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Python library for differentiable linear algebra with block-sparse tensors, supporting abelian symmetries. It supports a range of Projected Entangled Pair States (PEPS) and Matrix Product States (MPS) algorithms employing the core symmetric Tensor class.

• Z₂ for parity conservation
• U(1) for particle number conservation
• Direct product of abelian groups such as Z₃xU(1) or U(1)xU(1)xZ₂ and so on

• NumPy
• PyTorch

It allows automatic differentiation (autograd) on backends which support it.

• Quickstarts for a few end-to-end examples of PEPS calculations.
• Benchmarks, including basic DMRG example.
• Full documentation for multiple further examples.
• Two-dimensional tensor networks library peps-torch. It includes variational optimization of iPEPS with abelian symmetries for spin models powered by YASTN tensor.

git clone https://github.com/yastn/yastn.git
cd yastn
pip install .

or just clone and add YASTN root to your Python import path to successfully import yastn (see pyproject.toml for dependencies).

Tests, which are also a good source of usage examples, can be found in the folder tests. To verify that everything works, get pytest. To test specific elements of the repository, run

pytest -v ./tests/tensor
pytest -v ./tests/operators
pytest -v ./tests/mps
pytest -v ./tests/peps

To test YASTN on PyTorch backend and integration of autograd features (assuming PyTorch is installed)

pytest -v --backend torch

To execute the tests on a GPU

pytest -v --backend torch --device cuda

If you find this package useful for a project published in an academic journal, you can consider citing the release paper, with the following BibTex entry

@misc{yastn,
      title={YASTN: Yet another symmetric tensor networks;
               A Python library for abelian symmetric tensor network calculations},
      author={Marek M. Rams and Gabriela W\'{o}jtowicz and Aritra Sinha and Juraj Hasik},
      year={2024},
      eprint={2405.12196},
      archivePrefix={arXiv},
      url={https://arxiv.org/abs/2405.12196},
}