Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/140318
Title: Variational Quantum Classifier
Author: Gil Fuster, Elies M.
Director/Tutor: Latorre, José Ignacio
Keywords: Xarxes neuronals (Informàtica)
Ordinadors quàntics
Treballs de fi de grau
Neural networks (Computer science)
Quantum computers
Bachelor's thesis
Issue Date: Jan-2019
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.
Note: Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2019, Tutor: José Ignacio Latorre
URI: http://hdl.handle.net/2445/140318
Appears in Collections:Treballs Finals de Grau (TFG) - Física

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