DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Siheon | ko |
dc.contributor.author | Park, Daniel K. K. | ko |
dc.contributor.author | Rhee, June-Koo Kevin | ko |
dc.date.accessioned | 2023-03-27T06:02:16Z | - |
dc.date.available | 2023-03-27T06:02:16Z | - |
dc.date.created | 2023-03-27 | - |
dc.date.created | 2023-03-27 | - |
dc.date.issued | 2023-02 | - |
dc.identifier.citation | SCIENTIFIC REPORTS, v.13, no.1 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | http://hdl.handle.net/10203/305827 | - |
dc.description.abstract | A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm that demonstrates empirical sub-quadratic run-time complexity with quantum operations feasible even in NISQ computers. We experimented our algorithm with toy example dataset on cloud-based NISQ machines as a proof of concept. We also numerically investigated its performance on the standard Iris flower and MNIST datasets to confirm the practicality and scalability. | - |
dc.language | English | - |
dc.publisher | NATURE PORTFOLIO | - |
dc.title | Variational quantum approximate support vector machine with inference transfer | - |
dc.type | Article | - |
dc.identifier.wosid | 000940302500014 | - |
dc.identifier.scopusid | 2-s2.0-85149053678 | - |
dc.type.rims | ART | - |
dc.citation.volume | 13 | - |
dc.citation.issue | 1 | - |
dc.citation.publicationname | SCIENTIFIC REPORTS | - |
dc.identifier.doi | 10.1038/s41598-023-29495-y | - |
dc.contributor.localauthor | Rhee, June-Koo Kevin | - |
dc.contributor.nonIdAuthor | Park, Daniel K. K. | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.