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:
AutoreEmail
Pipicelli, Claudioclaudio.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:
nomeemail
de Giovanni, Francescodegiovan@unina.it
Tutor:
nomeemail
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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