001005289 001__ 1005289
001005289 005__ 20231027114356.0
001005289 0247_ $$2doi$$a10.22331/q-2023-03-09-942
001005289 0247_ $$2Handle$$a2128/34362
001005289 0247_ $$2WOS$$aWOS:000979644900001
001005289 037__ $$aFZJ-2023-01404
001005289 082__ $$a530
001005289 1001_ $$0P:(DE-Juel1)190763$$aLocher, David$$b0$$eCorresponding author
001005289 245__ $$aQuantum Error Correction with Quantum Autoencoders
001005289 260__ $$aWien$$bVerein zur Förderung des Open Access Publizierens in den Quantenwissenschaften$$c2023
001005289 3367_ $$2DRIVER$$aarticle
001005289 3367_ $$2DataCite$$aOutput Types/Journal article
001005289 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1683111125_3821
001005289 3367_ $$2BibTeX$$aARTICLE
001005289 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001005289 3367_ $$00$$2EndNote$$aJournal Article
001005289 520__ $$aActive quantum error correction is a central ingredient to achieve robust quantum processors. Inthis paper we investigate the potential of quantum machine learning for quantum error correction.Specifically, we demonstrate how quantum neural networks, in the form of quantum autoencoders,can be trained to learn optimal strategies for active detection and correction of errors, includingspatially correlated computational errors as well as qubit losses. We highlight that the denoisingcapabilities of quantum autoencoders are not limited to the protection of specific states but extendto the entire logical codespace. We also show that quantum neural networks can be used to discovernew logical encodings that are optimally adapted to the underlying noise. Moreover, we find that,even in the presence of moderate noise in the quantum autoencoders themselves, they may still besuccessfully used to perform beneficial quantum error correction.
001005289 536__ $$0G:(DE-HGF)POF4-5221$$a5221 - Advanced Solid-State Qubits and Qubit Systems (POF4-522)$$cPOF4-522$$fPOF IV$$x0
001005289 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001005289 7001_ $$0P:(DE-Juel1)184904$$aCardarelli, Lorenzo$$b1
001005289 7001_ $$0P:(DE-Juel1)179396$$aMüller, Markus$$b2$$eCorresponding author
001005289 773__ $$0PERI:(DE-600)2931392-2$$a10.22331/q-2023-03-09-942$$gVol. 7, p. 942 -$$p942 -$$tQuantum$$v7$$x2521-327X$$y2023
001005289 8564_ $$uhttps://juser.fz-juelich.de/record/1005289/files/Invoice_34_2023.pdf
001005289 8564_ $$uhttps://juser.fz-juelich.de/record/1005289/files/q-2023-03-09-942-1.pdf$$yOpenAccess
001005289 8767_ $$834/2023$$92023-03-07$$a1200191364$$d2023-03-13$$eAPC$$jZahlung erfolgt
001005289 909CO $$ooai:juser.fz-juelich.de:1005289$$pdnbdelivery$$popenCost$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire
001005289 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)190763$$aForschungszentrum Jülich$$b0$$kFZJ
001005289 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)184904$$aForschungszentrum Jülich$$b1$$kFZJ
001005289 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)179396$$aForschungszentrum Jülich$$b2$$kFZJ
001005289 9131_ $$0G:(DE-HGF)POF4-522$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5221$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vQuantum Computing$$x0
001005289 9141_ $$y2023
001005289 915pc $$0PC:(DE-HGF)0000$$2APC$$aAPC keys set
001005289 915pc $$0PC:(DE-HGF)0001$$2APC$$aLocal Funding
001005289 915pc $$0PC:(DE-HGF)0002$$2APC$$aDFG OA Publikationskosten
001005289 915pc $$0PC:(DE-HGF)0003$$2APC$$aDOAJ Journal
001005289 915__ $$0LIC:(DE-HGF)CCBYNV$$2V:(DE-HGF)$$aCreative Commons Attribution CC BY (No Version)$$bDOAJ$$d2019-06-12T07:01:21Z
001005289 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2019-06-12T07:01:21Z
001005289 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2019-06-12T07:01:21Z
001005289 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2022-11-15
001005289 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2022-11-15
001005289 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001005289 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Peer review$$d2019-06-12T07:01:21Z
001005289 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2022-11-15
001005289 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2022-11-15
001005289 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bQUANTUM-AUSTRIA : 2022$$d2023-10-27
001005289 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-10-27
001005289 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-10-27
001005289 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-10-27
001005289 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2023-10-27
001005289 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bQUANTUM-AUSTRIA : 2022$$d2023-10-27
001005289 920__ $$lyes
001005289 9201_ $$0I:(DE-Juel1)PGI-2-20110106$$kPGI-2$$lTheoretische Nanoelektronik$$x0
001005289 980__ $$ajournal
001005289 980__ $$aVDB
001005289 980__ $$aUNRESTRICTED
001005289 980__ $$aI:(DE-Juel1)PGI-2-20110106
001005289 980__ $$aAPC
001005289 9801_ $$aAPC
001005289 9801_ $$aFullTexts