Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143009
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dc.contributor.authorXu, Huawenen_US
dc.contributor.authorGhosh, Sanjiben_US
dc.contributor.authorMatuszewski, Michalen_US
dc.contributor.authorLiew, Timothy Chi Hinen_US
dc.date.accessioned2020-07-21T03:06:03Z-
dc.date.available2020-07-21T03:06:03Z-
dc.date.issued2020-
dc.identifier.citationXu, H., Ghosh, S., Matuszewski, M., & Liew, T. C. H. (2020). Universal self-correcting computing with disordered exciton-polariton neural networks. Physical Review Applied, 13(6), 064074-. doi:10.1103/PhysRevApplied.13.064074en_US
dc.identifier.issn2331-7019en_US
dc.identifier.urihttps://hdl.handle.net/10356/143009-
dc.description.abstractWe show theoretically that neural networks based on disordered exciton-polariton systems allow the realization of Toffoli gates. Noise in input signals is self-corrected by the networks, such that the obtained Toffoli gates are in principle cascadable, where their universality would allow for arbitrary circuits without the need of additional error-correcting codes. We further find that the exciton-polariton reservoir computers can directly simulate composite circuits, such that they are a highly efficient platform allowing circuits to operate in a single step, minimizing the delay of signal transport between elements and error-correction overhead.en_US
dc.description.sponsorshipMOE (Min. of Education, S’pore)en_US
dc.language.isoenen_US
dc.relation.ispartofPhysical Review Applieden_US
dc.rights© 2020 American Physical Society. All rights reserved. This paper was published in Physical Review Applied and is made available with permission of American Physical Society.en_US
dc.subjectScience::Physicsen_US
dc.titleUniversal self-correcting computing with disordered exciton-polariton neural networksen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.identifier.doi10.1103/PhysRevApplied.13.064074-
dc.description.versionPublished versionen_US
dc.identifier.issue6en_US
dc.identifier.volume13en_US
dc.subject.keywordsExciton-polaritonsen_US
dc.subject.keywordsNeural Networksen_US
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