Question Answering (QA) has proved to be an arduous challenge in the area of natural language processing (NLP) and artificial intelligence (AI). Many attempts have been made to develop complete solutions for QA as well as improving significant sub-modules of the QA systems to improve the overall performance through the course of time. Questions are the most important piece of QA, because knowing the question is equivalent to knowing what counts as an answer (Harrah in Philos Sci, 1961 [1]). In this work, we have attempted to understand questions in a better way by using Quantum Machine Learning (QML). The properties of Quantum Computing (QC) have enabled classically intractable data processing. So, in this paper, we have performed question classification on questions from two classes of SelQA (Selection-based Question Answering) dataset using quantum-based classifier algorithms-quantum support vector machine (QSVM) and variational quantum classifier (VQC) from Qiskit (Quantum Information Science toolKIT) for Python. We perform classification with both classifiers in almost similar environments and study the effects of circuit depths while comparing the results of both classifiers. We also use these classification results with our own rule-based QA system and observe significant performance improvement. Hence, this experiment has helped in improving the quality of QA in general.

翻译：问答（QA）已被证明是自然语言处理（NLP）和人工智能（AI）领域的一项艰巨挑战。为开发完整的QA解决方案以及改进QA系统的关键子模块以整体提升性能，人们已进行了诸多尝试。问题是QA中最关键的要素，因为知晓问题即等同于知晓何为答案（Harrah,《哲学科学》, 1961[1]）。本研究通过量子机器学习（QML）尝试更深入地理解问题。量子计算（QC）的特性已实现传统计算难以处理的数据分析。因此，本文基于SelQA（基于选择的问答）数据集中两类问题，采用量子分类算法——量子支持向量机（QSVM）和基于Qiskit（量子信息科学工具包）Python的变分量子分类器（VQC）进行问题分类。我们在近乎相同的环境下执行两种分类器分类，并研究电路深度的影响，同时比较两种分类器的结果。此外，我们将分类结果应用于自研规则型QA系统，观察到显著的性能提升。因此，本实验普遍有助于提升问答质量。