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dc.contributor.authorJaderberg, B
dc.contributor.authorAnderson, LW
dc.contributor.authorXie, W
dc.contributor.authorAlbanie, S
dc.contributor.authorKiffner, M
dc.contributor.authorJaksch, D
dc.date.accessioned2022-05-09T11:03:51Z
dc.date.available2022-05-09T11:03:51Z
dc.date.issued2022-07-01
dc.date.submitted2021-12-08
dc.identifier.issn2058-9565
dc.identifier.otherqstac6825
dc.identifier.otherac6825
dc.identifier.otherqst-101561.r1
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/336893
dc.descriptionFunder: National Research Foundation Singapore; doi: https://doi.org/10.13039/501100001381
dc.description.abstract<jats:title>Abstract</jats:title> <jats:p>The resurgence of self-supervised learning, whereby a deep learning model generates its own supervisory signal from the data, promises a scalable way to tackle the dramatically increasing size of real-world data sets without human annotation. However, the staggering computational complexity of these methods is such that for state-of-the-art performance, classical hardware requirements represent a significant bottleneck to further progress. Here we take the first steps to understanding whether quantum neural networks (QNNs) could meet the demand for more powerful architectures and test its effectiveness in proof-of-principle hybrid experiments. Interestingly, we observe a numerical advantage for the learning of visual representations using small-scale QNN over equivalently structured classical networks, even when the quantum circuits are sampled with only 100 shots. Furthermore, we apply our best quantum model to classify unseen images on the <jats:italic>ibmq_paris</jats:italic> quantum computer and find that current noisy devices can already achieve equal accuracy to the equivalent classical model on downstream tasks.</jats:p>
dc.languageen
dc.publisherIOP Publishing
dc.subjectPaper
dc.subjectvariational quantum algorithms
dc.subjectquantum machine learning
dc.subjectself-supervised learning
dc.subjectdeep learning
dc.subjectquantum neural networks
dc.titleQuantum self-supervised learning
dc.typeArticle
dc.date.updated2022-05-09T11:03:50Z
prism.issueIdentifier3
prism.publicationNameQuantum Science and Technology
prism.volume7
dc.identifier.doi10.17863/CAM.84312
dcterms.dateAccepted2022-04-19
rioxxterms.versionofrecord10.1088/2058-9565/ac6825
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidJaderberg, B [0000-0001-9297-0175]
dc.contributor.orcidKiffner, M [0000-0002-8321-6768]
dc.identifier.eissn2058-9565
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/M013243/1, EP/M013774/1, EP/T001062/1)
pubs.funder-project-idVisual AI (EP/T028572/1)
cam.issuedOnline2022-05-06


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