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SEQUENCE:1
UID:UW-Physics-Event-6874
DTSTART:20220330T160000Z
DTEND:20220330T171500Z
DTSTAMP:20240917T022233Z
LAST-MODIFIED:20220328T173522Z
LOCATION:Chamberlin 5280 (Zoom link also available for online particip
ants who signed up on our mailing list)
SUMMARY:Machine Learning Statistical Gravity from Multi-Region Entangl
ement Entropy\, Physics ∩ ML Seminar\, Yi-Zhuang You\, UC San Diego
DESCRIPTION:The Ryu-Takayanagi formula directly connects quantum entan
glement and geometry. Yet the assumption of static geometry lead to an
exponentially small mutual information between far-separated disjoint
regions\, which does not hold in many systems such as free fermion co
nformal field theories. In this talk\, I will present a microscopic mo
del by superimposing entanglement features of an ensemble of random te
nsor networks of different bond dimensions\, which can be mapped to a
statistical gravity model consisting of a massive scalar field on a fl
uctuating background geometry. We propose a machine-learning algorithm
that recovers the underlying geometry fluctuation from multi-region e
ntanglement entropy data by modeling the bulk geometry distribution vi
a a generative neural network. To demonstrate its effectiveness\, we t
ested the model on a free fermion system and showed mutual information
can be mediated effectively by geometric fluctuation. Remarkably\, lo
cality emerged from the learned distribution of bulk geometries\, poin
ting to a local statistical gravity theory in the holographic bulk.
URL:https://www.physics.wisc.edu/events/?id=6874
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