Towards a Hybrid Quantum-Classical Computing Framework for Database Optimization Problems in Real Time Setup

TL;DR

This work tackles the difficulty of real-time database optimization with quantum assistance by proposing a real-time, quantum-augmented DB system that enables transparent control over the solving process. It introduces two scalable strategies: iterative correlation relaxation to simplify QUBOs while preserving variables, and a problem-aware decomposition–embedding–sampling–composition pipeline that preserves semantics and enables large-scale partitioning. A preliminary Iter-Q2O prototype integrates these ideas with PostgreSQL and demonstrates substantial improvements, achieving up to $14\times$ speedups over the classical optimizer and outperforming a black-box quantum solver on standard join-order benchmarks. The approach directly addresses challenges of solver opacity, QUBO overcomplexity, and QUBO oversize, and shows practical potential for real-time, scalable quantum-augmented data systems. Overall, the paper advances a cohesive framework and concrete techniques for integrating quantum sampling into database optimization workflows with real-time constraints.

Abstract

Quantum computing has shown promise for solving complex optimization problems in databases, such as join ordering and index selection. Prior work often submits formulated problems directly to black-box quantum or quantum-inspired solvers with the expectation of directly obtaining a good final solution. Due to the black-box nature of these solvers, users cannot perform fine-grained control over the solving procedure to balance the accuracy and efficiency, which in turn limits flexibility in real-time settings where most database problems arise. Moreover, it leads to limited potential for handling large-scale database optimization problems. In this paper, we propose a vision for the first real-time quantum-augmented database system, enabling transparent solutions for database optimization problems. We develop two complementary scalability strategies to address large-scale challenges, overcomplexity, and oversizing that exceed hardware limits. We integrate our approach with a database query optimizer as a preliminary prototype, evaluating on real-world workload, achieving up to 14x improvement over the classical query optimizer. We also achieve both better efficiency and solution quality than a black-box quantum solver.

Figures (6)

• Figure 1: Quantum Annealing Compare with Classical Approach.
• Figure 2: A Generic Workflow for Hybrid Quantum-Classical Optimization.
• Figure 3: Overview of a Quantum-Augmented Database System.
• Figure 4: Scalable Hybrid Quantum-Classical Methods.
• Figure 5: Framework of Iterative-based Quantum-augmented Query Optimizer.