lambeq
lambeq is an open-source, modular, extensible high-level Python library for experimental Quantum Natural Language Processing (QNLP), created by Cambridge Quantum’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 3.8 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.
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},
}
Getting started
Tutorials
Reference