Is Quantum Computing Ready for Real-Time Database Optimization?

2026-01-17

AI Summary

This work proposes Q2O, the first end-to-end real-time quantum-enhanced query optimizer, to address the exponential search space challenge in database query optimization—particularly for join order selection under high-complexity workloads. Q2O formulates database statistics as a nonlinear optimization problem and leverages D-Wave’s NL-Solver quantum annealing service for efficient solution. The system integrates seamlessly into the PostgreSQL optimizer via a plan-hinting mechanism, enabling practical deployment without disrupting existing query processing pipelines. Experimental results demonstrate that Q2O consistently produces high-quality execution plans while meeting stringent database latency requirements, significantly improving optimization efficiency for complex queries.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Database systems encompass several performance-critical optimization tasks, such as join ordering and index tuning. As data volumes grow and workloads become more complex, these problems have become exponentially harder to solve efficiently. Quantum computing, especially quantum annealing, is a promising paradigm that can efficiently explore very large search spaces through quantum tunneling. It can escape local optima by tunneling through energy barriers rather than climbing over them. Earlier works mainly focused on providing an abstract representation (e.g., Quadratic Unconstrained Binary Optimization (QUBO)) for the database optimization problems (e.g., join order) and overlooked the real integration within database systems due to the high overhead of quantum computing services (e.g., a minimum 5s runtime for D-Wave's CQM-Solver). Recently, quantum annealing providers have offered more low-latency solutions, e.g., NL-Solver, which paves the road to actually realizing quantum solutions within DBMSs. However, this raises new systems research challenges in balancing efficiency and solution quality. In this talk, we show that this balance is possible to achieve. As a proof of concept, we present Q2O, the first real Quantum-augmented Query Optimizer. We show the end-to-end workflow: we encode the join order problem as a nonlinear model, a format solvable by the NL-Solver, using actual database statistics; the solution is translated into a plan hint that guides PostgreSQL's optimizer to produce a complete plan. Q2O is capable of handling actual queries in real time.

Problem

• quantum computing
• database optimization
• join ordering
• real-time query optimization
• quantum annealing

Innovation

• Quantum Annealing
• Query Optimization
• Join Ordering
• NL-Solver
• Quantum-Augmented DBMS