Autoregressive neural quantum states of Fermi Hubbard models
Neural quantum states (NQSs) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly correlated systems. Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to the Fermi-Hubbard and the (non-Hermitian) Hatano-Nelson-Hubbard...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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American Physical Society
2025-02-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.7.013122 |
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| author | Eduardo Ibarra-García-Padilla Hannah Lange Roger G. Melko Richard T. Scalettar Juan Carrasquilla Annabelle Bohrdt Ehsan Khatami |
| author_facet | Eduardo Ibarra-García-Padilla Hannah Lange Roger G. Melko Richard T. Scalettar Juan Carrasquilla Annabelle Bohrdt Ehsan Khatami |
| author_sort | Eduardo Ibarra-García-Padilla |
| collection | DOAJ |
| description | Neural quantum states (NQSs) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly correlated systems. Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to the Fermi-Hubbard and the (non-Hermitian) Hatano-Nelson-Hubbard models in one and two dimensions. In both cases, we observe that the convergence of the RNN ansatz is challenged when increasing the interaction strength. We present a physically motivated and easy-to-implement strategy for improving the optimization, namely, by ramping of the model parameters. Furthermore, we investigate the advantages and disadvantages of the autoregressive sampling property of both network architectures. For the Hatano-Nelson-Hubbard model, we identify convergence issues that stem from the autoregressive sampling scheme in combination with the non-Hermitian nature of the model. Our findings provide insights into the challenges of the NQS approach and make the first step towards exploring strongly correlated electrons using this ansatz. |
| format | Article |
| id | doaj-art-80c99a5c92b447ab988d0acf66d13823 |
| institution | Kabale University |
| issn | 2643-1564 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj-art-80c99a5c92b447ab988d0acf66d138232025-02-03T15:03:28ZengAmerican Physical SocietyPhysical Review Research2643-15642025-02-017101312210.1103/PhysRevResearch.7.013122Autoregressive neural quantum states of Fermi Hubbard modelsEduardo Ibarra-García-PadillaHannah LangeRoger G. MelkoRichard T. ScalettarJuan CarrasquillaAnnabelle BohrdtEhsan KhatamiNeural quantum states (NQSs) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly correlated systems. Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to the Fermi-Hubbard and the (non-Hermitian) Hatano-Nelson-Hubbard models in one and two dimensions. In both cases, we observe that the convergence of the RNN ansatz is challenged when increasing the interaction strength. We present a physically motivated and easy-to-implement strategy for improving the optimization, namely, by ramping of the model parameters. Furthermore, we investigate the advantages and disadvantages of the autoregressive sampling property of both network architectures. For the Hatano-Nelson-Hubbard model, we identify convergence issues that stem from the autoregressive sampling scheme in combination with the non-Hermitian nature of the model. Our findings provide insights into the challenges of the NQS approach and make the first step towards exploring strongly correlated electrons using this ansatz.http://doi.org/10.1103/PhysRevResearch.7.013122 |
| spellingShingle | Eduardo Ibarra-García-Padilla Hannah Lange Roger G. Melko Richard T. Scalettar Juan Carrasquilla Annabelle Bohrdt Ehsan Khatami Autoregressive neural quantum states of Fermi Hubbard models Physical Review Research |
| title | Autoregressive neural quantum states of Fermi Hubbard models |
| title_full | Autoregressive neural quantum states of Fermi Hubbard models |
| title_fullStr | Autoregressive neural quantum states of Fermi Hubbard models |
| title_full_unstemmed | Autoregressive neural quantum states of Fermi Hubbard models |
| title_short | Autoregressive neural quantum states of Fermi Hubbard models |
| title_sort | autoregressive neural quantum states of fermi hubbard models |
| url | http://doi.org/10.1103/PhysRevResearch.7.013122 |
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