Lange, Hannah and Bornet, Guillaume and Emperauger, Gabriel and Chen, Cheng and Lahaye, Thierry and Kienle, Stefan and Browaeys, Antoine and Bohrdt, Annabelle (2025) Transformer neural networks and quantum simulators: a hybrid approach for simulating strongly correlated systems. QUANTUM, 9: 1675. ISSN 2521-327X,
Full text not available from this repository.Abstract
Owing to their great expressivity and versatility, neural networks have gained attention for simulating large two-dimensional quantum many-body systems. However, their expressivity comes with the cost of a challenging optimization due to the in general rugged and complicated loss landscape. Here, we present a hybrid optimization scheme for neural quantum states (NQS), involving a data-driven pretraining with numerical or experimental data and a second, Hamiltonian-driven optimization stage. By using both projective measurements from the computational basis as well as expectation values from other measurement configurations such as spin-spin correlations, our pretraining gives access to the sign structure of the state, yielding improved and faster convergence that is robust w.r.t. experimental imperfections and limited datasets. We apply the hybrid scheme to the ground state search for the 2D transverse field Ising model and dipolar XY model on 6 x 6 and 10 x 10 square lattices with a patched transformer wave function, using numerical data as well as experimental data from a programmable Rydb erg quantum simulator [Chen et al., Nature 616 (2023)], and show that the information from a second measurement basis highly improves the performance. Our work paves the way for a reliable and efficient optimization of neural quantum states.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | STATE; |
| Subjects: | 500 Science > 530 Physics |
| Divisions: | Physics > Institute of Theroretical Physics > Chair Professor Grifoni > Group John Schliemann |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 19 May 2026 05:18 |
| Last Modified: | 19 May 2026 05:18 |
| URI: | https://pred.uni-regensburg.de/id/eprint/67478 |
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