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• W3009104906 abstract "Although artificial neural networks have recently been proven to provide a promising new framework for constructing quantum many-body wave functions, the parametrization of a quantum wave function with non-abelian symmetries in terms of a Boltzmann machine inherently leads to biased results due to the basis dependence. We demonstrate that this problem can be overcome by sampling in the basis of irreducible representations instead of spins, for which the corresponding ansatz respects the non-abelian symmetries of the system. We apply our methodology to find the ground states of the one-dimensional antiferromagnetic Heisenberg (AFH) model with spin-1/2 and spin-1 degrees of freedom, and obtain a substantially higher accuracy than when using the s_{z} basis as an input to the neural network. The proposed ansatz can target excited states, which is illustrated by calculating the energy gap of the AFH model. We also generalize the framework to the case of anyonic spin chains." @default.
• W3009104906 created "2020-03-13" @default.
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• W3009104906 date "2020-03-02" @default.
• W3009104906 modified "2023-10-06" @default.
• W3009104906 title "Restricted Boltzmann Machines for Quantum States with Non-Abelian or Anyonic Symmetries" @default.
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• W3009104906 doi "https://doi.org/10.1103/physrevlett.124.097201" @default.
• W3009104906 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32202867" @default.
• W3009104906 hasPublicationYear "2020" @default.
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