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dc.contributor.authorPepper, Alex
dc.contributor.authorTischler, Nora
dc.contributor.authorPryde, Geoff J
dc.date.accessioned2019-10-18T02:48:16Z
dc.date.available2019-10-18T02:48:16Z
dc.date.issued2019
dc.identifier.issn0031-9007
dc.identifier.doi10.1103/PhysRevLett.122.060501
dc.identifier.urihttp://hdl.handle.net/10072/388526
dc.description.abstractWith quantum resources a precious commodity, their efficient use is highly desirable. Quantum autoencoders have been proposed as a way to reduce quantum memory requirements. Generally, an autoencoder is a device that uses machine learning to compress inputs, that is, to represent the input data in a lower-dimensional space. Here, we experimentally realize a quantum autoencoder, which learns how to compress quantum data using a classical optimization routine. We demonstrate that when the inherent structure of the dataset allows lossless compression, our autoencoder reduces qutrits to qubits with low error levels. We also show that the device is able to perform with minimal prior information about the quantum data or physical system and is robust to perturbations during its optimization routine.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherAmerican Physical Society
dc.relation.ispartofissue6
dc.relation.ispartofjournalPhysical Review Letters
dc.relation.ispartofvolume122
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchPhysical sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode51
dc.subject.fieldofresearchcode40
dc.subject.keywordsScience & Technology
dc.subject.keywordsPhysics, Multidisciplinary
dc.titleExperimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationPepper, A; Tischler, N; Pryde, GJ, Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning, Physical Review Letters, 2019, 122 (6)
dc.date.updated2019-10-18T02:37:14Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2019 American Physical Society. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
gro.hasfulltextFull Text
gro.griffith.authorTischler, Nora
gro.griffith.authorPryde, Geoff
gro.griffith.authorPepper, Alex


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