dc.contributor.authorAltaisky, Mikhail V.
dc.contributor.authorZolnikova, Nadezhda N.
dc.contributor.authorKaputkina, Natalia E.
dc.contributor.authorKrylov, Victor A.
dc.contributor.authorLozovik, Yurii E.
dc.contributor.authorDattani, Nikesh S.
dc.date.accessioned2016-06-29T05:08:35Z
dc.date.available2016-06-29T05:08:35Z
dc.date.issued2016
dc.identifier.citationAltaisky, M. V., Zolnikova, N. N., Kaputkina, N. E., Krylov, V. A., Lozovik, Y. E., & Dattani, N. S. (2016). Towards a feasible implementation of quantum neural networks using quantum dots. Applied Physics Letters, 108(10), 103108-.en_US
dc.identifier.issn0003-6951en_US
dc.identifier.urihttp://hdl.handle.net/10220/40834
dc.description.abstractWe propose an implementation of quantum neural networks using an array of quantum dots with dipole-dipole interactions. We demonstrate that this implementation is both feasible and versatile by studying it within the framework of GaAs based quantum dotqubits coupled to a reservoir of acoustic phonons. Using numerically exact Feynman integral calculations, we have found that the quantum coherence in our neural networks survive for over a hundred ps even at liquid nitrogen temperatures (77 K), which is three orders of magnitude higher than current implementations, which are based on SQUID-based systems operating at temperatures in the mK range.en_US
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en_US
dc.format.extent4 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesApplied Physics Lettersen_US
dc.rights© 2016 AIP Publishing LLC. This paper was published in Applied Physics Letters and is made available as an electronic reprint (preprint) with permission of AIP Publishing LLC. The published version is available at: [http://dx.doi.org/10.1063/1.4943622]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.en_US
dc.subjectQuantum dotsen_US
dc.subjectPhononsen_US
dc.titleTowards a feasible implementation of quantum neural networks using quantum dotsen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Materials Science and Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1063/1.4943622
dc.description.versionPublished versionen_US


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