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arXiv 2406.09077cond-mat.str-elcond-mat.otherphysics.comp-phquant-ph
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使用对称感知神经回流变换对二维相互作用晶格电子的谱学研究

Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations

Imelda Romero, Jannes Nys, Giuseppe Carleo

AI总结:

本文提出一种将晶格对称性嵌入费米子波函数的群等变神经回流变换框架,用于研究偏离半填充的二维 t-V 模型,显著提升了基态能量与低能激发的计算精度,并准确识别相变临界点。

摘要:

神经网络已证明是表示量子多体系统(包括费米子系统)基态的强大工具。然而,将晶格对称性高效地整合到神经表示中仍然是一个重大挑战。在这项工作中,我们提出了一个将晶格对称性嵌入费米子波函数的框架,并展示了其同时求解基态和低能激发态的能力。使用群等变神经回流变换,我们研究了偏离半填充的方格上的 t-V 模型。我们的对称感知回流显著改善了基态能量,并为高达 10 x 10 的晶格提供了准确的低能激发。我们还计算了精确的两点密度相关函数和结构因子,以识别相变和临界点。这些发现引入了一个对研究量子材料和相变至关重要的对称感知框架。

英文摘要:

Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In this work, we introduce a framework for embedding lattice symmetries in fermionic wavefunctions and demonstrate its ability to target both ground states and low-lying excitations. Using group-equivariant neural backflow transformations, we study the t-V model on a square lattice away from half-filling. Our symmetry-aware backflow significantly improves ground-state energies and yields accurate low-energy excitations for lattices up to 10 x 10. We also compute accurate two-point density-correlation functions and the structure factor to identify phase transitions and critical points. These findings introduce a symmetry-aware framework important for studying quantum materials and phase transitions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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