TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysCore.6.4.088
TI  - Neural network quantum states analysis of the Shastry-Sutherland model
PY  - 2023/12/22
UR  - https://scipost.org/SciPostPhysCore.6.4.088
JF  - SciPost Physics Core
JA  - SciPost Phys. Core
VL  - 6
IS  - 4
SP  - 088
A1  - Mezera, Matej
AU  - Menšíková, Jana
AU  - Baláž, Pavel
AU  - Žonda, Martin
AB  - 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.
ER  -

@Article{10.21468/SciPostPhysCore.6.4.088,
	title={{Neural network quantum states analysis of the Shastry-Sutherland model}},
	author={Matěj Mezera and Jana Menšíková and Pavel Baláž and Martin Žonda},
	journal={SciPost Phys. Core},
	volume={6},
	pages={088},
	year={2023},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCore.6.4.088},
	url={https://scipost.org/10.21468/SciPostPhysCore.6.4.088},
}