Evidence map›Paper›PMID 42455769›Full record

ArticleJournal of chemical theory and computation2026

TENSO: Software Package for Numerically Exact Open Quantum Dynamics Based on Efficient Tree Tensor Network Decomposition of the Hierarchical Equations of Motion.

Juan C Rodriguez-Betancourt, Michelle C Anderson, Luchang Niu, Xinxian Chen, Ignacio Franco

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Article in Journal of chemical theory and computation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Juan C Rodriguez-BetancourtDepartment of Chemistry, University of Rochester, Rochester, New York14627, United States.
Michelle C AndersonDepartment of Chemistry, University of Rochester, Rochester, New York14627, United States.ORCID 0000-0002-1351-7450
Luchang NiuDepartment of Physics and Astronomy, University of Rochester, Rochester, New York14627, United States.
Xinxian ChenDepartment of Chemistry, University of Rochester, Rochester, New York14627, United States.ORCID 0000-0002-0363-0097
Ignacio FrancoDepartment of Chemistry, University of Rochester, Rochester, New York14627, United States.ORCID 0000-0002-0802-8185

Funding

Basic Energy Sciences DE-SC0025334
6 · The paper itself

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.

Identifiers

PMID42455769
PMCPMC13420585

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