Machine Learning Detection of Quantum Many-Body Localization Phase Transition

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

doi:10.1364/CLEO_QELS.2022.FF2I.3

Conference on Lasers and Electro-Optics
Technical Digest Series (Optica Publishing Group, 2022),
paper FF2I.3
•https://doi.org/10.1364/CLEO_QELS.2022.FF2I.3

Machine Learning Detection of Quantum Many-Body Localization Phase Transition

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

CLEO: QELS_Fundamental Science 2022

San Jose, California United States
15–20 May 2022
ISBN: 978-1-957171-05-0

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Quantum Computation I (FF2I)

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R. Ziv, A. Rubio-Abadal, A. Keselman, R. Talmon, I. Bloch, and M. Segev, "Machine Learning Detection of Quantum Many-Body Localization Phase Transition," in Conference on Lasers and Electro-Optics, Technical Digest Series (Optica Publishing Group, 2022), paper FF2I.3.

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Abstract

We propose a scheme for detection of quantum many-body phase transitions using unsupervised machine learning. We validate it on simulated 1D Bose-Hubbard model, and then use it on an experimental 2D system undergoing many-body localization.

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