Quantum self-supervised learning
Publication Date
2022-07-01Journal Title
Quantum Science and Technology
ISSN
2058-9565
Publisher
IOP Publishing
Volume
7
Issue
3
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Jaderberg, B., Anderson, L., Xie, W., Albanie, S., Kiffner, M., & Jaksch, D. (2022). Quantum self-supervised learning. Quantum Science and Technology, 7 (3) https://doi.org/10.1088/2058-9565/ac6825
Description
Funder: National Research Foundation Singapore; doi: https://doi.org/10.13039/501100001381
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>
Keywords
Paper, variational quantum algorithms, quantum machine learning, self-supervised learning, deep learning, quantum neural networks
Sponsorship
Engineering and Physical Sciences Research Council (EP/M013243/1, EP/M013774/1, EP/T001062/1)
Visual AI (EP/T028572/1)
Identifiers
qstac6825, ac6825, qst-101561.r1
External DOI: https://doi.org/10.1088/2058-9565/ac6825
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336893
Rights
Licence:
https://creativecommons.org/licenses/by/4.0/
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