Quantum Natural Language Processing

This repository contains information about NLP course, in particular, I share QNLP material, I hope you find useful for your careeers.

by: Javier Orduz

Contents

1. Instructions
2. Introduction
3. Implementation
4. References

Instructions

1. Read this readme.md
2. Check a notebook from notebook folder where you find
   • Classical NLP: from example_1 to example_4
   • Quantum NLP: from example_5 to example_7
3. Select only one notebook.
4. Create another version where you modify the parameters.
5. Modify any parameters in the selected NB, at least, three.
6. Obtain different quantum circuits.
7. Discuss your results after changing those parameters.
8. Describe the elements in the circuits. E.g. number of qbits, gates and name of the gates.
9. Submit your final report about your analysis in the previous step in Canvas.
10. 3 Extra points choose an article from any online news paper, and use a qiskit (tool from IBM) function to obtain the frequency words.

Purpose

The main goal in these sessions is to create a QNLP collaborative repository, therefore you should clone and work with Pull Request.

Introduction

Quantum computing is an interesting field to work. It is an area with some applications and it can be powerful if it is combined with Machine Learning area. Recently, we can explore applications on several topics such as computer vision, cybersecurity, economy, finance, science and others. In particular, this repository is to create an interesting and repository on QNLP to help your career on the next generation of scientists.

Consider this a recent topic and it very probably you have a lot questions without solve, but don't worry, it is part to be a scientist.

This material is created by different authors, therefore you will be sure about the source if you find any detail, please, let me know and I will fix it.

There are some concepts from quantum computing what you probably don't know, it is ok, I will share with you references, papers, and books to read, but consider that on NLP, quantum computing topics are out of the scope of the course.

To check quantum computing concepts, go to Nielsen and Chuang book [1]; to know how parameterized in quantum circuits, and Circuit Ansatz [2], some QNLP tools can be found in ref. [3] if you want to know more about text structure [4],

Material

I will be using material created by different authors, so here you find the links

• Gogioso, S. Doing QNLP
• Luongo, A. and Bellante, A. Quantum Algorithms for QNLP
• Meichanetzidis, K. QNLP implementation

Consider those links don't depend on me, those can be broken but I can not do anything.

Implementation: assignment

1. Create your Notebook into the PutYourWorkHere folder, and try to explain every step (as a tutorial).
   • You can use one notebook from NB folder Don't forget to write your name and your contributor names. You can convert your NB to HTML or anyother format (pandoc). I share some websites with information (some references [12, 13]), you can use but be creative.

• Use readme.md into the examples folder to provide a summary.
• Use Pull Request to maintain this repository with the last version, when you send the PR, put my user name (jaorduz) to approve the work. This is a link to know how to work with PR Pull Request's (A), Pull Request's (B), and ISSUES.

References

[1] Nielsen and Chuang Book

[2] ArXiv 1906.07682, Circuit Ansatz

[3] Lambeq, check the documentation, and DisCopy. More repositories in the next section.

[4] https://arxiv.org/pdf/1904.03478.pdf

Other materials

[1] Cambridge Quantum

[2] How does lambeq work?. Revise the repository

[3] QNLP post, one application repository

[4] Workshop

[5] A Quantum Natural Language Processing Approach to Musical Intelligencehttps://arxiv.org/abs/2111.06741

[6] lambeq: An Efficient High-Level Python Library for Quantum NLP https://arxiv.org/abs/2110.04236

[7] QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer https://arxiv.org/abs/2102.12846

[8] Grammar-Aware Question-Answering on Quantum Computers https://arxiv.org/abs/2012.03756

[9] A hybrid classical-quantum workflow for natural language processing https://arxiv.org/abs/2004.06800

[10] Developing Quantum Annealer Driven Data Discovery https://arxiv.org/abs/1603.07980

[11] A gentle introduction to Quantum Natural Language Processing https://arxiv.org/abs/2202.11766

[12] Quantum natural language processing https://discopy.readthedocs.io/en/main/notebooks.qnlp.html

[13] qnlp-experiment https://discopy.readthedocs.io/en/main/notebooks/qnlp-experiment.html