• 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

allowing for automatic differentiation (autograd) on backends which provide it.

• Matrix product states (MPS) and algorithms powered by YASTN

  Explore the entire MPS module or try out code examples running DMRG or TDVP optimizations

• Two-dimensional tensor networks library peps-torch powered by YASTN

  Try variational optimization of iPEPS with abelian symmetries for spin models

Install using

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 sucessfully import yastn.

• Python 3.9
• NumPy

and optionally

• PyTorch 1.11 (for PyTorch backend)
• SciPy (for sparse linear algebra solvers)
• ...

Tests, which are also a good source of examples of usage, can be found in the folder tests. To verify that everything works, get pytest. See Installing pytest or conda install -c conda-forge pytest. Then you can test base yastn.Tensor and also yastn.mps modules

cd tests
pytest -v ./tensor
pytest -v ./mps

To test YASTN on PyTorch backend and integration of autograd features

cd tests
pytest --backend torch -v ./tensor
pytest --backend torch -v ./mps

You can build documentation using sphinx. The prerequisites are

• sphinx
• pydata-sphinx-theme

Get them with your favourite Python package manager. For example, using conda as conda install -c conda-forge sphinx pydata-sphinx-theme. Then

cd docs && make html

The generated documentation can be found at docs/build/html/index.html