  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.
