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

2026-02-15

AI Summary

This work proposes the first quantum-enhanced real-time database system to address the limitations of black-box quantum solvers in database optimization, which typically lack fine-grained control and struggle to balance efficiency with solution accuracy. Built upon a hybrid quantum-classical computing framework, the system deeply integrates quantum-inspired heuristics with query optimization techniques, enabling precise control and dynamic trade-offs during the solving process. To handle extremely large-scale problems, two complementary scalability strategies are introduced to overcome current hardware constraints. Experimental evaluation on real-world workloads demonstrates that the system achieves up to a 14× speedup over classical optimizers while significantly outperforming existing black-box quantum solvers in both solution quality and computational efficiency.

Technology Category

• Search and Optimization: Heuristic Search
• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Solvers and Tools

Application Category

• 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
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• quantum computing
• database optimization
• real-time systems
• black-box solvers
• scalability

Innovation

• hybrid quantum-classical computing
• database optimization
• real-time query optimization
• scalability strategies
• transparent quantum solver