Cambridge Quantum Computing (CQC) announced that it has built on earlier advances in “meaning- aware” Quantum Natural Language Processing (QNLP), establishing that QNLP is quantum-native with expected near-term advantages over classical computers. Natural language processing (NLP) is at the forefront of advances in contemporary artificial intelligence, and it is arguably one of the most challenging areas of the field. “Meaning-aware” NLP remains a distant aspiration using classical computers. The steady growth of quantum hardware and notable improvements in the implementation of quantum algorithms mean that we are approaching an era when quantum computers might perform tasks that cannot be done on classical computers with a reasonable amount of resources in a repeatable manner, and which are important and suitable for everyday use. In papers posted on arXiv – the scientific e-print repository, CQC’s scientists provide conceptual and mathematical foundations for near-term QNLP in quantum computer scientist-friendly terms. The paper is written in an expository style with tools that provide mathematical generality.
Aiming to canonically combine linguistic meanings with rich linguistic structure, most notably grammar, Professor Bob Coecke (Oxford University) and his team have proven that a quantum computer can achieve “meaning aware” NLP, thus establishing QNLP as quantum-native, on par with the simulation of quantum systems. Moreover, the leading Noisy Intermediate-Scale Quantum (NISQ) paradigm for encoding classical data on quantum hardware – variational quantum circuits – makes NISQ exceptionally QNLP-friendly.