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dc.contributor.advisorLatorre, José Ignacio-
dc.contributor.authorGil Fuster, Elies M.-
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
dc.format.extent5 p.-
dc.rightscc-by-nc-nd (c) Gil, 2019-
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
Appears in Collections:Treballs Finals de Grau (TFG) - Física

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