Inspired by the advancements in large language models based on transformers, we introduce the transformer quantum state (TQS): a versatile machine learning model for quantum many-body problems. In sharp contrast to Hamiltonian/task specific models, TQS can generate the entire phase diagram, predict field strengths with experimental measurements, and transfer such a knowledge to new systems it has never been trained on before, all within a single model. With specific tasks, fine-tuning the TQS produces accurate results with small computational cost. Versatile by design, TQS can be easily adapted to new tasks, thereby pointing towards a general-purpose model for various challenging quantum problems.
@article{arxiv.2208.01758,
title = {Transformer Quantum State: A Multi-Purpose Model for Quantum Many-Body Problems},
author = {Yuan-Hang Zhang and Massimiliano Di Ventra},
journal= {arXiv preprint arXiv:2208.01758},
year = {2023}
}