Quantum Computing for Query Containment of Conjunctive Queries

2026-02-25

AI Summary

This work addresses the challenge of efficiently determining conjunctive query containment under set semantics—a problem known for its high computational complexity and, in some cases, undecidability within classical database systems. For the first time, we introduce quantum computing to this fundamental problem by proposing a provably correct optimization-based encoding that transforms query containment into an optimization task solvable on gate-based quantum circuits or quantum annealing hardware. Through rigorous formal modeling and co-design across algorithmic and hardware layers, we validate the correctness and near-term scalability of our approach on both quantum simulators and real quantum devices. Our results establish a novel quantum paradigm for tackling long-standing intractable problems in classical database theory.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Satisfiability

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• query containment
• conjunctive queries
• quantum computing
• database optimization
• computational complexity

Innovation

• quantum computing
• query containment
• conjunctive queries
• quantum optimization
• quantum annealing