This paper explores the use of transformer models as wavefunction ansatz for variational ground state searches in simulating qubit systems, specifically two-dimensional Rydberg atom arrays. The authors demonstrate that transformers achieve higher accuracies than conventional recurrent neural networks (RNNs) and introduce a novel architecture, large patched transformers (LPTFs), to accelerate simulations. LPTFs combine a powerful patched transformer model with an efficient patched RNN, significantly reducing computational costs while maintaining high accuracy. The results surpass state-of-the-art quantum Monte Carlo methods, enabling the study of large Rydberg systems and their phase transitions.
Publisher
Communications Physics
Published On
Mar 11, 2024
Authors
Kyle Sprague, Stefanie Czischek
Tags
transformer models
wavefunction ansatz
variational ground state
Rydberg atom arrays
large patched transformers
computational costs
quantum Monte Carlo
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