Quantum Algorithms for Compositional Natural Language Processing

William Zeng
(Rigetti Computing)
Bob Coecke
(Univesity of Oxford)

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.

In Dimitrios Kartsaklis, Martha Lewis and Laura Rimell: Proceedings of the 2016 Workshop on Semantic Spaces at the Intersection of NLP, Physics and Cognitive Science (SLPCS 2016), Glasgow, Scotland, 11th June 2016, Electronic Proceedings in Theoretical Computer Science 221, pp. 67–75.
Published: 2nd August 2016.

ArXived at: http://dx.doi.org/10.4204/EPTCS.221.8 bibtex PDF
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