and Electro-Optics, CLEO 2022
Country/TerritoryUnited States
CitySan Jose
Period15/05/2220/05/22

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering
  • Spectroscopy
  • Management, Monitoring, Policy and Law
  • Biomedical Engineering
  • Acoustics and Ultrasonics
  • Materials Science (miscellaneous)

Fingerprint

Dive into the research topics of 'Machine Learning Detection of Quantum Many-Body Localization Phase Transition'. Together they form a unique fingerprint.

Cite this