Quantum Natural Language Processing on Near-Term Quantum Computers


In this work, we describe a full-stack pipeline for natural language processing on near-term quan- tum 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.

K. Meichanetzidis, G. De Felice, A. Toumi and B. Coecke, Stefano Gogioso and Nicolo Chiappori

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