Quantum natural language processing (QNLP)

Natural Language Processing (NLP) is often used to perform tasks such as machine translation, sentiment analysis, relationship extraction, word sense disambiguation and automatic summary generation.

Partners

However standard NLP methods scale poorly with performance and accuracy with increased problem complexity. Methods based on Distributional Compositional techniques offer potential for better accuracy with increased complexity. They are classically resource intensive, but are compatible and better scalable on quantum computers.

This use-case will use a hybrid classical-quantum workflow to implement and demonstrate sentence similarity algorithms applied to parallel data extraction applications (such as machine translation) and intent detection applications (such as chat bots).

Our deliverables so far

Our webinars so far

08/02/2022 QNLP

Our website uses cookies to give you the most optimal experience online by: measuring our audience, understanding how our webpages are viewed and improving consequently the way our website works, providing you with relevant and personalized marketing content. You have full control over what you want to activate. You can accept the cookies by clicking on the “Accept all cookies” button or customize your choices by selecting the cookies you want to activate. You can also decline all cookies by clicking on the “Decline all cookies” button. Please find more information on our use of cookies and how to withdraw at any time your consent on our privacy policy.
Accept all cookies
Decline all cookies
Privacy Policy