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Cambridge Quantum Releases First Toolkit and Library for Quantum Language Processing

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CQ hopes open-source access to its quantum natural language processing toolkit — lambeq — will unleash the quantum community’s innovative power and prompt more research investigations into the technology.

Cambridge Quantum (CQ) today announced the release of the world’s first toolkit and library for Quantum Natural Language Processing (QNLP), which will be released on a fully open-sourced basis.

The toolkit, called lambeq — after the late mathematician and linguist Joachim Lambek, is the world’s first software toolkit for QNLP capable of converting sentences into quantum circuits. Lamneq is designed to accelerate the development of practical, real-world QNLP applications, including automated dialogue, text mining, language translation, text-to-speech, language generation and bioinformatics.

CQ believe that the QNLP advance is a pivotal moment in not just the field of QNLP, but also artificial intelligence (AI) and quantum computing, in general.

First, the technology will make AI more accountable, a leading criticism of today’s classical AI.

“One of the great advantages of quantum computing — one of its great promises — is that you can do things differently. With lambeq we have proven that AI need not be a black box: it can be accountable. It can be interpretable.”

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“One of the great advantages of quantum computing — one of its great promises — is that you can do things differently,” said Khan. “With lambeq we have proven that AI need not be a black box: it can be accountable. It can be interpretable.”

Khan added that the tool demonstrates that QNLP is quantum native, which means that compositional structure language and quantum systems are mathematically identical. This could open up entirely new paths to applications for quantum computers, if not new industries.

The company also reinforced its commitment to the deomcratisation of quantum computing by releasing lambeq on an open source basis. 

“Releasing the toolkit on an open-sourced basis will benefit the global quantum computing community and the rapidly growing ecosystem of quantum computing researchers, developers and users.”

“Releasing the toolkit on an open-sourced basis will benefit the global quantum computing community and the rapidly growing ecosystem of quantum computing researchers, developers and users,” said Bob Coecke, CQ’s chief scientist  “we want to democratize access to quantum computing.”

lambeq will work seamlessly with CQ’s TKET, the world’s leading and fastest-growing quantum software development platform that is also fully open-sourced and has gained a worldwide user base now measured in the hundreds of thousands. This provides QNLP developers with access to the broadest possible range of quantum computers.

lambeq was conceived, designed and engineered by CQ’s Oxford-based quantum computing research team led by Chief Scientist Bob Coecke, with senior scientist Dimitrios Kartsaklis, Ph.D., as chief architect of the platform. lambeq, and QNLP more broadly, is the result of a research project stretching back over a decade.

“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,” said Coecke in a company news release. “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.”

Coecke said that QNLP will help technology move away from brute force AI techniques.

“In various papers published over the course of the past year,” Coecke added, “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.”

According to the researchers, 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, which can 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. 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.

Genomics and Proteomics
QNLP may also be applicable to the analysis of symbol sequences that arise in genomics as well as in proteomics.

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.”

CQ’s Kartsaklis said that open-source lambeq will help researchers move QNLP from theory to practice and from the lab into the real-world.

“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 here: The quantum circuits generated by lambeq have thus far been executed and implemented on IBM quantum computers and Honeywell Quantum Solutions’ H series devices.

The toolkit is introduced by a technical report uploaded on arxiv available here: A more generally accessible blog post can be found here.

Technical enquiries can be directed to [email protected].

If you found this article to be informative, you can explore more current quantum news here, exclusives, interviews, and podcasts.

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With a several-decades long background in journalism and communications, Matt Swayne has worked as a science communicator for an R1 university for more than 12 years, specializing in translating high tech and deep tech for the general audience. He has served as a writer, editor and analyst at The Quantum Insider since its inception. In addition to his service as a science communicator, Matt also develops courses to improve the media and communications skills of scientists and has taught courses. [email protected]

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