Titled “QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer,” the paper presents the first “medium-scale” implementation of common NLP tasks. Completed on an IBM quantum computer, the experiment, which instantiated sentences as parameterised quantum circuits, embeds word meanings as quantum states which are “entangled” according to the grammatical structure of the sentence.
The paper builds on prior proof-of-concept work for the previous experiment and, significantly, achieves convergence for the far larger datasets that are employed here. One of the objectives of the Cambridge Quantum team is to describe Quantum Natural Language Processing (QNLP) and their results in a way that is accessible to NLP researchers and practitioners thus paving the way for the NLP community to engage with a quantum encoding of language processing.
Professor Bob Coecke, Cambridge Quantum’s Chief Scientist and also the Head of Cambridge Quantum ’s QNLP project, commented, “We are working on an immensely ambitious project at CQ that is aimed at utilising quantum computers, as they scale, to move beyond expensive black-box mechanisms for NLP to a paradigm where we become more effective, more accurate and more scalable in an area of computer science that epitomises artificial intelligence. Having made considerable progress already on our ‘quantum-native’ brand of compositional NLP, we are now moving beyond our initial research and working on applications that can be developed in synch with timelines provided by quantum computing hardware companies such as IBM, Honeywell, Google and others.”
He added, “Equally, at a time when quantum computing is becoming a topic of general interest it is imperative that those of us who are working within this sector provide results that are verifiable. Our record of publication at Cambridge Quantum strives at all times to meet these exacting standards – we are science led and enterprise driven.”