Quantum Algorithms for Compositional Natural Language Processing

TL;DR

This paper introduces a quantum computing approach to natural language processing, specifically enhancing the tensor-based compositional model (CSC) by leveraging quantum algorithms for improved efficiency and sentence categorization.

Contribution

It extends a quantum algorithm for sentence categorization within the CSC model, achieving quadratic speedup over classical methods and addressing computational resource limitations.

Findings

Quantum algorithms can significantly speed up compositional NLP tasks.

The extended algorithm demonstrates quadratic speedup in sentence categorization.

Quantum RAM enhances the efficiency of tensor-based NLP models.

Abstract

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.