Neural networks in quantum many-body physics: a hands-on tutorial
- URL: http://arxiv.org/abs/2101.11099v1
- Date: Tue, 26 Jan 2021 22:04:29 GMT
- Title: Neural networks in quantum many-body physics: a hands-on tutorial
- Authors: Juan Carrasquilla, Giacomo Torlai
- Abstract summary: In this Article, we overview some applications of machine learning in condensed matter physics and quantum information.
We present supervised machine learning with convolutional neural networks to learn a phase transition, unsupervised learning with restricted Boltzmann machines to perform quantum tomography, and variational Monte Carlo with recurrent neural-networks for approximating the ground state of a many-body Hamiltonian.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Over the past years, machine learning has emerged as a powerful computational
tool to tackle complex problems over a broad range of scientific disciplines.
In particular, artificial neural networks have been successfully deployed to
mitigate the exponential complexity often encountered in quantum many-body
physics, the study of properties of quantum systems built out of a large number
of interacting particles. In this Article, we overview some applications of
machine learning in condensed matter physics and quantum information, with
particular emphasis on hands-on tutorials serving as a quick-start for a
newcomer to the field. We present supervised machine learning with
convolutional neural networks to learn a phase transition, unsupervised
learning with restricted Boltzmann machines to perform quantum tomography, and
variational Monte Carlo with recurrent neural-networks for approximating the
ground state of a many-body Hamiltonian. We briefly review the key ingredients
of each algorithm and their corresponding neural-network implementation, and
show numerical experiments for a system of interacting Rydberg atoms in two
dimensions.
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