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http://hdl.handle.net/2445/140318
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DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Latorre, José Ignacio | - |
dc.contributor.author | Gil Fuster, Elies M. | - |
dc.date.accessioned | 2019-09-17T12:23:19Z | - |
dc.date.available | 2019-09-17T12:23:19Z | - |
dc.date.issued | 2019-01 | - |
dc.identifier.uri | http://hdl.handle.net/2445/140318 | - |
dc.description | Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2019, Tutor: José Ignacio Latorre | ca |
dc.description.abstract | In this work we propose a quantum alternative to Artificial Neural Networks in classification tasks. We design a set of different neural networks and quantum circuits and test their performances. We found that a Variational Quantum Classifier can outperform a classical model using far less free parameters and, thus, being more eficient. Further, a complex classification task requires deeper quantum circuits, which nevertheless grow at a slower pace than the number of neurons needed in a Neural Network for the same task. | ca |
dc.format.extent | 5 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | ca |
dc.rights | cc-by-nc-nd (c) Gil, 2019 | - |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.source | Treballs Finals de Grau (TFG) - Física | - |
dc.subject.classification | Xarxes neuronals (Informàtica) | cat |
dc.subject.classification | Ordinadors quàntics | cat |
dc.subject.classification | Treballs de fi de grau | cat |
dc.subject.other | Neural networks (Computer science) | eng |
dc.subject.other | Quantum computers | eng |
dc.subject.other | Bachelor's theses | eng |
dc.title | Variational Quantum Classifier | eng |
dc.type | info:eu-repo/semantics/bachelorThesis | ca |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca |
Appears in Collections: | Treballs Finals de Grau (TFG) - Física |
Files in This Item:
File | Description | Size | Format | |
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GIL FUSTER Elies Miquel.pdf | 316.85 kB | Adobe PDF | View/Open |
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