Probing Criticality in Quantum Spin Chains with Neural Networks
- URL: http://arxiv.org/abs/2005.02104v2
- Date: Fri, 7 Aug 2020 16:51:35 GMT
- Title: Probing Criticality in Quantum Spin Chains with Neural Networks
- Authors: A Berezutskii, M Beketov, D Yudin, Z Zimbor\'as and J Biamonte
- Abstract summary: We show that even neural networks with no hidden layers can be effectively trained to distinguish between magnetically ordered and disordered phases.
Our results extend to a wide class of interacting quantum many-body systems and illustrate the wide applicability of neural networks to many-body quantum physics.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The numerical emulation of quantum systems often requires an exponential
number of degrees of freedom which translates to a computational bottleneck.
Methods of machine learning have been used in adjacent fields for effective
feature extraction and dimensionality reduction of high-dimensional datasets.
Recent studies have revealed that neural networks are further suitable for the
determination of macroscopic phases of matter and associated phase transitions
as well as efficient quantum state representation. In this work, we address
quantum phase transitions in quantum spin chains, namely the transverse field
Ising chain and the anisotropic XY chain, and show that even neural networks
with no hidden layers can be effectively trained to distinguish between
magnetically ordered and disordered phases. Our neural network acts to predict
the corresponding crossovers finite-size systems undergo. Our results extend to
a wide class of interacting quantum many-body systems and illustrate the wide
applicability of neural networks to many-body quantum physics.
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