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

TL;DR

This paper proposes a hybrid quantum-classical framework for real-time database optimization, enabling transparent control and scalability, demonstrated with a prototype that outperforms classical and black-box quantum methods.

Contribution

It introduces a novel hybrid quantum-classical system for database optimization, addressing scalability and transparency issues in real-time settings.

Findings

Up to 14x improvement over classical query optimizer

Better efficiency and solution quality than black-box quantum solvers

Successful prototype integration with real-world workload

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.