Quantum Natural Language Processing on Near-Term Quantum Computers
Abstract
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.
Cite
@article{arxiv.2005.04147,
title = {Quantum Natural Language Processing on Near-Term Quantum Computers},
author = {Konstantinos Meichanetzidis and Stefano Gogioso and Giovanni de Felice and Nicolò Chiappori and Alexis Toumi and Bob Coecke},
journal= {arXiv preprint arXiv:2005.04147},
year = {2021}
}
Comments
In Proceedings QPL 2020, arXiv:2109.01534. This work was originally commissioned by Cambridge Quantum Computing (CQC) and was carried out independently by the CQC team and the Hashberg team