Unsupervised learning using topological data augmentation

Understand

Unsupervised machine learning is a cornerstone of artificial intelligence as it provides algorithms capable of learning tasks, such as classification of data, without explicit human assistance.

• We present an unsupervised deep learning protocol for finding topological indices of quantum systems.
• The core of the proposed scheme is a 'topological data augmentation' procedure that uses seed objects to generate ensembles of topologically equivalent data.
• Such data, assigned with dummy labels, can then be used to train a neural network classifier for sorting arbitrary objects into topological equivalence classes.