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pyTTN: An open-source toolbox for open and closed system quantum dynamics simulations using tree tensor networks.

Lachlan P Lindoy1, Daniel Rodrigo-Albert1, Yannic Rath1

  • 1National Physical Laboratory, Teddington TW11 0LW, United Kingdom.

The Journal of Chemical Physics
|November 24, 2025
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Summary
This summary is machine-generated.

We introduce the Python Tree Tensor Network (pyTTN) package for simulating quantum systems. This tool efficiently models quantum dynamics using Tree Tensor Networks (TTN) for both closed and open systems.

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Area of Science:

  • Computational Physics
  • Quantum Chemistry
  • Quantum Information Science

Background:

  • Accurate simulation of quantum systems is crucial for understanding molecular dynamics and material properties.
  • Existing methods often face scalability challenges with increasing system size and complexity.
  • Tree Tensor Network (TTN) based wave function representations offer a promising approach for efficient quantum simulations.

Purpose of the Study:

  • To present the Python Tree Tensor Network (pyTTN) package for evaluating dynamical properties of quantum systems.
  • To enable efficient simulations of both closed and open quantum systems using TTN ansätze.
  • To provide a user-friendly and extendable platform for advanced quantum dynamics research.

Main Methods:

  • Utilizes Tree Tensor Network (TTN) representations for wave functions.
  • Implements adaptive bond dimension techniques via subspace expansion for efficiency.
  • Supports zero- and finite-temperature calculations for general Hamiltonians.
  • Includes tools for open quantum system dynamics, such as hierarchical equations of motion and generalized quasi-Lindblad methods.

Main Results:

  • Demonstrates the package's capability through benchmark simulations of photo-excitation dynamics in pyrazine.
  • Applies pyTTN to a challenging model of exciton dynamics in a donor-acceptor system.
  • Successfully models various open quantum systems, including the spin-boson model and Anderson impurity model.

Conclusions:

  • The pyTTN package offers a powerful, efficient, and user-friendly tool for quantum dynamics simulations.
  • Its design facilitates integration into broader computational modeling workflows.
  • pyTTN is well-suited for diverse applications in condensed matter physics, quantum chemistry, and quantum information science.