lambeq

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lambeq is an open-source, modular, extensible high-level Python library for experimental Quantum Natural Language Processing (QNLP), created by Quantinuum’s QNLP team. At a high level, the library allows the conversion of any sentence to a quantum circuit, based on a given compositional model and certain parameterisation and choices of ansätze, and facilitates training for both quantum and classical NLP experiments. The notes for the latest release can be found here.

lambeq is available for Python 3.9 and higher, on Linux, macOS and Windows. To install, type:

pip install lambeq

or refer to Installation for more information. To start the tutorial, go to Step 1: Sentence Input. To see the example notebooks, go to Examples. To use the command-line interface, read Command-line interface. To make your own contributions to lambeq, see Contributing to lambeq.

Note

Please do not try to read this documentation directly from the preview provided in the GitHub repository, since some of the pages will not be rendered properly.

User support

If you need help with lambeq or you think you have found a bug, please send an email to lambeq-support@cambridgequantum.com. You can also open an issue at lambeq’s GitHub repository. Someone from the development team will respond to you as soon as possible. Furthermore, if you want to subscribe to lambeq’s mailing list (lambeq-users@cambridgequantum.com), send an email to lambeq-support@cambridgequantum.com to let us know.

Note that the best way to get in touch with the QNLP community and learn about lambeq is to join our QNLP discord server, where you can ask questions, get notified about important announcements and news, and chat with other QNLP researchers.

Licence

Licensed under the Apache 2.0 License.

How to cite

If you use lambeq for your research, please cite the accompanying paper [Kea2021]:

@article{kartsaklis2021lambeq,
   title={lambeq: {A}n {E}fficient {H}igh-{L}evel {P}ython {L}ibrary for {Q}uantum {NLP}},
   author={Dimitri Kartsaklis and Ian Fan and Richie Yeung and Anna Pearson and Robin Lorenz and Alexis Toumi and Giovanni de Felice and Konstantinos Meichanetzidis and Stephen Clark and Bob Coecke},
   year={2021},
   journal={arXiv preprint arXiv:2110.04236},
}

Toolkit

External links