activity
20222026
most citedLearning Feynman Diagrams with Tensor Trains

89 citations · 145 across the 4 of their papers we have counts for

collaborators

5 papers

cond-mat.str-el2026

Efficient Optimization of Tensor Rings with Low-Rank Environments

Matthieu Jeannin, Alessandro Chessari, Jan Von Delft

Tensor-ring (TR) decompositions provide a natural representation of periodic systems but are difficult to optimize because the closed geometry prevents a global canonical form and…

cond-mat.str-el2025★ 3 cited

A comprehensive study of out-of-equilibrium Kondo effect and Coulomb blockade

Matthieu Jeannin, Yuriel Núñez-Fernández, Thomas Kloss +2

We present a comprehensive set of numerically exact results for the Anderson model of a quantum dot coupled to two electrodes in non-equilibrium regime. We use a high order perturb…

physics.comp-ph2024★ 41 cited

Learning tensor networks with tensor cross interpolation: new algorithms and libraries

Yuriel Núñez Fernández, Marc K. Ritter, Matthieu Jeannin +8

The tensor cross interpolation (TCI) algorithm is a rank-revealing algorithm for decomposing low-rank, high-dimensional tensors into tensor trains/matrix product states (MPS). TCI…

cond-mat.str-el2024★ 12 cited

Cross-extrapolation reconstruction of low-rank functions and application to quantum many-body observables in the strong coupling regime

Matthieu Jeannin, Yuriel Núñez-Fernández, Thomas Kloss +2

We present a general-purpose algorithm to extrapolate a low rank function of two variables from a small domain to a larger one. It is based on the cross-interpolation formula. We a…

cond-mat.str-el2022★ 89 cited

Learning Feynman Diagrams with Tensor Trains

Yuriel Nunez-Fernandez, Matthieu Jeannin, Philipp T. Dumitrescu +4

We use tensor network techniques to obtain high order perturbative diagrammatic expansions for the quantum many-body problem at very high precision. The approach is based on a tens…