  Quantum Natural Language Processing (QNLP) deals with the design and
implementation of NLP models intended to be run on quantum hardware. In this
paper, we present results on the first NLP experiments conducted on Noisy
Intermediate-Scale Quantum (NISQ) computers for datasets of size >= 100
sentences. Exploiting the formal similarity of the compositional model of
meaning by Coecke et al. (2010) with quantum theory, we create representations
for sentences that have a natural mapping to quantum circuits. We use these
representations to implement and successfully train two NLP models that solve
simple sentence classification tasks on quantum hardware. We describe in detail
the main principles, the process and challenges of these experiments, in a way
accessible to NLP researchers, thus paving the way for practical Quantum
Natural Language Processing.
