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METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20260815T115846Z
LAST-MODIFIED:20191106T134804Z
DTSTART:20191113T203000Z
DTEND:20191113T220000Z
UID:event2239@bu.edu
URL:http://physics.bu.edu/internal/events/show/2239
SUMMARY:How hard is it to learn a quantum state?
DESCRIPTION:Featuring Roger Melko\, University of Waterloo and the Perimete
	r Institute\n\nPart of the Condensed Matter Theory Seminar Series.\n\nThe f
	undamental difficulties in simulating quantum physics is one of the core mo
	tivations for building a quantum\ncomputer. As we enter the current era of 
	NISQ hardware\, we are faced with a new paradigm: devices that are difficul
	t to simulate\, but which can be measured to produce an abundance of data. 
	At the same time\, powerful machine learning methods are being adapted to l
	earn representations of quantum states directly from measurement data. It i
	s therefore fair to wonder whether difficulties in simulating large quantum
	 systems also translate into difficulties in learning their states from dat
	a. In this talk I will use recent strategies for quantum state reconstructi
	on based on generative modeling with neural networks to study the learnabil
	ity scaling of prototypical groundstate wavefunctions in NISQ devices. The 
	efficiency with which quantum states can be reconstructed in neural network
	s can be quantified numerically\, and compared to expectations such as thos
	e given by tensor network theory. I will speculate on the implications that
	 an answer to the question "how hard is it to learn a quantum state" would 
	have\, focusing on the current generation of experimental quantum simulator
	s.
LOCATION:SCI 328\, 590 Commonwealth Avenue\, 02215
STATUS:CONFIRMED
CLASS:PUBLIC
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