Excited Pfaffians: New Neural Network Architecture for Quantum States

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-28 04:00

研究人员开发了一种名为 Excited Pfaffians 的新型神经网络架构，旨在更有效地表示多个量子态。与传统方法相比，这种方法显著降低了计算成本，能够实现更快的训练和对更多状态的建模。该架构已成功应用于二聚碳和铍原子等复杂系统，这在这些领域的神经网络应用中尚属首次。 AI

影响引入了一种新颖的神经网络架构，可显著加速量子态计算，有望在计算化学和物理学领域带来新发现。

排序理由这是一篇详细介绍量子力学模拟新方法和架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Nicholas Gao, Till Grutschus, Frank No\'e, Stephan G\"unnemann · 2026-05-28 04:00

Pfaffian 爱好者们：跨越结构与状态的广义神经波函数

arXiv:2603.14515v2 Announce Type: replace Abstract: Neural-network wave functions in Variational Monte Carlo (VMC) have achieved great success in accurately representing both ground and excited states. However, achieving sufficient numerical accuracy in state overlaps requires in…