Named after the late mathematician and linguist Joachim Lambek , the toolkit - called lambeq - is 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. lambeq works seamlessly with the company's TKET, a leading quantum software development platform that is also fully open-sourced. This provides QNLP developers with access to the broadest possible range of quantum computers, says the company.
"Our team has been involved in foundational work that explores how quantum computers can be used to solve some of the most intractable problems in artificial intelligence," says CQ's Chief Scientist Bob Coecke. "This work was based on advances originally pioneered by me, Steve Clark, now CQ's Head of AI, and others. NLP sits at the heart of these investigations. The release of lambeq is the natural next step after the publication a few months ago that provided details of the world's first QNLP implementation by CQ on actual quantum computers, and our initial disclosure of the foundational principles in December 2019."
"In various papers published over the course of the past year," says Coecke, "we have not only provided details on how quantum computers can enhance NLP but also demonstrated that QNLP is 'quantum native,' meaning the compositional structure governing language is mathematically the same as that governing quantum systems. This will ultimately move the world away from the current paradigm of AI that relies on brute force techniques that are opaque and approximate."
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