qtealeaves 1.7.32

Quantum TEA's python tensor network library

qtealeaves

The qtealeaves library of Quantum TEA contains tensor network representation as python classes, e.g., MPS, TTN, LPTN, and TTO. qtealeaves is the API for building quantum models in Quantum TEA and has for example a single-tensor update ground state search for TTNs. Moreover, qtealeaves is backbone for running quantum circuits via Quantum matcha TEA and the frontend for the fortran backend running Quantum Green TEA, i.e., solving the Schrödinger equation.

Documentation

Here is the documentation. The documentation can also be built locally via sphinx. Building the documentation requires sphinx, sphinx-gallery, and sphinx_rtd_theme. These dependencies can be installed via pip install .[docs] after cloning the repository.

License

The project qtealeaves is hosted at the repository https://baltig.infn.it/quantum_tea_leaves/py_api_quantum_tea_leaves.git, and is licensed under the following license:

Apache License 2.0

The license applies to the files of this project as indicated in the header of each file, but not its dependencies.

Installation

Independent of the use-case, you have to install the dependencies. Then, there are the options using it as a stand-alone package, within quantum matcha TEA, or for quantum green TEA.

Local installation via pip

The package is available via PyPi and pip install qtealeaves. After cloning the repository, an local installation via pip is also possible via pip install ..

Dependencies

The python dependencies can be found in the pyproject.toml and are required independently of the following use-cases.

The qtealeaves package comes with the abstract definition of the tensor required for our tensor networks as well as with a dense tensor based on numpy and cupy. This tensor allows one to run simulations without symmetry on CPU and GPU. Other tensor for symmetries or using pytorch instead of numpy/cupy will become available in the future via Quantum Red TEA (qredtea).

Stand-alone package

If you are looking to explore small exact diagonalization examples, want to run a single-tensor update ground state search with TTNs, or have TN-states on files to be post-processed, you are ready to go.

qmatchatea simulations

Quantum circuit simulations via qmatchatea require both qredtea and qtealeaves as a dependency. Follow the instructions contained in the qmatchatea repository.