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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.<\/jats:p>","DOI":"10.1088\/2632-2153\/acafd5","type":"journal-article","created":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T22:42:30Z","timestamp":1672785750000},"page":"015006","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Variational quantum one-class classifier"],"prefix":"10.1088","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2818-488X","authenticated-orcid":true,"given":"Gunhee","family":"Park","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8792-5641","authenticated-orcid":true,"given":"Joonsuk","family":"Huh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3177-4143","authenticated-orcid":true,"given":"Daniel K","family":"Park","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"mlstacafd5bib1","author":"Wittek","year":"2014"},{"key":"mlstacafd5bib2","author":"Schuld","year":"2021"},{"key":"mlstacafd5bib3","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1038\/s41534-021-00513-z","article-title":"Parameter estimation in quantum sensing based on deep reinforcement learning","volume":"8","author":"Xiao","year":"2022","journal-title":"npj Quantum Inf."},{"key":"mlstacafd5bib4","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.113.130503","article-title":"Quantum support vector machine for big data classification","volume":"113","author":"Rebentrost","year":"2014","journal-title":"Phys. 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