Scalable Physics-Inspired Transformers for Spin Glasses
Efficient sampling of the Boltzmann distribution in frustrated spin glasses is central to statistical mechanics and combinatorial optimization. Despite advances in machine-learning-based approaches, two issues persist: limited understanding of why variational models fail to benefit from increased scale, unlike the monotonic scaling law of large language models; and high computational cost on large systems that negates advantages over classical sampling methods. Here, we develop a physics-inspired transformer with interpretable sparse attention and spin-tailored positional embeddings to address these challenges. By further leveraging FlashAttention for parallel ancestral sampling, it achie...
| Authors | Lu Zhong, Wenli Duan, Jing Liu et al. |
|---|---|
| Primary category | cond-mat.dis-nn |
| Published | 22 June 2026 |
| Last updated at source | 23 June 2026 |
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