Variational Monte Carlo with large patched transformers

Large language models, like transformers, have recently demonstrated immense powers in text and image generation. This success is driven by the ability to capture long-range correlations between elements in a sequence. The same feature makes the transformer a powerful wavefunction ansatz that addresses the challenge of describing correlations in simulations of qubit systems. Here we consider two-dimensional Rydberg atom arrays to demonstrate that transformers reach higher accuracies than conventional recurrent neural networks for variational ground state searches. We further introduce large, patched transformer models, which consider a sequence of large atom patches, and show that this architecture significantly accelerates the simulations. The proposed architectures reconstruct ground states with accuracies beyond state-of-the-art quantum Monte Carlo methods, allowing for the study of large Rydberg systems in different phases of matter and at phase transitions. Our high-accuracy ground state representations at reasonable computational costs promise new insights into general large-scale quantum many-body systems. Ground state representations with artificial neural network methods enable high-accuracy simulations of quantum many-body systems. The authors study the performance of the transformer network architecture on this task and demonstrate its vast potential for novel findings in quantum physics.