Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/140318
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorLatorre, José Ignacio-
dc.contributor.authorGil Fuster, Elies M.-
dc.date.accessioned2019-09-17T12:23:19Z-
dc.date.available2019-09-17T12:23:19Z-
dc.date.issued2019-01-
dc.identifier.urihttp://hdl.handle.net/2445/140318-
dc.descriptionTreballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2019, Tutor: José Ignacio Latorreca
dc.description.abstractIn 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.extent5 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isoengca
dc.rightscc-by-nc-nd (c) Gil, 2019-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.classificationXarxes neuronals (Informàtica)cat
dc.subject.classificationOrdinadors quànticscat
dc.subject.classificationTreballs de fi de graucat
dc.subject.otherNeural networks (Computer science)eng
dc.subject.otherQuantum computerseng
dc.subject.otherBachelor's thesiseng
dc.titleVariational Quantum Classifiereng
dc.typeinfo:eu-repo/semantics/bachelorThesisca
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca
Appears in Collections:Treballs Finals de Grau (TFG) - Física

Files in This Item:
File Description SizeFormat 
GIL FUSTER Elies Miquel.pdf316.85 kBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons