Neural network quantum states analysis of the Shastry-Sutherland model
Matěj Mezera, Jana Menšíková, Pavel Baláž, Martin Žonda
SciPost Phys. Core 6, 088 (2023) · published 22 December 2023
- doi: 10.21468/SciPostPhysCore.6.4.088
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Abstract
We utilize neural network quantum states (NQS) to investigate the ground state properties of the Heisenberg model on a Shastry-Sutherland lattice using the variational Monte Carlo method. We show that already relatively simple NQSs can be used to approximate the ground state of this model in its different phases and regimes. We first compare several types of NQSs with each other on small lattices and benchmark their variational energies against the exact diagonalization results. We argue that when precision, generality, and computational costs are taken into account, a good choice for addressing larger systems is a shallow restricted Boltzmann machine NQS. We then show that such NQS can describe the main phases of the model in zero magnetic field. Moreover, NQS based on a restricted Boltzmann machine correctly describes the intriguing plateaus forming in magnetization of the model as a function of increasing magnetic field.
Cited by 1
Authors / Affiliations: mappings to Contributors and Organizations
See all Organizations.- 1 2 Matej Mezera,
- 2 3 Jana Menšíková,
- 3 Pavel Baláž,
- 2 Martin Žonda
- 1 Freie Universität Berlin / Freie Universität Berlin [FU Berlin]
- 2 Charles University
- 3 Institute of Physics of the Czech Academy of Sciences [FZU]
- Grantová Agentura České Republiky (through Organization: Grantová agentura České republiky / Czech Science Foundation [GAČR])
- Ministerstvo Školství, Mládeže a Tělovýchovy (through Organization: Ministerstvo školství, mládeže a tělovýchovy České republiky / Ministry of Education Youth and Sports [MSMT])