Internal:Quantum Natural Language Processing

Background

Much of modern NLP seems to stem from the parsing then subsequent vectorization of natural language inputs. As is the common case with quantum computing, if we can frame our input data as a vector then we can process that data using unitary operations. With vectorized data, linear operations can be performed on that data. This leaves an obvious opening for the application of QML techniques.

This semester's project has the following goals:

• Apply a QNLP framework to the DNISQ model. This first step should not be too rigorous as the distribution of decomposed domains is something with which we already have experience.
• Find a way to contribute to a model on the quantum side of a framework (an optimized ansatz, encoding methodology, application to noisy circuits, etc.)

Apart from the distribution, I will more or less shy away from any contribution on the classical side of the framework (such as tokenization or parsing).

Literature

[1] lambeq: An Efficient High-Level Python Library for Quantum NLP

Dimitri Kartsaklis, Ian Fan, Richie Yeung, Anna Pearson, Robin Lorenz, Alexis Toumi, Giovanni de Felice, Konstantinos Meichanetzidis, Stephen Clark, Bob Coecke https://arxiv.org/pdf/2110.04236.pdf

lambeq is a high-level, open source python library used for experimental QNLP which can be found here[1].