An adaptive patching method exploits block-sparse QTT structures to reduce computational costs for tensor contractions and enables efficient evaluation of bubble diagrams and Bethe-Salpeter equations.
A causality-based divide-and-conquer algorithm for nonequilibrium Green’s function calculations with quantics tensor trains, September 2025
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H-NESSi uses HODLR compression of Green's functions plus DLR for initial states to reduce Kadanoff-Baym equation propagation from cubic to lower scaling while preserving controllable accuracy.
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Adaptive Patching for Tensor Train Computations
An adaptive patching method exploits block-sparse QTT structures to reduce computational costs for tensor contractions and enables efficient evaluation of bubble diagrams and Bethe-Salpeter equations.