Transformer quantum state: A multipurpose model for quantum many-body problems

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.

Automated summary

Inspired by transformers, TQS is a versatile machine learning model for quantum many-body problems, capable of generating phase diagrams, predicting experimental measurements, and adapting to new systems. It produces accurate results with small computational cost and can be easily adapted to new tasks, making it a general-purpose model for challenging quantum problems.