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.

翻译：量子计算在解决数据库中的复杂优化问题（如连接顺序与索引选择）方面已展现出潜力。先前的研究通常将形式化的问题直接提交至黑盒量子或量子启发求解器，期望直接获得良好的最终解。由于此类求解器的黑盒特性，用户无法对求解过程进行细粒度控制以平衡精度与效率，这进而限制了在大多数数据库问题出现的实时场景中的灵活性。此外，这也导致处理大规模数据库优化问题的潜力受限。本文提出了首个实时量子增强数据库系统的愿景，旨在为数据库优化问题提供透明化的解决方案。我们开发了两种互补的可扩展性策略，以应对超出硬件限制的大规模挑战、过度复杂性与规模过大的问题。我们将该方法与数据库查询优化器集成为初步原型，并在真实工作负载上进行评估，相比经典查询优化器实现了高达14倍的性能提升。同时，我们的方法在黑盒量子求解器的基础上，实现了更高的效率与更优的解质量。