Contributor info: Dr Markus Wallerberger

Lorenzo Crippa, Igor Krivenko, Samuele Giuli, Gabriele Bellomia, Alexander Kowalski, Francesco Petocchi, Alberto Scazzola, Markus Wallerberger, Giacomo Mazza, Luca de Medici, Giorgio Sangiovanni, Massimo Capone, Adriano Amaricci

SciPost Phys. Codebases 58 (2025) · published 24 September 2025 | Toggle abstract · pdf

We present a next-generation version of EDIpack, a flexible, high-performance numerical library using Lanczos-based exact diagonalization to solve generic quantum impurity problems, such as those introduced in Dynamical Mean-Field Theory to describe extended strongly correlated materials. This new release efficiently solves impurity problems allowing for different broken-symmetry solutions, including superconductivity, featuring local spin-orbit coupling and/or electron-phonon coupling. It provides quick access to dynamical correlation functions on the entire complex frequency plane at zero and low-temperatures. The modular architecture of the software not only provides Fortran APIs but also includes bindings to C/C++, interfaces with Python and Julia or with TRIQS and w2dynamics research platforms, thus ensuring unprecedented level of inter-operability. The outlook includes further extensions to study quantum materials and cold atoms quantum simulators, as well as quantum information applications.

Lorenzo Crippa, Igor Krivenko, Samuele Giuli, Gabriele Bellomia, Alexander Kowalski, Francesco Petocchi, Alberto Scazzola, Markus Wallerberger, Giacomo Mazza, Luca de Medici, Giorgio Sangiovanni, Massimo Capone, Adriano Amaricci

SciPost Phys. Codebases 58-r5.3 (2025) · published 24 September 2025 | Toggle abstract · src

We present a next-generation version of EDIpack, a flexible, high-performance numerical library using Lanczos-based exact diagonalization to solve generic quantum impurity problems, such as those introduced in Dynamical Mean-Field Theory to describe extended strongly correlated materials. This new release efficiently solves impurity problems allowing for different broken-symmetry solutions, including superconductivity, featuring local spin-orbit coupling and/or electron-phonon coupling. It provides quick access to dynamical correlation functions on the entire complex frequency plane at zero and low-temperatures. The modular architecture of the software not only provides Fortran APIs but also includes bindings to C/C++, interfaces with Python and Julia or with TRIQS and w2dynamics research platforms, thus ensuring unprecedented level of inter-operability. The outlook includes further extensions to study quantum materials and cold atoms quantum simulators, as well as quantum information applications.

Hiroshi Shinaoka, Naoya Chikano, Emanuel Gull, Jia Li, Takuya Nomoto, Junya Otsuki, Markus Wallerberger, Tianchun Wang, Kazuyoshi Yoshimi

SciPost Phys. Lect. Notes 63 (2022) · published 22 September 2022 | Toggle abstract · pdf

This lecture note reviews recently proposed sparse-modeling approaches for efficient ab initio many-body calculations based on the data compression of Green's functions. The sparse-modeling techniques are based on a compact orthogonal basis, an intermediate representation (IR) basis, for imaginary-time and Matsubara Green's functions. A sparse sampling method based on the IR basis enables solving diagrammatic equations efficiently. We describe the basic properties of the IR basis, the sparse sampling method and its applications to ab initio calculations based on the GW approximation and the Migdal-Eliashberg theory. We also describe a numerical library for the IR basis and the sparse sampling method, sparse-ir, and provide its sample codes. This lecture note follows the Japanese review article with major revisions [H. Shinaoka et al., Solid State Physics 56(6), 301 (2021)].

Hiroshi Shinaoka, Dominique Geffroy, Markus Wallerberger, Junya Otsuki, Kazuyoshi Yoshimi, Emanuel Gull, Jan Kuneš

SciPost Phys. 8, 012 (2020) · published 28 January 2020 | Toggle abstract · pdf

Many-body calculations at the two-particle level require a compact representation of two-particle Green's functions. In this paper, we introduce a sparse sampling scheme in the Matsubara frequency domain as well as a tensor network representation for two-particle Green's functions. The sparse sampling is based on the intermediate representation basis and allows an accurate extraction of the generalized susceptibility from a reduced set of Matsubara frequencies. The tensor network representation provides a system independent way to compress the information carried by two-particle Green's functions. We demonstrate efficiency of the present scheme for calculations of static and dynamic susceptibilities in single- and two-band Hubbard models in the framework of dynamical mean-field theory.