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.