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dc.contributor.authorDong, Nanqing
dc.contributor.authorKampffmeyer, Michael
dc.contributor.authorVoiculescu, Irina
dc.contributor.authorXing, Eric
dc.date.accessioned2022-12-08T08:50:33Z
dc.date.available2022-12-08T08:50:33Z
dc.date.issued2022-04-27
dc.description.abstractAlthough quantum neural networks (QNNs) have shown promising results in solving simple machine learning tasks recently, the behavior of QNNs in binary pattern classification is still underexplored. In this work, we find that QNNs have an Achilles’ heel in binary pattern classification. To illustrate this point, we provide a theoretical insight into the properties of QNNs by presenting and analyzing a new form of symmetry embedded in a family of QNNs with full entanglement, which we term negational symmetry. Due to negational symmetry, QNNs can not differentiate between a quantum binary signal and its negational counterpart. We empirically evaluate the negational symmetry of QNNs in binary pattern classification tasks using Google’s quantum computing framework. Both theoretical and experimental results suggest that negational symmetry is a fundamental property of QNNs, which is not shared by classical models. Our findings also imply that negational symmetry is a double-edged sword in practical quantum applications.en_US
dc.identifier.citationDong, Kampffmeyer, Voiculescu, Xing. Negational symmetry of quantum neural networks for binary pattern classification. Pattern Recognition. 2022;129en_US
dc.identifier.cristinIDFRIDAID 2060040
dc.identifier.doi10.1016/j.patcog.2022.108750
dc.identifier.issn0031-3203
dc.identifier.issn1873-5142
dc.identifier.urihttps://hdl.handle.net/10037/27740
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalPattern Recognition
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleNegational symmetry of quantum neural networks for binary pattern classificationen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)