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QNLP - Quantum Natural Language Processing

In this software suite we have developed a workflow to implement a hybridised classical-quantum model for representing language and representing sentence meanings in quantum states [1].

The model

This work is based upon and inspired by the DisCo (also known as DisCoCat) formalism of Coecke et al. [2,3,4], wherein both compositional and distributional sentence structure and information is used to represent meaning. Following the work on Zeng and Coecke [4], we aimed to implement a modified approach to implement this model for quantum processors. Further details will follow shortly in the upcoming paper.

Requirements

To build and run this software suite it is recommended to have the Intel compilers and Intel MPI libraries. We make use of the Intel Quantum Simulator (Intel-QS, qHiPSTER) [5,6] as the underlying simulator for our methods. If not available, the build can use GCC/Clang, provided the toolchain supports C++14 at a minimum (C++17 prefered). In addition, an MPI library is required. We have successfully used both MPICH and OpenMPI with non-Intel compiler builds.

This suite is primarily developed for Linux HPC systems, though it may also run on laptops/desktops. MacOS can be used, but some additional work is necessary.

Documentation and examples

All documentation for this project is available at https://ichec.github.io/QNLP

Example runnable scripts are available in the modules/py/scripts directory. C++ demos and tests are available under demos and modules/tests respectively. Jupyter notebooks are available at modules/py/nb.

Referencing

If using this work in any way as part of your research, please feel free to cite us as: "A hybrid classical-quantum workflow for natural language processing" by O'Riordan et al, Mach. Learn.: Sci. Technol. (2020). DOI: https://doi.org/10.1088/2632-2153/abbd2e


  • [1]: Lee J. O'Riordan, Myles Doyle, Fabio Baruffa, Venkatesh Kannan, A hybrid classical-quantum workflow for natural language processing, IOP Machine Learning: Science and Technology (2020). DOI: https://doi.org/10.1088/2632-2153/abbd2e
  • [2]: Stephen Clark, Bob Coecke and Mehrnoosh Sadrzadeh, A Compositional Distributional Model of Meaning, Proceedings of the Second Quantum Interaction Symposium, 2008.
  • [3]: Bob Coecke, Mehrnoosh Sadrzadeh and Stephen Clark, Mathematical Foundations of a Compositional Distributional Model of Meaning, Special issue of Linguistic Analysis, 2010. arXiv:1003.4394
  • [4]: William Zeng and Bob Coecke, Quantum Algorithms for Compositional Natural Language Processing, Proceedings of SLPCS, 2016. arXiv:1608.01406
  • [5]: Mikhail Smelyanskiy, Nicolas P. D. Sawaya, Alán Aspuru-Guzik. qHiPSTER: The Quantum High Performance Software Testing Environment, arXiv:1601.07195
  • [6]: Gian Giacomo Guerreschi, Justin Hogaboam, Fabio Baruffa, Nicolas P. D. Sawaya, Intel Quantum Simulator: A cloud-ready high-performance simulator of quantum circuits. arXiv:2001.10554