The British company: Cambridge Quantum (“CQ”) today announced the release of a toolkit and library for Quantum Natural Language Processing (QNLP), believed to be the first. The toolkit is named lambeq, named after the late mathematician and linguist Joachim Lambek and is capable of converting sentences into a quantum circuit, with the hope that it will accelerate the development of practical, real-world QNLP applications, such as automated dialogue, text mining, language translation, text-to-speech, language generation and bioinformatics.
lambeq is according to CQ, the world’s first software toolkit for QNLP capable of converting sentences into a quantum circuit. It is designed to accelerate the development of practical, real-world QNLP applications, such as automated dialogue, text mining, language translation, text-to-speech, language generation and bioinformatics.
lambeq has been released on a fully open-sourced basis for the benefit of the world’s quantum computing community and the rapidly growing ecosystem of quantum computing researchers, developers and users like the company offering of TKET, a quantum programming language. lambeq as expected works seamlessly with CQ’s TKET, a fast-growing quantum software development platform that is also fully open-sourced (for an overview of languages please see our article on “Popularity of Quantum Programming Languages“).
The toolset, originated from CQ’s Oxford-based quantum computing research team led by Chief Scientist Bob Coecke, with senior scientist Dimitrios Kartsaklis, PhD, as the chief architect of the platform. lambeq, and QNLP more broadly, is the result of a research project stretching back over a decade.
lambeq – Quantum NLP
lambeq enables and automates the design and deployment of NLP experiments of the compositional-distributional (DisCo) type that CQ scientists have previously described. This means moving from syntax/grammar diagrams, which encode a text’s structure, to either (classical) tensor networks or quantum circuits implemented with TKET, ready to be optimised for machine learning tasks such as text classification. lambeq has a modular design so that users can swap components in and out of the model and have flexibility in architecture design.
lambeq removes the barriers to entry for practitioners and researchers who are focused on AI and human-machine interactions, potentially one of the most significant applications of quantum technologies. TKET has gained a worldwide user base now measured in the hundreds of thousands. lambeq has the potential to become the most important toolkit for the quantum computing community seeking to engage with QNLP applications that are amongst the most important markets for AI. A key point that has become apparent recently is that QNLP will also be applicable to the analysis of symbol sequences that arise in genomics as well as in proteomics.
Applications for Drug Discovery and Pharmaceutical companies
Merck Group, a launch partner and early adopter of lambeq, recently published a research paper on QNLP as part of a project with the innovation programme Quantum Entrepreneurship Laboratory from the Technical University of Munich.
Thomas Ehmer from Merck’s IT Healthcare Innovation Incubator and co-founder of the Quantum Computing Interest Group, said, “Using the unique features of quantum computing for fundamental breakthroughs is an important part of our research at Merck. Our recently disclosed project in QNLP with researchers from TU Munich has proven that binary classification tasks for sentences using QNLP techniques can achieve results comparable even at this stage to existing classical methods. Clearly, the infrastructure around quantum computing will need to advance before these techniques can be employed commercially. Critically, we can see how the approach employed in QNLP opens the route towards explainable AI, and thus to more accurate intelligence that is also accountable – which is critical in medicine.”
“There is a lot of interesting theoretical work on QNLP, but theory usually stands at some distance from practice,” said Kartsaklis. “With lambeq, we give researchers the opportunity to gain hands-on experience on experimental aspects of QNLP, which is currently completely unexplored ground. This is a crucial step towards reaching the point where practical, real-world NLP applications on quantum hardware become a reality.
lambeq has been released as a conventional Python repository on GitHub and is available at the CQ’s github https://github.com/CQCL/lambeq. The quantum circuits generated by lambeq have thus far been implemented on IBM quantum computers and Honeywell Quantum Solutions’ H series devices.