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Variational quantum one-class classifier

Park, Gunhee and Huh, Joonsuk and Park, Daniel K. (2023) Variational quantum one-class classifier. Machine Learning: Science and Technology, 4 (1). Art. No. 015006. ISSN 2632-2153. doi:10.1088/2632-2153/acafd5.

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One-class classification (OCC) is a fundamental problem in pattern recognition with a wide range of applications. This work presents a semi-supervised quantum machine learning algorithm for such a problem, which we call a variational quantum one-class classifier (VQOCC). The algorithm is suitable for noisy intermediate-scale quantum computing because the VQOCC trains a fully-parameterized quantum autoencoder with a normal dataset and does not require decoding. The performance of the VQOCC is compared with that of the one-class support vector machine (OC-SVM), the kernel principal component analysis (PCA), and the deep convolutional autoencoder (DCAE) using handwritten digit and Fashion-MNIST datasets. The numerical experiment examined various structures of VQOCC by varying data encoding, the number of parameterized quantum circuit layers, and the size of the latent feature space. The benchmark shows that the classification performance of VQOCC is comparable to that of OC-SVM and PCA, although the number of model parameters grows only logarithmically with the data size. The quantum algorithm outperformed DCAE in most cases under similar training conditions. Therefore, our algorithm constitutes an extremely compact and effective machine learning model for OCC.

Item Type:Article
Related URLs:
URLURL TypeDescription
Park, Gunhee0000-0002-2818-488X
Huh, Joonsuk0000-0002-8792-5641
Park, Daniel K.0000-0002-3177-4143
Additional Information:Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. This research was supported by the Yonsei University Research Fund of 2022 (2022-22-0124), by the National Research Foundation of Korea (Grant Nos. 2021M3H3A1038085, 2019M3E4A1079666, 2022M3E4A1074591, and 2022M3H3A106307411), and by the KIST Institutional Program (2E31531-22-076). Data availability statement. The data that support the findings of this study are available upon reasonable request from the authors.
Funding AgencyGrant Number
Yonsei University2022-22-0124
National Research Foundation of Korea2021M3H3A1038085
National Research Foundation of Korea2019M3E4A1079666
National Research Foundation of Korea2022M3E4A1074591
National Research Foundation of Korea2022M3H3A106307411
Korea Institute of Science and Technology (KIST)2E31531-22-076
Issue or Number:1
Record Number:CaltechAUTHORS:20230209-988069100.9
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:119175
Deposited By: Research Services Depository
Deposited On:15 Mar 2023 16:45
Last Modified:15 Mar 2023 16:45

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