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Title: Deep Quantum Geometry of Matrices

Abstract

We employ machine learning techniques to provide accurate variational wave functions for matrix quantum mechanics, with multiple bosonic and fermionic matrices. The variational quantum Monte Carlo method is implemented with deep generative flows to search for gauge-invariant low-energy states. The ground state (and also long-lived metastable states) of an SU(N) matrix quantum mechanics with three bosonic matrices, and also its supersymmetric “mini-BMN” extension, are studied as a function of coupling and N. Known semiclassical fuzzy sphere states are recovered, and the collapse of these geometries in more strongly quantum regimes is probed using the variational wave function. We then describe a factorization of the quantum mechanical Hilbert space that corresponds to a spatial partition of the emergent geometry. Under this partition, the fuzzy sphere states show a boundary-law entanglement entropy in the large N limit.

Authors:
;
Publication Date:
Research Org.:
Stanford Univ., CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1605980
Alternate Identifier(s):
OSTI ID: 1803256
Grant/Contract Number:  
de-sc0018134; SC0018134
Resource Type:
Published Article
Journal Name:
Physical Review. X
Additional Journal Information:
Journal Name: Physical Review. X Journal Volume: 10 Journal Issue: 1; Journal ID: ISSN 2160-3308
Publisher:
American Physical Society
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Physics

Citation Formats

Han, Xizhi, and Hartnoll, Sean A. Deep Quantum Geometry of Matrices. United States: N. p., 2020. Web. doi:10.1103/PhysRevX.10.011069.
Han, Xizhi, & Hartnoll, Sean A. Deep Quantum Geometry of Matrices. United States. https://doi.org/10.1103/PhysRevX.10.011069
Han, Xizhi, and Hartnoll, Sean A. Mon . "Deep Quantum Geometry of Matrices". United States. https://doi.org/10.1103/PhysRevX.10.011069.
@article{osti_1605980,
title = {Deep Quantum Geometry of Matrices},
author = {Han, Xizhi and Hartnoll, Sean A.},
abstractNote = {We employ machine learning techniques to provide accurate variational wave functions for matrix quantum mechanics, with multiple bosonic and fermionic matrices. The variational quantum Monte Carlo method is implemented with deep generative flows to search for gauge-invariant low-energy states. The ground state (and also long-lived metastable states) of an SU(N) matrix quantum mechanics with three bosonic matrices, and also its supersymmetric “mini-BMN” extension, are studied as a function of coupling and N. Known semiclassical fuzzy sphere states are recovered, and the collapse of these geometries in more strongly quantum regimes is probed using the variational wave function. We then describe a factorization of the quantum mechanical Hilbert space that corresponds to a spatial partition of the emergent geometry. Under this partition, the fuzzy sphere states show a boundary-law entanglement entropy in the large N limit.},
doi = {10.1103/PhysRevX.10.011069},
journal = {Physical Review. X},
number = 1,
volume = 10,
place = {United States},
year = {2020},
month = {3}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1103/PhysRevX.10.011069

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