Autoregressive neural quantum states of Fermi Hubbard models

Journal Article · 02 February 2025 · Physical Review Research

DOI: https://doi.org/10.1103/PhysRevResearch.7.013122 · OSTI ID:2510912

Ibarra-García-Padilla, Eduardo ; Lange, Hannah ; Melko, Roger G. ; Scalettar, Richard T. ; Carrasquilla, Juan ; Bohrdt, Annabelle ; Khatami, Ehsan

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. Published by the American Physical Society 2025

• Sponsoring Organization: USDOE
• Grant/Contract Number: SC0022311
• OSTI ID: 2510912
• Journal Information: Physical Review Research, Journal Name: Physical Review Research Journal Issue: 1 Vol. 7; ISSN PPRHAI; ISSN 2643-1564
• Publisher: American Physical SocietyCopyright Statement
• Country of Publication: United States
• Language: English