Quantinuum enhances its quantum natural language processing toolkit lambeq

Quantinuum, an integrated quantum computing company, is debuting a major update to its open-source Python library and toolkit, lambeq (pronounced “lambek”). lambeq converts any natural language sentence into a quantum circuit, ready to be realized on a quantum computer.

The new release has been designed for a growing community of researchers, developers, and users versed in quantum natural language processing (QNLP) and natural language processing (NLP), according to the vendor.

The update will support the growth of QNLP and potential future applications such as automated dialogue, text mining, language translation, text-to-speech, language generation and bioinformatics.

“Since we launched lambeq, we have received valuable feedback from a rapidly growing community of users, and many of the new features available today reflect this. The new version of lambeq now comes, for example, with a native state-of-the-art parser that has been fully integrated with the toolkit. Additionally, the toolkit is now equipped with a training package that supports popular supervised learning libraries, such as PyTorch, to help users efficiently train NLP tasks using the quantum circuits and tensor networks that lambeq generates. This update is all about accessibility—and crucially, reducing the time it takes to achieve results,” said Quantinuum’s head of applied quantum NLP research, Dr. Dimitrios Kartsaklis.

Additionally, lambeq’s new neural-based CCG parser, Bobcat, is trained on a large human-annotated corpus of syntactic derivations.

It is fully integrated with the toolkit, simplifying the installation process, and presents improved state-of-the art parsing performance. The previous parser remains part of the toolkit, and for the benefit of the community, Bobcat will also be released as a separate stand-alone opensource tool in due course.

The new update is equipped with a command-line interface, making most of the toolkit's functionality available to users with no programming knowledge. It also contains a new supervised training module designed to simplify the process of training parameterised quantum circuits and tensor networks in a machine learning setup.

With this update, lambeq becomes more flexible in providing users with more options on the quantum circuits it can generate. It allows manipulation of syntax diagrams and makes it simpler to define the quantum circuits from the syntactic structure.

The visualisation of lambeq's output has also been improved, and documentation has been expanded with numerous examples to remove the barrier to entry for general users.

For more information about this news, visit www.quantinuum.com.