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

TL;DR

The paper presents sparse-ir, a set of libraries that use optimal compression and sparse sampling techniques to efficiently handle imaginary-time propagators in finite-temperature quantum many-body calculations.

Contribution

It introduces the sparse-ir libraries that combine the intermediate representation and sparse sampling for efficient propagator handling, with implementations in Python, Julia, and Fortran.

Findings

Efficient handling of imaginary-time propagators in many-body calculations.

Robust error estimates for the compressed propagators.

Sample codes demonstrating practical applications.

Abstract

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-priori error estimates, and secondly, sparse sampling, near-optimal grids in imaginary time and imaginary frequency from which the propagator can be reconstructed and on which diagrammatic equations can be solved. IR and sparse sampling are packaged into stand-alone, easy-to-use Python, Julia and Fortran libraries, which can readily be included into existing software. We also include an extensive set of sample codes showcasing the library for typical many-body and ab initio methods.