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}
}
https://doi.org/10.1103/PhysRevX.10.011069
Web of Science
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