SQL2Circuits: Estimating Metrics for SQL Queries with A Quantum Natural
  Language Processing Method
Valter Uotila
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.