@article{10.1021/acs.jctc.6c00525,
    author = {Rodriguez-Betancourt, Juan C. and Anderson, Michelle C. and Niu, Luchang and Chen, Xinxian and Franco, Ignacio},
    title = {TENSO: Software Package for
Numerically Exact Open Quantum Dynamics Based on Efficient Tree Tensor
Network Decomposition of the Hierarchical Equations of Motion},
    journal = {Journal of Chemical Theory and Computation},
    volume = {22},
    number = {14},
    pages = {7048-7069},
    year = {2026},
    month = {07},
    abstract = {TENSO is a versatile and powerful
open-source
software package for numerically exact simulations of the dynamics
of quantum systems immersed in structured thermal environments. It
is based on a tree tensor network decomposition of the hierarchical
equations of motion (HEOM) that efficiently curtails its curse of
dimensionality with bath complexity. As such, TENSO enables exact non-Markovian open-quantum-dynamics simulations even
with complex environments typical of chemistry and quantum information
science. TENSO allows for time-dependent drive
in the system and for noncommuting fluctuations. More generally, TENSO efficiently propagates the dynamics for any method
with a generator of the dynamics that can be expressed in a sum-of-products
form, including the HEOM and multilayer multiconfigurational time-dependent
Hartree methods. TENSO enables simulations
using tensor trees and trains of arbitrary order and implements three
propagation strategies for the coupled master equations: two fixed
rank methods that require a constant memory footprint during the dynamics
and one adaptive rank method with a variable memory footprint controlled
by the target level of computational error. In contrast to the accompanying
theory and algorithmic paper [ 

J. Chem. Phys.
2025, 163, 104109. ], the focus here is on the practical usage
and applications of TENSO with underlying theoretical
concepts introduced only as needed.},
    issn = {1549-9618},
    doi = {10.1021/acs.jctc.6c00525},
    url = {https://doi.org/10.1021/acs.jctc.6c00525},
    eprint = {https://pubs.acs.org/jctcce/article-pdf/22/14/7048/65965735/acs.jctc.6c00525.pdf},
}