Parts of Speech Tagging in NLP: Runtime Optimization with Quantum Formulation and ZX Calculus

TL;DR

This paper introduces a quantum computing approach to parts of speech tagging in NLP, achieving quadratic speedup over classical methods and demonstrating implementation using ZX calculus on NISQ systems.

Contribution

It presents a novel quantum formulation for POS tagging and demonstrates its optimization and implementation using ZX calculus on NISQ hardware.

Findings

Quadratic speedup over classical POS tagging methods

Quantum gate-level optimization demonstrated with ZX calculus

Implementation feasibility on NISQ systems

Abstract

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.