BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//RLASKEY//CALENDEROUS//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20260825T154025Z
LAST-MODIFIED:20200810T154805Z
DTSTART:20200810T193000Z
DTEND:20200810T203000Z
UID:event2354@bu.edu
URL:http://physics.bu.edu/internal/events/show/2354
SUMMARY:Quantum Information and Network Science
DESCRIPTION:Featuring Xiangyi Meng\, Boston University\, Physics Department
	\n\nPart of the PhD Final Oral Exams.\n\nIt is key to understand how quantu
	m information theory works at the large scale---especially\, on or within t
	he statistical theory of graphs/networks. New concepts and methods are awai
	ting ahead to promote the development of both communities. This presentatio
	n consists of three parts to explore this potential direction:\nOur first w
	ork is to understand how to establish long-distance entanglement transmissi
	on in a quantum network where each link has non-zero concurrence---a measur
	e of bipartite entanglement. We introduce a fundamental statistical theory\
	, concurrence percolation theory (ConPT)\, and find the existence of an ent
	anglement transmission threshold predicted by ConPT which is lower than the
	 known classical-percolation-based results---a "quantum advantage" that is 
	more general and efficient than expected. ConPT also shows a percolation-li
	ke universal critical behavior derived by finite-size analysis.\nOur second
	 work is to study continuous-time quantum walk as an open system that stron
	gly interacts with the environment where non-Markovianity may significantly
	 speed up the dynamics. We confirm this speed-up by first introducing a gen
	eral multi-scale perturbation method that works on integro-differential equ
	ations and then building the Hamiltonian on regular networks\, e.g.\, star 
	or complete graphs\, which can be mapped to an error correction algorithm s
	cheme of practical significance.\nOur third work explores the possible use 
	of entanglement entropy (EE) in machine-learning fields. We introduce a new
	 long-short-term-memory-based recurrent neural network architecture using t
	ensorization techniques to forecast chaotic time series\, the learnability 
	of which is determined not only by the number of free parameters but also t
	he tensorization complexity---recognized as how EE scales.\n\n----\nJoin Zo
	om Meeting\nhttps://bostonu.zoom.us/j/95158303290?pwd=Y0c2QU9sZmh1WDdRSWJ5b
	DhBOVFXdz09\n \nMeeting ID: 951 5830 3290\nPasscode: 873607
LOCATION: \, \, 
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
END:VCALENDAR