Natural language processing (NLP) problems are ubiquitous in classical computing, where they often require significant computational resources to infer sentence meanings. With the appearance of quantum computing hardware and simulators, it is worth developing methods to examine such problems on these platforms. In this manuscript we demonstrate the use of quantum computing models to perform NLP tasks, where we represent corpus meanings, and perform comparisons between sentences of a given structure. We develop a hybrid workflow for representing small and large scale corpus data sets to be encoded, processed, and decoded using a quantum circuit model. In addition, we provide our results showing the efficacy of the method, and release our developed toolkit as an open software suite.

自然语言处理（NLP）问题在古典计算中无处不在，在这些情况下，它们通常需要大量的计算资源来推断句子的含义。随着量子计算硬件和模拟器的出现，值得开发在这些平台上研究此类问题的方法。在此手稿中，我们演示了如何使用量子计算模型来执行NLP任务，其中我们代表了语料库的含义，并在给定结构的句子之间进行了比较。我们开发了一种混合工作流，用于表示要使用量子电路模型进行编码，处理和解码的小型和大型语料库数据集。此外，我们提供的结果显示了该方法的有效性，并以开放软件套件的形式发布了我们开发的工具包。