2 research outputs found
Featuring the topology with the unsupervised machine learning
Images of line drawings are generally composed of primitive elements. One of
the most fundamental elements to characterize images is the topology; line
segments belong to a category different from closed circles, and closed circles
with different winding degrees are nonequivalent. We investigate images with
nontrivial winding using the unsupervised machine learning. We build an
autoencoder model with a combination of convolutional and fully connected
neural networks. We confirm that compressed data filtered from the trained
model retain more than 90% of correct information on the topology, evidencing
that image clustering from the unsupervised learning features the topology.Comment: 14 pages, 7 figure
Topological quantum phase transitions retrieved through unsupervised machine learning
The discovery of topological features of quantum states plays an important
role in modern condensed matter physics and various artificial systems. Due to
the absence of local order parameters, the detection of topological quantum
phase transitions remains a challenge. Machine learning may provide effective
methods for identifying topological features. In this work, we show that the
unsupervised manifold learning can successfully retrieve topological quantum
phase transitions in momentum and real space. Our results show that the
Chebyshev distance between two data points sharpens the characteristic features
of topological quantum phase transitions in momentum space, while the widely
used Euclidean distance is in general suboptimal. Then a diffusion map or
isometric map can be applied to implement the dimensionality reduction, and to
learn about topological quantum phase transitions in an unsupervised manner. We
demonstrate this method on the prototypical Su-Schrieffer-Heeger (SSH) model,
the Qi-Wu-Zhang (QWZ) model, and the quenched SSH model in momentum space, and
further provide implications and demonstrations for learning in real space,
where the topological invariants could be unknown or hard to compute. The
interpretable good performance of our approach shows the capability of manifold
learning, when equipped with a suitable distance metric, in exploring
topological quantum phase transitions.Comment: Accepted by Physical Review B (2020); Updated version including
implications and demonstrations for learning in real space, and with
appendices; Typos were corrected; 14 pages, 12 figure