Sampling two-dimensional spin systems with transformers
Autoregressive Neural Networks based on dense or convolutional layers have recently been shown to be a viable strategy for generating classical spin systems. Unlike these methods, sampling with transformers is commonly considered to be computationally inefficient. In this work, we propose a novel approach to transformer-based neural samplers in which we generate not a single spin per step but groups of spins. As an additional improvement, we construct a model of approximated probabilities, further improving the efficiency of the algorithm. Despite our approach being computationally heavier than dense networks or CNN-based approaches, we were able to sample larger systems of up to $180 \ti...
| Authors | Piotr Białas, Piotr Korcyl, Tomasz Stebel et al. |
|---|---|
| Primary category | cond-mat.dis-nn |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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