Quantum Natural Language Processing on Near-Term Quantum Computers
This work addresses the challenge of applying quantum computing to NLP for researchers and practitioners, though it appears incremental as it builds on existing quantum-like frameworks.
The authors tackled the problem of performing natural language processing on near-term quantum computers by mapping compositional distributional semantics diagrams to quantum circuits, enabling compatibility with NISQ devices and quantum machine learning techniques.
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.