Cambridge Quantum makes quantum natural language processing a reality


Cambridge Quantum (“CQ”) today announced a first in quantum computing. He released an open source toolkit and library for a quantum natural language processing (QNLP) toolkit called lambeq. The toolkit accelerates the development of practical and real-life QNLP applications, such as automated dialogue, text mining, language translation, text-to-speech, language generation, and bioinformatics.

Simply put, the Toolkit uses QNLP to turn sentences into quantum circuits to be implemented on quantum hardware. The strange name of the toolkit is not a quantum acronym or a derivation of a technical term. It is a tribute to Joachim Lambek, a late professor at McGill University who product innovative work in mathematics, logic, linguistics and theoretical computer science.

Earlier this year, Cambridge Quantum and Honeywell Quantum Solutions (HQS) announced their intention to merge. Cambridge Quantum develops quantum software for many fields, including quantum chemistry, quantum machine learning, and augmented quantum cybersecurity. It is present in the United States, Europe and Japan. Honeywell International, a $ 32 billion conglomerate, will separate from Honeywell Quantum Solutions as part of the planned merger. Honeywell’s quantum computing hardware uses trapped ion technology.

How QNLP works

Converting an ordinary sentence into quantum circuits is a complicated process. The first step turns the words in the sentence into something the computer can understand. Through this process, the sentence is converted into a logical format called a syntax tree. Then, the software analyzes the syntax tree, then organizes it into parts of the speech. Once the parts of speech have been identified, they are converted into a string diagram, much like the sentence diagrams you learned in grade school. After being encoded, tensor networks or quantum circuits implemented with TKET are ready to be optimized for machine learning tasks such as text classification. Lambeq’s modular design allows users to swap components in and out of the model and have flexibility in architecture design.

An important initiative

Recognizing the long-term commercial potential of NLP, in early 2021, Cambridge Quantum made a strategic decision to increase its QNLP expertise by appointing Professor Bob Coecke as Chief Scientist. Prior to this appointment, Coecke served as CQ’s Senior Scientific Advisor. He focuses on developing and building a leading quantum natural language processing capability for Cambridge Quantum.

During his 20-year tenure at Oxford University as a professor of quantum foundations, logic and structures, Coecke became a pioneer of QNLP and one of the foremost experts in technology. His research includes categorical quantum mechanics (now in the American Mathematical Society’s MSC2020 classification), ZX calculus, DisCoCat, the mathematical foundations of resource theories, and QNLP. He co-wrote Imagine quantum processes, a book containing 5,000 images offering an alternative to fully schematic quantum formalism. He is also the author or co-author of over 150 published research articles.

Conclude

Many significant NLP applications exceed the capabilities of traditional computers. As QNLP and quantum computers continue to improve, there will be many emerging commercial quantum applications. Thanks to Coecke’s expertise and experience, the merged entity of Honeywell and Cambridge Quantum will have a significant strategic advantage in the development of future QNLP applications.

Analyst’s Notes:

1. At present, QNLP research is still in the experimental stage. It will be several years before it is sufficiently advanced to be deployed in a large production environment. However, QNLP is largely unexplored, and lambeq opens the door for researchers to use and advance QNLP for unique applications.

2. Lambeq is available as a conventional Python repository on GitHub and is available here:

GitHubGitHub – CQCL / lambeq: A high-level Python library for quantum natural language processing

3. Lambeq-generated quantum circuits have been executed and implemented on superconducting quantum computers from IBM and H-series trapped ion devices from Honeywell Quantum Solutions.

4. The lambeq toolkit is introduced by a technical report downloaded from Arxiv available here:

arXiv.orglambeq: an efficient high-level Python library for quantum NLP

Note: The editors and editors of Moor Insights & Strategy may have contributed to this article.

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