Error-free affine, unitary, and probabilistic OBDDS

dc.date.accessioned	2019-01-22T20:36:50Z
dc.date.available	2019-01-22T20:36:50Z
dc.date.issued	2018
dc.identifier.issn	0302-9743
dc.identifier.uri	https://dspace.kpfu.ru/xmlui/handle/net/147969
dc.description.abstract	© IFIP International Federation for Information Processing 2018. We introduce the affine OBDD model and show that zero-error affine OBDDs can be exponentially narrower than bounded-error unitary and probabilistic OBDDs on certain problems. Moreover, we show that Las Vegas unitary and probabilistic OBDDs can be quadratically narrower than deterministic OBDDs. We also obtain the same results for the automata versions of these models.
dc.relation.ispartofseries	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.subject	Affine models
dc.subject	Las Vegas computation
dc.subject	OBDDs
dc.subject	Quantum and probabilistic computation
dc.subject	Succinctness
dc.subject	Zero-error
dc.title	Error-free affine, unitary, and probabilistic OBDDS
dc.type	Conference Paper
dc.relation.ispartofseries-volume	10952 LNCS
dc.collection	Публикации сотрудников КФУ
dc.relation.startpage	175
dc.source.id	SCOPUS03029743-2018-10952-SID85050618811

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Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.