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VERSION:2.0
PRODID:-//RLASKEY//CALENDEROUS//EN
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METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20261018T170942Z
LAST-MODIFIED:20180404T143432Z
DTSTART:20180409T160000Z
DTEND:20180409T170000Z
UID:event1941@bu.edu
URL:http://physics.bu.edu/internal/events/show/1941
SUMMARY:"Towards learning quantum states with generative models"
DESCRIPTION:Featuring Juan Felipe Carrasquilla\,  Vector Institute for Arti
	ficial Intelligence\nHosted by: Alexandre Day\n\nPart of the Condensed Matt
	er Theory Seminar Series.\n\nThe technological success of machine learning 
	techniques has motivated a research area in the\ncondensed matter physics a
	nd quantum information communities\, where new tools and conceptual connect
	ions between\nmachine learning and many-body physics are rapidly developing
	. In this talk\, I will discuss the use\nof generative models for learning 
	quantum states. First\, I will describe restricted Boltzmann machines\n(RBM
	) as a tool to model the wave function of large many-body systems and explo
	re their use in the\nproblem of quantum state tomography of pure states. In
	 the same vein\, I will briefly discuss how to learn\nmore general mixed st
	ates through a combination of positive-operator valued measures\, tensor ne
	tworks\,\nand generative models. In this setting\, generative models enable
	 accurate learning of prototypical quantum\nstates of large size directly f
	rom measurements mimicking experimental data.
LOCATION:SCI 328\, 590 Commonwealth Avenue\, 02215
STATUS:CONFIRMED
CLASS:PUBLIC
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