变压器
量子
计算机科学
哈密顿量(控制论)
量子计算机
计算机工程
量子力学
物理
数学优化
数学
电压
作者
Yuanhang Zhang,Massimiliano Di Ventra
出处
期刊:Physical review
[American Physical Society]
日期:2023-02-22
卷期号:107 (7)
被引量:33
标识
DOI:10.1103/physrevb.107.075147
摘要
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.
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