Pipicelli, Claudio (2020) Quantum Machine Learning: A Comparison Between Quantum and Classical Support Vector Machine. [Tesi di dottorato]
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| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | Quantum Machine Learning: A Comparison Between Quantum and Classical Support Vector Machine |
| Autori: | Autore Email Pipicelli, Claudio claudio.pipicelli@unina.it |
| Data: | 10 Giugno 2020 |
| Numero di pagine: | 205 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dipartimento: | Matematica e Applicazioni "Renato Caccioppoli" |
| Dottorato: | Scienze matematiche e informatiche |
| Ciclo di dottorato: | 32 |
| Coordinatore del Corso di dottorato: | nome email de Giovanni, Francesco degiovan@unina.it |
| Tutor: | nome email Benerecetti, Massimo [non definito] |
| Data: | 10 Giugno 2020 |
| Numero di pagine: | 205 |
| Parole chiave: | Quantum Machine Learning,Quantum Support Vector Machine |
| Settori scientifico-disciplinari del MIUR: | Area 01 - Scienze matematiche e informatiche > INF/01 - Informatica |
| Depositato il: | 13 Giu 2020 07:44 |
| Ultima modifica: | 28 Ott 2021 12:22 |
| URI: | http://www.fedoa.unina.it/id/eprint/13259 |
Abstract
This thesis is mainly focused on the study of Quantum Support Vector Machine (QSVM), a very important member of the recent and innovative Quantum Machine Learning field, and its comparison with conventional Support Vector Machine (SVM). In this paper, I have worked on the application of Quantum Support Vector Machine algorithm, that runs on near term quantum processors from I.B.M., through IBM Quantum Experience cloud service, to a set of supervised machine learning case studies and I compared its performance with classical Support Vector Machine algorithm; net of the enormous hype surrounding the proliferation of quantum technologies in recent years, are we beginning to glimpse an application of real interest in which quantum systems, albeit with limitations, offer concrete improvements already now?
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