English(EN) Generalization of Transformer-Based Neural Quantum States via In-Context Learning

新理论解释了Transformer神经量子态的泛化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员开发了一个理论框架，以理解基于Transformer的神经量子态在上下文学习中的泛化能力。该研究建立了泛化误差界限，表明预测误差随着上下文示例的增多和Transformer深度的增加而减小。这种深度要求与量子系统的大小成线性关系，数值模拟支持了这些发现。 AI

影响为Transformer架构在量子物理应用中的泛化能力提供了理论基础。

排序理由学术论文，详细阐述了对机器学习模型泛化特性的理论分析。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Zhen Qin, Qing Qu, Alfred O. Hero III · 2026-10-05 04:00

基于Transformer的神经量子态通过上下文学习实现泛化

arXiv:2610.03463v1 Announce Type: cross Abstract: Neural quantum states based on modern deep learning architectures have emerged as powerful representations for quantum many-body systems. In particular, Transformer-based neural quantum states provide expressive models capable of …