  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.
