论文标题
NLP中语音标记的一部分：使用量子配方和ZX演算的运行时优化
Parts of Speech Tagging in NLP: Runtime Optimization with Quantum Formulation and ZX Calculus
论文作者
论文摘要
本文提出了使用量子计算方法对自然语言处理中语音标记的部分进行优化的表述，并进一步证明了使用ZX-Calculus的量子门级可运行的优化，从而在嘈杂的中等中等规模量子系统（NISQ）的背景下保持实现目标。我们的量子配方在经典的对应物上表现出二次速度，并在ZX conculus假设的帮助下进一步证明了可实现的优化。
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.
