qchaitea

The quantum chai tea library of Quantum TEA provides methods to handle machine learning datasets and use tensor network machine learning

Documentation

Here is the documentation. The documentation can also be built locally via sphinx.

License

The project qchaitea is hosted at the repository https://baltig.infn.it/quantum_tea_internal/py_api_quantum_chai_tea.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

The qchaitea library is never used as stand-alone package; it replaces the tensor backend for one of the quantum TEA applications in agreement with a qtealeaves version. Therefore, the minimal use-case to explore the library is together with qtealeaves. Moreover, it can be used as well with qmatchatea.

Local installation via pip

The package is available via a local pip installation as pip install ., i.e., after cloning the repository.

Dependencies

The python dependencies can be found in the pyproject.toml file and are required independently of the following use-cases. Optional dependencies are required for specific use-cases, as indicated in the file.

Project history

The project merges the effort of the Leto project with the aim to access the tensor network machine learning algorithms of qtealeaves in a workflow adapted to machine learning problems.

The Leto project originates in the master thesis of Massimo Colombo at the tensor ai solutions GmbH under the supervision of Marco Trenti and Timo Felser. Leto is a figure in Greek mythology, known as the Titaness and mother of the twin deities Apollo and Artemis. She is often associated with motherhood, protectiveness, and modesty. Leto faced challenges, including persecution by the jealous goddess Hera during her pregnancy. She found refuge on the island of Delos, where she gave birth to Apollo and Artemis. Leto is venerated as a protective and nurturing deity, symbolizing the strength of motherhood in Greek mythology. This thesis contained the implementation of 'convolutional' Tensor Networks layers.