Journal article
Quantum self-supervised learning
- Abstract:
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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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(Preview, Version of record, 2.6MB, Terms of use)
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- Publisher copy:
- 10.1088/2058-9565/ac6825
Authors
Bibliographic Details
- Publisher:
- IOP Publishing
- Journal:
- Quantum Science and Technology More from this journal
- Volume:
- 7
- Issue:
- 3
- Article number:
- 35005
- Publication date:
- 2022-05-06
- Acceptance date:
- 2022-04-19
- DOI:
- EISSN:
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2058-9565
Item Description
- Language:
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English
- Keywords:
- Pubs id:
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1259788
- Local pid:
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pubs:1259788
- Deposit date:
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2022-06-14
Terms of use
- Copyright holder:
- Jaderberg et al.
- Copyright date:
- 2022
- Rights statement:
- ©2022 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
- Licence:
- CC Attribution (CC BY)
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