  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 greater than
100 sentences. Exploiting the formal similarity of the compositional model of
meaning by Coecke, Sadrzadeh and Clark (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 NLP models
that solve simple sentence classification tasks on quantum hardware. We conduct
quantum simulations that compare the syntax-sensitive model of Coecke et al.
with two baselines that use less or no syntax; specifically, we implement the
quantum analogues of a "bag-of-words" model, where syntax is not taken into
account at all, and of a word-sequence model, where only word order is
respected. We demonstrate that all models converge smoothly both in simulations
and when run on quantum hardware, and that the results are the expected ones
based on the nature of the tasks and the datasets used. Another important goal
of this paper is to describe in a way accessible to AI and NLP researchers the
main principles, process and challenges of experiments on quantum hardware. Our
aim in doing this is to take the first small steps in this unexplored research
territory and pave the way for practical Quantum Natural Language Processing.
