Quantum Computing for Query Containment of Conjunctive Queries

TL;DR

This paper introduces a novel quantum computing approach to solve the complex query containment problem in databases, demonstrating its potential to improve scalability and efficiency using current quantum hardware limitations.

Contribution

It presents the first quantum computing formulation for query containment in conjunctive queries, with a prototype implementation and experimental validation.

Findings

Quantum optimization effectively addresses query containment.

The approach scales within current quantum hardware limitations.

Prototype demonstrates soundness and practical feasibility.

Abstract

We address the problem of checking query containment, a foundational problem in database research. Although extensively studied in theory research, optimization opportunities arising from query containment are not fully leveraged in commercial database systems, due to the high computational complexity and sometimes even undecidability of the underlying decision problem. In this article, we present the first approach to applying quantum computing to the query containment problem for conjunctive queries under set semantics. We propose a novel formulation as an optimization problem that can be solved on gate-based quantum hardware, and in some cases directly maps to quantum annealers. We formally prove this formulation to be correct and present a prototype implementation which we evaluate using simulator software as well as quantum devices. Our experiments successfully demonstrate that our approach is sound and scales within the current limitations of quantum hardware. In doing so, we show that quantum optimization can effectively address this problem. Thereby, we contribute a new computational perspective on the query containment problem.