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dc.contributor.authorMaurya, Satvik
dc.contributor.authorMude, Chaithanya
dc.contributor.authorOliver, William
dc.contributor.authorLienhard, Benjamin
dc.contributor.authorTannu, Swamit
dc.date.accessioned2023-07-11T19:18:26Z
dc.date.available2023-07-11T19:18:26Z
dc.date.issued2023-06-17
dc.identifier.isbn979-8-4007-0095-8
dc.identifier.urihttps://hdl.handle.net/1721.1/151100
dc.publisherACM|Proceedings of the 50th Annual International Symposium on Computer Architectureen_US
dc.relation.isversionofhttps://doi.org/10.1145/3579371.3589042en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceAssociation for Computing Machineryen_US
dc.titleScaling Qubit Readout with Hardware Efficient Machine Learning Architecturesen_US
dc.typeArticleen_US
dc.identifier.citationMaurya, Satvik, Mude, Chaithanya, Oliver, William, Lienhard, Benjamin and Tannu, Swamit. 2023. "Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures."
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentMassachusetts Institute of Technology. Department of Physics
dc.identifier.mitlicensePUBLISHER_POLICY
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2023-07-01T07:57:16Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2023-07-01T07:57:17Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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