Abstract:Neural-network quantum states have emerged as a powerful variational framework for quantum many-body systems, with recent progress often driven by massively parallel architectures such as transformers. Recurrent neural network quantum states, however, are frequently regarded as intrinsically sequential and therefore less scalable. Here we revisit this view by showing that modern recurrent architectures can support fast, accurate, and computationally accessible neural quantum state simulations. Using autoregressive recurrent wave functions together with recent advances in parallelizable recurrence, we develop variational ansätze, called parallel scan recurrent neural quantum states (PSR-NQS), which can be trained efficiently within variational Monte Carlo in one and two spatial dimensions. We demonstrate accurate benchmark results and show that, with iterative retraining, our approach reaches two-dimensional spin lattices as large as $52\times52$ while remaining in agreement with available quantum Monte Carlo data. Our results establish recurrent architectures as a practical and promising route toward scalable neural quantum state simulations with modest computational resources.
| Comments: | 13 pages, 2 figures, 6 tables |
| Subjects: | Strongly Correlated Electrons (cond-mat.str-el); Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG); Computational Physics (physics.comp-ph); Quantum Physics (quant-ph) |
| Cite as: | arXiv:2605.13807 [cond-mat.str-el] |
| (or arXiv:2605.13807v1 [cond-mat.str-el] for this version) | |
| https://doi.org/10.48550/arXiv.2605.13807 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Mohamed Hibat-Allah [view email]
[v1]
Wed, 13 May 2026 17:36:32 UTC (371 KB)