2020
DOI: 10.1103/physrevx.10.011069
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Deep Quantum Geometry of Matrices

Abstract: We employ machine learning techniques to provide accurate variational wavefunctions for matrix quantum mechanics, with multiple bosonic and fermionic matrices.Variational quantum Monte Carlo 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, as well as its supersymmetric 'mini-BMN' extension, are studied as a function of coupling and N .Known semicl… Show more

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Cited by 47 publications
(65 citation statements)
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References 71 publications
(119 reference statements)
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“…It therefore behooves us to find methods suitable for studying the zero temperature quantum states of multimatrix quantum mechanics directly. Progress was made recently in this direction by using a neural network variational wave function [17]. Here we describe a different approach.…”
mentioning
confidence: 99%
“…It therefore behooves us to find methods suitable for studying the zero temperature quantum states of multimatrix quantum mechanics directly. Progress was made recently in this direction by using a neural network variational wave function [17]. Here we describe a different approach.…”
mentioning
confidence: 99%
“…Nanoparticles with sizes less than 10 nm are rapidly cleared by renal excretion due to the renal average filtration pore of 10 nm, while nanoparticles with sizes over 100 nm may be recognized by macrophages and cleared by the mononuclear phagocyte system. 38 Nanoparticle with sizes from 10 to 100 nm have demonstrated enhanced uptake in the abnormal vasculature of tumors. 39 The hydrodynamic Hyp-NP size of 37.3 nm is well within the size range known to passively accumulate in tumor tissue through the enhanced permeability and retention (EPR) effect and generate a longer circulation time for biological function, 38 which may putatively enable greater hypericin localization in necrotic tumor areas.…”
Section: Discussionmentioning
confidence: 99%
“…Here, the choice b k = (J) k , corresponding to the replacement J i → Jn i seems natural. 14 This connection was also explored recently in[26].…”
mentioning
confidence: 91%
“…The recent work [26] that appeared while this paper was in preparation also considers the emergence of geometry and the connection between areas and entropies in matrix models (see also [27][28][29][30][31] for related considerations). For a discussion on entanglement and its potential dual in the c = 1 model, see [32,33].…”
Section: Introductionmentioning
confidence: 99%