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Quantum self-supervised learning

Abstract:

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...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1088/2058-9565/ac6825

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Physics
Oxford college:
Wolfson College
Role:
Author
ORCID:
0000-0001-9297-0175
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Oxford college:
Keble College
Role:
Author
ORCID:
0000-0003-0269-3237
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Physics
Oxford college:
Keble College
Role:
Author
ORCID:
0000-0002-8321-6768
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Publisher:
IOP Publishing Publisher's website
Journal:
Quantum Science and Technology Journal website
Volume:
7
Issue:
3
Article number:
35005
Publication date:
2022-05-06
Acceptance date:
2022-04-19
DOI:
EISSN:
2058-9565
Language:
English
Keywords:
Pubs id:
1259788
Local pid:
pubs:1259788
Deposit date:
2022-06-14

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