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Towards quantum algorithms for natural language processing

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If you have a question about this talk, please contact Tamara Polajnar.

Recent work has begun to demonstrate the applicability of quantum computing techniques to problems in machine learning. These include algorithms for unsupervised clustering, restricted Boltzmann machines, and perceptrons as well as optimization algorithms that can be run on adiabatic quantum computers. This talk will introduce these recent advances as well as describe how these algorithms could be especially relevant to compositional models of natural language semantics, where there is common mathematical structure. At a basic level, quantum computers are naturally suited to manipulate vectors in high dimensional tensor product spaces, so it is hoped that the introduction of such devices will provide new avenues for natural language processing.

No working knowledge of quantum mechanics will be assumed.

This talk is part of the NLIP Seminar Series series.

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