Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143433
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dc.contributor.authorBallarini, Darioen_US
dc.contributor.authorGianfrate, Antonioen_US
dc.contributor.authorPanico, Riccardoen_US
dc.contributor.authorOpala, Andrzejen_US
dc.contributor.authorGhosh, Sanjiben_US
dc.contributor.authorDominici, Lorenzoen_US
dc.contributor.authorArdizzone, Vincenzoen_US
dc.contributor.authorDe Giorgi, Milenaen_US
dc.contributor.authorLerario, Giovannien_US
dc.contributor.authorGigli, Giuseppeen_US
dc.contributor.authorLiew, Timothy Chi Hinen_US
dc.contributor.authorMatuszewski, Michalen_US
dc.contributor.authorSanvitto, Danieleen_US
dc.date.accessioned2020-09-01T07:12:51Z-
dc.date.available2020-09-01T07:12:51Z-
dc.date.issued2020-
dc.identifier.citationBallarini, D., Gianfrate, A., Panico, R., Opala, A., Ghosh, S., Dominici, L., ... Sanvitto, D. (2020). Polaritonic neuromorphic computing outperforms linear classifiers. Nano Letters, 20(5), 3506-3512. doi:10.1021/acs.nanolett.0c00435en_US
dc.identifier.issn1530-6992en_US
dc.identifier.urihttps://hdl.handle.net/10356/143433-
dc.description.abstractMachine learning software applications are ubiquitous in many fields of science and society for their outstanding capability to solve computationally vast problems like the recognition of patterns and regularities in big data sets. In spite of these impressive achievements, such processors are still based on the so-called von Neumann architecture, which is a bottleneck for faster and power-efficient neuromorphic computation. Therefore, one of the main goals of research is to conceive physical realizations of artificial neural networks capable of performing fully parallel and ultrafast operations. Here we show that lattices of exciton-polariton condensates accomplish neuromorphic computing with outstanding accuracy thanks to their high optical nonlinearity. We demonstrate that our neural network significantly increases the recognition efficiency compared with the linear classification algorithms on one of the most widely used benchmarks, the MNIST problem, showing a concrete advantage from the integration of optical systems in neural network architectures.en_US
dc.language.isoenen_US
dc.relation.ispartofNano Lettersen_US
dc.rightsThis document is the Accepted Manuscript version of a Published Work that appeared in final form in Nano Letters, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.nanolett.0c00435en_US
dc.subjectScience::Physics::Optics and lighten_US
dc.titlePolaritonic neuromorphic computing outperforms linear classifiersen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.identifier.doi10.1021/acs.nanolett.0c00435-
dc.description.versionAccepted versionen_US
dc.identifier.pmid32251601-
dc.identifier.scopus2-s2.0-85084694910-
dc.identifier.issue5en_US
dc.identifier.volume20en_US
dc.identifier.spage3506-3512en_US
dc.identifier.epage3512en_US
dc.subject.keywordsExciton-polaritonsen_US
dc.subject.keywordsOptical Microcavitiesen_US
dc.description.acknowledgementERC “ElecOpteR” Grant 780757en_US
dc.description.acknowledgementSingapore, MOE2017-T2-1-001en_US
dc.description.acknowledgementSingapore, MOE2018-T2-02-068en_US
dc.description.acknowledgementPoland, 2016/22/E/ST3/ 00045en_US
dc.description.acknowledgementPoland, 2017/25/Z/ST3/03032en_US
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