Neural network quantum states analysis of the Shastry-Sutherland model

0580185 - FZÚ 2024 RIV NL eng J - Journal Article
Mezera, M. - Menšíková, J. - Baláž, Pavel - Žonda, M.
Neural network quantum states analysis of the Shastry-Sutherland model.
Scipost Physics Core. Roč. 6, č. 4 (2023), č. článku 088. ISSN 2666-9366
R&D Projects: GA ČR GX19-28594X; GA ČR(CZ) GF23-05263K
Research Infrastructure: e-INFRA CZ - 90140
Institutional support: RVO:68378271
Keywords : Shastry-Sutherland model * neural networks * variational Monte Carlo * restricted Boltzmann machines
OECD category: Condensed matter physics (including formerly solid state physics, supercond.)
Method of publishing: Open access

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.

Permanent Link: https://hdl.handle.net/11104/0348949