June 10, 2024, 4:46 a.m. | Di Luo, Zhuo Chen, Kaiwen Hu, Zhizhen Zhao, Vera Mikyoung Hur, Bryan K. Clark

cs.LG updates on arXiv.org arxiv.org

arXiv:2101.07243v4 Announce Type: replace-cross
Abstract: Symmetries such as gauge invariance and anyonic symmetry play a crucial role in quantum many-body physics. We develop a general approach to constructing gauge invariant or anyonic symmetric autoregressive neural network quantum states, including a wide range of architectures such as Transformer and recurrent neural network (RNN), for quantum lattice models. These networks can be efficiently sampled and explicitly obey gauge symmetries or anyonic constraint. We prove that our methods can provide exact representation for …

abstract architectures arxiv autoregressive cond-mat.dis-nn cond-mat.str-el cs.lg general hep-lat lattice network neural network physics quant-ph quantum replace rnn role symmetry transformer type

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