Lindoy, L P; Rodrigo-Albert, D; Rath, Y; Rungger, I (2025) pyTTN: An open-source toolbox for open and closed system quantum dynamics simulations using tree tensor networks. The Journal of Chemical Physics, 163 (20). 202501 ISSN 0021-9606

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Abstract

We present the Python Tree Tensor Network (pyTTN) package for the evaluation of dynamical properties of closed and open quantum systems that makes use of Tree Tensor Network (TTN) based representations of wave functions. This package includes several features allowing for easy setup of zero- and finite-temperature calculations for general Hamiltonians using single and multi-set TTN ansätze with an adaptive bond dimension using subspace expansion techniques. In addition to these core features, pyTTN provides several tools for setting up efficient simulations of open quantum system dynamics, including the use of the TTN ansatz to represent the auxiliary density operator space for the simulation of the hierarchical equation of motion method and generalized quasi-Lindblad pseudomode methods. We present a set of applications for the package, starting with the widely used benchmark case of the photo-excitation dynamics of 24 mode pyrazine, after which we consider a more challenging model describing the exciton dynamics at the interface of an n-oligothiophene donor–C60 fullerene acceptor system. Finally, we consider applications to open quantum systems, including the spin-boson model, a set of extended dissipative spin models, and an Anderson impurity model. By combining ease of use and an efficient implementation, along with an extendable design that allows for the addition of future extensions, pyTTN can be integrated into a wide range of computational modeling software.

Item Type:	Article
Keywords:	Tensor Network Methods, Quantum Dynamics, Anderson Impurity Model, Vibronic Dynamics
Subjects:	Quantum Phenomena > Quantum Information Processing and Communication
Divisions:	Quantum Technologies
Identification number/DOI:	10.1063/5.0301775
Last Modified:	21 May 2026 14:38
URI:	https://eprintspublications.npl.co.uk/id/eprint/10416