sparse-ir: Optimal compression and sparse sampling of many-body propagators

sparse-ir: Optimal compression and sparse sampling of many-body propagators

We introduce sparse-ir, a collection of libraries to efficiently handle imaginary-time propagators, a central object in finite-temperature quantum many-body calculations. We leverage two concepts: firstly, the intermediate representation (IR), an optimal compression of the propagator with robust a p...

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Bibliographic Details
Main Authors: Markus Wallerberger, Samuel Badr, Shintaro Hoshino, Sebastian Huber, Fumiya Kakizawa, Takashi Koretsune, Yuki Nagai, Kosuke Nogaki, Takuya Nomoto, Hitoshi Mori, Junya Otsuki, Soshun Ozaki, Thomas Plaikner, Rihito Sakurai, Constanze Vogel, Niklas Witt, Kazuyoshi Yoshimi, Hiroshi Shinaoka
Format: Article
Language:English
Published: Elsevier 2023-02-01
Series:SoftwareX
Subjects:
Intermediate representation
Sparse sampling
Python
Julia
Fortran
Online Access:http://www.sciencedirect.com/science/article/pii/S2352711022001844

Internet

http://www.sciencedirect.com/science/article/pii/S2352711022001844

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