Parts of Speech Tagging in NLP: Runtime Optimization with Quantum Formulation and ZX Calculus
This addresses runtime efficiency in NLP for researchers and practitioners, but it is incremental as it applies quantum methods to an existing NLP task.
The paper tackles parts of speech tagging in NLP by proposing a quantum computing approach with ZX calculus, achieving a quadratic speedup over classical methods and demonstrating implementable optimization for NISQ systems.
This paper proposes an optimized formulation of the parts of speech tagging in Natural Language Processing with a quantum computing approach and further demonstrates the quantum gate-level runnable optimization with ZX-calculus, keeping the implementation target in the context of Noisy Intermediate Scale Quantum Systems (NISQ). Our quantum formulation exhibits quadratic speed up over the classical counterpart and further demonstrates the implementable optimization with the help of ZX calculus postulates.