2020/05/31 by Konstantinos Meichanetzidis, Stefano Gogioso, Giovanni de Felice +3
Computer Science · Physics and Astronomy · #cs.CL #quant-ph
paper · pdf · doi:10.4204/eptcs.340.11
published as EPTCS 340, 2021, pp. 213-229 · 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
arxiv created 2021/09/06 · arxiv updated 2021/09/07
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.