  We propose a new application of quantum computing to the field of natural
language processing. Ongoing work in this field attempts to incorporate
grammatical structure into algorithms that compute meaning. In (Coecke,
Sadrzadeh and Clark, 2010), the authors introduce such a model (the CSC model)
based on tensor product composition. While this algorithm has many advantages,
its implementation is hampered by the large classical computational resources
that it requires. In this work we show how computational shortcomings of the
CSC approach could be resolved using quantum computation (possibly in addition
to existing techniques for dimension reduction). We address the value of
quantum RAM (Giovannetti,2008) for this model and extend an algorithm from
Wiebe, Braun and Lloyd (2012) into a quantum algorithm to categorize sentences
in CSC. Our new algorithm demonstrates a quadratic speedup over classical
methods under certain conditions.
