Identifying topological order through unsupervised machine learning

Unsupervised Machine Learning

Identifying topological order by entanglement entropy

Topological phases are unique states of matter that incorporate long-range quantum entanglement and host exotic excitations with fractional quantum statistics. Here we report a practical method to identify topological phases in arbitrary realistic models by accurately calculating the topological entanglement entropy using the density matrix renormalization group (DMRG). We argue that the DMRG a...

Machine Learning for NLP: Unsupervised learning techniques

• So far we have seen supervised learning (of classification): – learning based on a training set where labelling of instances represents the target (categorisation) function – classifier implements an approximation of the target funtion – outcome: a classification decision • Unsupervised learning: – learning based on unannotated instances; – outcome: a grouping of objects (instances and groups...

Identifying Uncertain Galaxy Morphologies Using Unsupervised Learning

With the onset of massive cosmological data collection through mediums such as the Sloan Digital Sky Survey (SDSS), galaxy classification has been accomplished for the most part with the help of citizen science communities like Galaxy Zoo. However, an analysis of one of the Galaxy Zoo morphological classification data sets has shown that a significant majority of all classified galaxies are, in...

Identifying Quantum Topological Phases Through Statistical Correlation

We theoretically examine the use of a statistical distance measure, the indistinguishability, as a generic tool for the identification of topological order. We apply this measure to the toric code and two fractional quantum Hall models. We find that topologically ordered states can be identified with the indistinguishability for both models. Calculations with the indistinguishability also under...