170 citations · 2.1k across the 78 of their papers we have counts for
9 papers · 1 filter
Parallelized contraction of tensor trains or matrix product operators
Simone Foderà, Marc K. Ritter, Hiroshi Shinaoka +1
Tensor Trains (TT), also known as Matrix Product States (MPS) and Matrix Product Operators (MPO), provide a compact and structured representation for high-dimensional data and oper…
Adaptive Patching for Tensor Train Computations
Gianluca Grosso, Marc K. Ritter, Stefan Rohshap +5
Quantics Tensor Train (QTT) operations such as matrix product operator contractions are prohibitively expensive for large bond dimensions. We propose an adaptive patching scheme th…
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…
Quantics Tensor Cross Interpolation for High-Resolution, Parsimonious Representations of Multivariate Functions in Physics and Beyond
Marc K. Ritter, Yuriel Núñez Fernández, Markus Wallerberger +3
Multivariate functions of continuous variables arise in countless branches of science. Numerical computations with such functions typically involve a compromise between two contrar…
Nevanlinna.jl: A Julia implementation of Nevanlinna analytic continuation
Kosuke Nogaki, Jiani Fei, Emanuel Gull +1
We introduce a Julia implementation of the recently proposed Nevanlinna analytic continuation method. The method is based on Nevanlinna interpolants and, by construction, preserves…
sparse-ir: optimal compression and sparse sampling of many-body propagators
Markus Wallerberger, Samuel Badr, Shintaro Hoshino +13
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 levera…