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VERSION:2.0
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BEGIN:VEVENT
DTSTAMP:20261018T231850Z
LAST-MODIFIED:20181206T201105Z
DTSTART:20181206T203000Z
DTEND:20181206T220000Z
UID:event2059@bu.edu
URL:http://physics.bu.edu/internal/events/show/2059
SUMMARY:AN APPLICATION OF MACHINE LEARNING TO STATISTICAL PHYSICS: FROM THE
	 PHASES OF QUANTUM CONTROL TO SATISFIABILITY PROBLEMS
DESCRIPTION:Featuring Alexandre Day\n\nPart of the PhD Final Oral Exams.\n\
	nExamining Committee:  Pankaj Mehta\, Anatoli Polkovnikov\, Anders Sandvik\
	, Martin Schmaltz\, Alex Sushkov\n\nAbstract:\n\n\nThis dissertation presen
	ts a study of machine learning methods with a focus on applications to stat
	istical and condensed matter physics\, in particular the problem of quantum
	 state preparation\, spin-glass and constraint satisfiability. We will star
	t by introducing the core principles of machine learning such as overfittin
	g\, bias-variance tradeoff and the disciplines of supervised\, unsupervised
	 and reinforcement learning. This discussion will be set in the context of 
	recent applications of machine learning to statistical physics and condense
	d matter physics. We then present the problem of quantum state preparation 
	and show how reinforcement learning along with stochastic optimization meth
	ods can be applied to identify and define phases of quantum control. Remini
	scent of condensed matter physics\, the underlying phases of quantum contro
	l are identified via a set of order parameters and further detailed in term
	s of their universal implications for optimal quantum control. In particula
	r\, casting the optimal quantum control problem as an optimization problem\
	, we show that it exhibits a generic glassy phase and establish a connectio
	n with the fields of spin-glass physics and constraint satisfiability probl
	ems. We then demonstrate how unsupervised learning methods can be used to o
	btain important information about the complexity of the phases described. W
	e end by presenting a novel clustering framework\, termed HAL for hierarchi
	cal agglomerative learning\, which exploits out-of-sample accuracy estimate
	s of machine learning classifiers to perform robust clustering of high-dime
	nsional data. We show applications of HAL to various clustering problems.
LOCATION:PRB 595\, 3 Cummington Mall\, 02215
STATUS:CONFIRMED
CLASS:PUBLIC
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