# Topological Trio

Date and time |
Oct 02, 2018 from 12:00 PM to 01:00 PM |
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Location | 60 St. George Street, MP 606 |

Host | Joseph Thywissen |

## Hui Zhai

**
Institute for Advanced Study, Tsinghua University
**

## Abstract

In this talk, I will describe three recent works related to topological physics, on the topics of non-equilibrium dynamics, strongly interacting physics, and machine learning, respectively.

Non-Equilibrium
Dynamics: Previous studies of topological effects have mostly focused
on equilibrium or near-equilibrium situations. We show that the
topological invariant can also manifest its physical effect in a quench
dynamics far from equilibrium.

Interaction
Effect: We utilize the recently proposed Sachdev-Ye-Kitaev model and
construct an exactly solvable model to address the interaction effect in
a topological band insulator. An interaction-induced topological
transition and its critical behaviors can be shown explicitly by this
model.

Machine Learning: We show that we can
train a neural network to predict accurately a topological invariant
from local input, and without human knowledge as a prior. We also
analyze the neural network to show that what is captured by the neural
network is precisely the mathematical formula for topological invariant.

References:

[1] Ce Wang, Pengfei Zhang, Xin Chen, Jinlong Yu, and Hui Zhai,

*Phys. Rev. Lett*.**118**, 185701 (2017)[2] Pengfei Zhang, Huitao Shen, and Hui Zhai,

*Phys. Rev. Lett*.**120**, 066401 (2018)[3] Pengfei Zhang and Hui Zhai,

*Phys. Rev. B***97**, 201112(R) (2018)

JOINT QO/AMO AND CMP SEMINAR

Please note non standard day and location.

Contact Name | Joane Magnaye |
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