Quantum Natural Language Processing on Near-Term Quantum Computers

Konstantinos Meichanetzidis
(University of Oxford and Cambridge Quantum Computing Ltd.)
Stefano Gogioso
(Hashberg)
Giovanni de Felice
(University of Oxford and Cambridge Quantum Computing Ltd.)
Nicolò Chiappori
(Hashberg)
Alexis Toumi
(University of Oxford and Cambridge Quantum Computing Ltd.)
Bob Coecke
(University of Oxford and Cambridge Quantum Computing Ltd.)

In this work, we describe a full-stack pipeline for natural language processing on near-term quantum computers, aka QNLP. The language-modelling framework we employ is that of compositional distributional semantics (DisCoCat), which extends and complements the compositional structure of pregroup grammars. Within this model, the grammatical reduction of a sentence is interpreted as a diagram, encoding a specific interaction of words according to the grammar. It is this interaction which, together with a specific choice of word embedding, realises the meaning (or "semantics") of a sentence. Building on the formal quantum-like nature of such interactions, we present a method for mapping DisCoCat diagrams to quantum circuits. Our methodology is compatible both with NISQ devices and with established Quantum Machine Learning techniques, paving the way to near-term applications of quantum technology to natural language processing.

In Benoît Valiron, Shane Mansfield, Pablo Arrighi and Prakash Panangaden: Proceedings 17th International Conference on Quantum Physics and Logic (QPL 2020), Paris, France, June 2 - 6, 2020, Electronic Proceedings in Theoretical Computer Science 340, pp. 213–229.
This work was originally commissioned by Cambridge Quantum Computing (CQC) and was carried out independently by the CQC team and the Hashberg team.
Published: 6th September 2021.

ArXived at: http://dx.doi.org/10.4204/EPTCS.340.11 bibtex PDF
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