论文标题
变压器量子状态：量子多体问题的多功能模型
Transformer Quantum State: A Multi-Purpose Model for Quantum Many-Body Problems
论文作者
论文摘要
受到基于变形金刚的大语言模型进步的启发，我们介绍了变压器量子状态（TQS）：一种用于量子多体问题的多功能机器学习模型。与哈密顿/任务特定模型形成鲜明对比的是，TQ可以生成整个相图，通过实验测量来预测现场强度，并将这种知识传输到以前从未有过的新系统，所有这些知识都在单个模型中。通过特定的任务，微调TQs可以以较小的计算成本产生准确的结果。通过设计多功能，TQ可以很容易地适应新任务，从而指出了针对各种具有挑战性的量子问题的通用模型。
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.
