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arXiv AI By Saleh Bunaiyan, Corentin Delacour, Shuvro Chowdhury, Kyle Lee, Abdelrahman S. Abdelrahman, Kerem Y. Camsari

IsingFormer: Augmenting Parallel Tempering With Learned Proposals

Read the original on arXiv AI →

The paper introduces IsingFormer, a Transformer model trained on long‑run MCMC configurations, which provides global proposal moves for Parallel Tempering (PT). By integrating these learned proposals into PT—forming Transformer‑Augmented Parallel Tempering (TAPT)—the authors demonstrate lower residual energies on 3D spin‑glass instances and improved efficiency on integer factorization tasks. A scaling study shows TAPT reduces the time‑to‑solution exponent by about 33% compared to standard PT across tested problem sizes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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