Pauli Hamiltonians
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Attention-based optimizer for symmetry finding
arXiv:2605.30429v1 Announce Type: cross Abstract: Finding symmetries is crucial for understanding physical models. In this work, we present an optimization framework that searches Pauli symmetries of Hamiltonians, merging the fields of machine learning with automated symmetry finding. Built on a Set-Transformer architecture, our framework uses self-attention to encode the pairwise and higher-order correlations among the Pauli-Strings.
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