dc.contributor
Universitat Politècnica de Catalunya. Institut de Ciències Fotòniques
dc.contributor
Massignan, Pietro Alberto
dc.contributor
Lewenstein, Maciej
dc.contributor.author
Pérez Díaz, Joel
dc.date.issued
2019-08-31
dc.identifier
https://hdl.handle.net/2117/168527
dc.identifier
ETSETB-230.145467
dc.description.abstract
A Neural Network is trained to classify Mott Insulator and Superfluid phases in an optical lattice using data generated with Diffusion Monte Carlo algorithms (DMC). The trained model is used to predict the phase transition and its dependence with different training parameters is studied. The study of this dependence shows the existence of optimal training and simulation parameters, which cannot be used due to computational limitations. This prevents to calculate the phase transition diagram consistent with other theoretical and experimental results.
dc.format
application/pdf
dc.publisher
Universitat Politècnica de Catalunya
dc.rights
S'autoritza la difusió de l'obra mitjançant la llicència Creative Commons o similar 'Reconeixement-NoComercial- SenseObraDerivada'
dc.rights
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
dc.subject
Machine learning
dc.subject
Quantum optics
dc.subject
Neural networks (Computer science)
dc.subject
Quantum Phase Transition
dc.subject
Machine Learning
dc.subject
Optical Lattices
dc.subject
Quantum Optics
dc.subject
Aprenentatge automàtic
dc.subject
Òptica quàntica
dc.subject
Xarxes neuronals (Informàtica)
dc.title
Detection of quantum phase transitions via machine learning algorithms
dc.title
Quantum Phase Transition detection via Machine Learning algorithms
