In recent developments, deep learning methodologies applied to Natural Language Processing (NLP) have revealed a paradox: They improve performance but demand considerable data and resources for their training. Alternatively, quantum computing exploits the principles of quantum mechanics to overcome the computational limitations of current methodologies, thereby establishing an emerging field known as quantum natural language processing (QNLP). This domain holds the potential to attain a quantum advantage in the processing of linguistic structures, surpassing classical models in both efficiency and accuracy. In this paper, it is proposed to categorise QNLP models based on quantum computing principles, architecture, and computational approaches. This paper attempts to provide a survey on how quantum meets language by mapping state-of-the-art in this area, embracing quantum encoding techniques for classical data, QNLP models for prevalent NLP tasks, and quantum optimisation techniques for hyper parameter tuning. The landscape of quantum computing approaches applied to various NLP tasks is summarised by showcasing the specific QNLP methods used, and the popularity of these methods is indicated by their count. From the findings, it is observed that QNLP approaches are still limited to small data sets, with only a few models explored extensively, and there is increasing interest in the application of quantum computing to natural language processing tasks.

翻译：近年来，应用于自然语言处理（NLP）的深度学习方法揭示了一个悖论：它们虽提升了性能，但训练过程需要大量的数据和计算资源。另一方面，量子计算利用量子力学原理来克服现有方法的计算局限，由此建立了一个新兴领域——量子自然语言处理（QNLP）。该领域有望在处理语言结构方面获得量子优势，在效率和准确性上超越经典模型。本文提出基于量子计算原理、体系结构和计算方法对QNLP模型进行分类。本文试图通过梳理该领域的最新进展，对量子与语言的结合进行综述，涵盖经典数据的量子编码技术、面向主流NLP任务的QNLP模型，以及用于超参数调优的量子优化技术。通过展示所使用的具体QNLP方法，总结了应用于各类NLP任务的量子计算方法概况，并通过方法数量反映了其流行程度。研究发现，QNLP方法目前仍局限于小型数据集，仅有少数模型得到深入探索，但将量子计算应用于自然语言处理任务的兴趣正日益增长。