Neural network quantum states analysis of the Shastry-Sutherland model
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 dif...
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| Format: | Article |
| Language: | English |
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SciPost
2023-12-01
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| Series: | SciPost Physics Core |
| Online Access: | https://scipost.org/SciPostPhysCore.6.4.088 |
| _version_ | 1797379028344111104 |
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| author | Matěj Mezera, Jana Menšíková, Pavel Baláž, Martin Žonda |
| author_facet | Matěj Mezera, Jana Menšíková, Pavel Baláž, Martin Žonda |
| author_sort | Matěj Mezera, Jana Menšíková, Pavel Baláž, Martin Žonda |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T20:16:13Z |
| format | Article |
| id | doaj.art-9f835c0d1d9440eea9856c28071ff891 |
| institution | Directory Open Access Journal |
| issn | 2666-9366 |
| language | English |
| last_indexed | 2024-03-08T20:16:13Z |
| publishDate | 2023-12-01 |
| publisher | SciPost |
| record_format | Article |
| series | SciPost Physics Core |
| spelling | doaj.art-9f835c0d1d9440eea9856c28071ff8912023-12-22T15:20:27ZengSciPostSciPost Physics Core2666-93662023-12-016408810.21468/SciPostPhysCore.6.4.088Neural network quantum states analysis of the Shastry-Sutherland modelMatěj Mezera, Jana Menšíková, Pavel Baláž, Martin ŽondaWe 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.https://scipost.org/SciPostPhysCore.6.4.088 |
| spellingShingle | Matěj Mezera, Jana Menšíková, Pavel Baláž, Martin Žonda Neural network quantum states analysis of the Shastry-Sutherland model SciPost Physics Core |
| title | Neural network quantum states analysis of the Shastry-Sutherland model |
| title_full | Neural network quantum states analysis of the Shastry-Sutherland model |
| title_fullStr | Neural network quantum states analysis of the Shastry-Sutherland model |
| title_full_unstemmed | Neural network quantum states analysis of the Shastry-Sutherland model |
| title_short | Neural network quantum states analysis of the Shastry-Sutherland model |
| title_sort | neural network quantum states analysis of the shastry sutherland model |
| url | https://scipost.org/SciPostPhysCore.6.4.088 |
| work_keys_str_mv | AT matejmezerajanamensikovapavelbalazmartinzonda neuralnetworkquantumstatesanalysisoftheshastrysutherlandmodel |