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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).
