This article presents a review of quantum computing research works for Natural Language Processing (NLP). Their goal is to improve the performance of current models, and to provide a better representation of several linguistic phenomena, such as ambiguity and long range dependencies. Several families of approaches are presented, including symbolic diagrammatic approaches, and hybrid neural networks. These works show that experimental studies are already feasible, and open research perspectives on the conception of new models and their evaluation.
@article{arxiv.2406.15370,
title = {Traitement quantique des langues : {\'e}tat de l'art},
author = {Sabrina Campano and Tahar Nabil and Meryl Bothua},
journal= {arXiv preprint arXiv:2406.15370},
year = {2024}
}