adapol: Adaptive pole-fitting for quantum many-body physics
Abstract
We describe adapol, a Python package for fitting Matsubara Green's functions, self-energies, and hybridization functions by a compact sum of simple poles with real pole locations and matrix-valued residues. This pole-fitting step arises in many contexts, including hybridization fitting for quantum impurity solvers based on exact diagonalization, perturbation theory, and tensor networks, the compression of existing pole expansions such as the discrete Lehmann representation, diagrammatic methods based on discrete pole representations, and the analytic continuation of Matsubara Green's functions. adapol uses a modified version of the AAA rational approximation algorithm to estimate pole locations, and non-convex optimization to refine them. This procedure yields accurate and compact fits in a black-box and noise-robust manner. adapol provides a simple, self-contained interface with extensive documentation and examples, as well as an interface to the TRIQS package.
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BibTeX
@misc{huang2026adapol,
title = {{adapol: Adaptive pole-fitting for quantum many-body physics}},
author = {Zhen Huang and Chia-Nan Yeh and Lin Lin and Nils Wentzell and Jason Kaye and Hugo U. R. Strand},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.40150},
eprint = {2609.40150},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.40150},
}
RIS
TY - UNPB
TI - adapol: Adaptive pole-fitting for quantum many-body physics
AU - Zhen Huang
AU - Chia-Nan Yeh
AU - Lin Lin
AU - Nils Wentzell
AU - Jason Kaye
AU - Hugo U. R. Strand
PY - 2026
DA - 2026/09/30
PB - arXiv
DO - 10.48550/arxiv.2609.40150
UR - https://arxiv.org/abs/2609.40150
AB - We describe adapol, a Python package for fitting Matsubara Green's functions, self-energies, and hybridization functions by a compact sum of simple poles with real pole locations and matrix-valued residues. This pole-fitting step arises in many contexts, including hybridization fitting for quantum impurity solvers based on exact diagonalization, perturbation theory, and tensor networks, the compression of existing pole expansions such as the discrete Lehmann representation, diagrammatic methods based on discrete pole representations, and the analytic continuation of Matsubara Green's functions. adapol uses a modified version of the AAA rational approximation algorithm to estimate pole locations, and non-convex optimization to refine them. This procedure yields accurate and compact fits in a black-box and noise-robust manner. adapol provides a simple, self-contained interface with extensive documentation and examples, as well as an interface to the TRIQS package.
ER -