2026-04-13T07:08:00Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/1685272025-07-17T16:28:05Zcom_2072_1033col_2072_452951

urn:hdl:2117/168527 Detection of quantum phase transitions via machine learning algorithms Quantum Phase Transition detection via Machine Learning algorithms Pérez Díaz, Joel Àrees temàtiques de la UPC::Enginyeria de la telecomunicació Machine learning Quantum optics Neural networks (Computer science) Quantum Phase Transition Machine Learning Optical Lattices Quantum Optics Aprenentatge automàtic Òptica quàntica Xarxes neuronals (Informàtica) 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. 2019-08-31 Master thesis S'autoritza la difusió de l'obra mitjançant la llicència Creative Commons o similar 'Reconeixement-NoComercial- SenseObraDerivada' http://creativecommons.org/licenses/by-nc-nd/3.0/es/ Open Access Universitat Politècnica de Catalunya