Detection of quantum phase transitions via machine learning algorithms

View/Open
Document typeMaster thesis
Date2019-08-31
Rights accessOpen Access
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.
Collections
Files | Description | Size | Format | View |
---|---|---|---|---|
Report.pdf | 661,8Kb | View/Open |
Except where otherwise noted, content on this work
is licensed under a Creative Commons license
:
Attribution-NonCommercial-NoDerivs 3.0 Spain