001     906298
005     20230123110600.0
024 7 _ |a 10.1088/1367-2630/ac5490
|2 doi
024 7 _ |a 2128/30930
|2 Handle
024 7 _ |a altmetric:113837537
|2 altmetric
024 7 _ |a WOS:000766853300001
|2 WOS
037 _ _ |a FZJ-2022-01352
082 _ _ |a 530
100 1 _ |a Fiorelli, Eliana
|0 P:(DE-Juel1)185970
|b 0
|e Corresponding author
245 _ _ |a Phase diagram of quantum generalized Potts-Hopfield neural networks
260 _ _ |a [London]
|c 2022
|b IOP
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1648468897_16573
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a We introduce and analyze an open quantum generalization of the q-state Potts-Hopfield neural network, which is an associative memory model based on multi-level classical spins. The dynamics of this many-body system is formulated in terms of a Markovian master equation of Lindblad type, which allows to incorporate both probabilistic classical and coherent quantum processes on an equal footing. By employing a mean field description we investigate how classical fluctuations due to temperature and quantum fluctuations effectuated by coherent spin rotations affect the ability of the network to retrieve stored memory patterns. We construct the corresponding phase diagram, which in the low temperature regime displays pattern retrieval in analogy to the classical Potts-Hopfield neural network. When increasing quantum fluctuations, however, a limit cycle phase emerges, which has no classical counterpart. This shows that quantum effects can qualitatively alter the structure of the stationary state manifold with respect to the classical model, and potentially allow one to encode and retrieve novel types of patterns.
536 _ _ |a 5224 - Quantum Networking (POF4-522)
|0 G:(DE-HGF)POF4-5224
|c POF4-522
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Lesanovsky, Igor
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Müller, Markus
|0 P:(DE-Juel1)179396
|b 2
|e Corresponding author
773 _ _ |a 10.1088/1367-2630/ac5490
|0 PERI:(DE-600)1464444-7
|p 033012
|t New journal of physics
|v 24
|y 2022
|x 1367-2630
856 4 _ |u https://juser.fz-juelich.de/record/906298/files/Invoice_8196961.pdf
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/906298/files/Fiorelli_2022_New_J._Phys._24_033012.pdf
909 C O |o oai:juser.fz-juelich.de:906298
|p openaire
|p open_access
|p OpenAPC
|p driver
|p VDB
|p openCost
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)185970
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)179396
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-522
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Quantum Computing
|9 G:(DE-HGF)POF4-5224
|x 0
914 1 _ |y 2022
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-01-29
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-01-29
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2021-01-29
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2021-01-29
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2022-11-12
|w ger
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b NEW J PHYS : 2021
|d 2022-11-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2022-11-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2022-11-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2021-07-19T16:18:19Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2021-07-19T16:18:19Z
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Blind peer review
|d 2021-07-19T16:18:19Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2022-11-12
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2022-11-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2022-11-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2022-11-12
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1150
|2 StatID
|b Current Contents - Physical, Chemical and Earth Sciences
|d 2022-11-12
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2022-11-12
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)PGI-2-20110106
|k PGI-2
|l Theoretische Nanoelektronik
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)PGI-2-20110106
980 _ _ |a APC
980 1 _ |a APC
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21