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arXiv · 2412.14032

Memory-Efficient Nonequilibrium Green's Function Framework Built On Quantics Tensor Trains

Maksymilian Środa·Ken Inayoshi·Hiroshi Shinaoka·Philipp Werner
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Abstract

One of the challenges in diagrammatic simulations of nonequilibrium phenomena in lattice models is the large memory demand for storing momentum-dependent two-time correlation functions. This problem can be overcome with the recently introduced quantics tensor train (QTT) representation of multivariable functions. Here, we demonstrate nonequilibrium Green's function simulations within the $GW$ and Migdal approximations with high momentum resolution, up to times which exceed the capabilities of standard implementations and are long enough to study, e.g., transient Floquet physics during multi-cycle electric field pulses and thermalization dynamics. The self-consistent calculation on the three-leg Kadanoff-Baym contour is fully self-contained, employing only QTT-compressed functions and input functions which are either generated directly in QTT form or obtained via quantics tensor cross interpolation.

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Maksymilian Środa, Ken Inayoshi, Hiroshi Shinaoka, Philipp Werner. 2024-12-18. Memory-Efficient Nonequilibrium Green's Function Framework Built On Quantics Tensor Trains. https://doi.org/10.1103/dxfb-b3l5

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