6 papers
Parallelized contraction of tensor trains or matrix product operators
Simone FoderÃ, 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…
Fast elementwise operations on tensor trains with alternating cross interpolation
Marc K. Ritter
Tensor trains (TTs), also known as matrix product states (MPS), are compressed representations of high-dimensional data that can be efficiently manipulated to perform calculations…
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…
Compressing local vertex functions from the multipoint numerical renormalization group using quantics tensor cross interpolation
Markus Frankenbach, Marc Ritter, Mathias Pelz +3
The multipoint numerical renormalization group (mpNRG) is a powerful impurity solver that provides accurate spectral data useful for computing local, dynamic correlation functions…
Two-particle calculations with quantics tensor trains: Solving the parquet equations
Stefan Rohshap, Marc K. Ritter, Hiroshi Shinaoka +3
We present the first application of quantics tensor trains (QTTs) and tensor cross interpolation (TCI) to the solution of a full set of self-consistent equations for multivariate f…
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…