Quantum computing has developed significantly in recent years. Developing algorithms to estimate various metrics for SQL queries has been an important research question in database research since the estimations affect query optimization and database performance. This work represents a quantum natural language processing (QNLP) -inspired approach for constructing a quantum machine learning model which can classify SQL queries with respect to their execution times and cardinalities. From the quantum machine learning perspective, we compare our model and results to the previous research in QNLP and conclude that our model reaches similar accuracy as the QNLP model in the classification tasks. This indicates that the QNLP model is a promising method even when applied to problems that are not in QNLP. We study the developed quantum machine learning model by calculating its expressibility and entangling capability histograms. The results show that the model has favorable properties to be expressible but also not too complex to be executed on quantum hardware.

翻译：量子计算近年来取得了显著发展。在数据库研究中，开发用于估计SQL查询各类度量的算法一直是一个重要研究课题，因为这些估计直接影响查询优化和数据库性能。本文提出一种受量子自然语言处理（QNLP）启发的量子机器学习模型构建方法，该模型能够根据执行时间和基数对SQL查询进行分类。从量子机器学习的角度出发，我们将所构建的模型与结果同先前QNLP研究进行对比，结果表明我们的模型在分类任务中达到了与QNLP模型相似的准确率。这说明即使应用于非QNLP领域的问题，QNLP模型仍是一种颇具前景的方法。我们通过计算所开发量子机器学习模型的可表达性和纠缠能力直方图对其进行了研究。结果表明该模型具有适宜的可表达性，同时复杂度适中，可在量子硬件上执行。