Welcome to PyTreeNet’s documentation!

PyTreeNet is a Python implemention of tree tensor networks with a focus on the simulation of quantum systems admitting a tree topology. It is based on the array implementation of NumPy.

Main Features

• General tree tensor networks, tree tensor networks states and tree tensor network operators

• Conversion of symbolic Hamiltonians into tree tensor networks

• Time-evolution algorithms: TEBD, one-site TDVP and two-site TDVP, BUG

Installation

To install PyTeNet from PyPI, call

python3 -m pip install pytreenet

Alternatively, you can clone the repository and install it in development mode via

python3 -m pip install -e <path/to/repo>

Indices and tables

• Index

• Module Index

• Search Page

• pytreenet package
  • Subpackages
    • pytreenet.contractions package
    • pytreenet.core package
    • pytreenet.operators package
    • pytreenet.random package
    • pytreenet.special_ttn package
    • pytreenet.time_evolution package
    • pytreenet.ttno package
    • pytreenet.ttns package
    • pytreenet.util package

Contributing

You are welcome to contribute to PyTreeNet. For code contributions create a pull request on GitHub. For bug reports and feature requests, please open an issue on GitHub. Please document code contributions using Google style docstrings and unit-test it with tests placed in the tests folder.

Citing

If you use PyTreeNet in your work, please cite the accompanying paper PyTreeNet: A Python Library for easy Utilisation of Tree Tensor Networks (arxiv:2407.13249) <https://arxiv.org/abs/2407.13249>

License

PyTreeNet is licensed under the EUPL v1.2.

References

1. Y.-Y. Shi, L.-M. Duan, G. Vidal,

   Classical simulation of quantum many-body systems with a tree tensor network

   Phys. Rev. A 74, 022320 (2006) (arXiv:quant-ph/0511070)
2. D. Bauernfeind, M. Aichhorn,

   Time dependent variational principle for tree Tensor Networks

   SciPost Physics 8, 024 (2020) (arXiv:1908.03090)
3. P. Silvi, F. Tschirsich, M. Gerster, J. Jünemann, D. Jaschke, M. Rizzi, S. Montangero

   The Tensor Networks Anthology: Simulation techniques for many-body quantum lattice systems

   SciPost Phys. Lect. Notes, 8 (2019) (arxiv:1710.03733)
4. G. Ceruti, C. Lubich, D. Sulz,

   Rank-adaptive time integration of tree tensor networks

   SIAM JNA, Vol. 6, Iss. 1, 194 - 222 (2023) (arXiv:2201.10291)