AI 中文总结
本研究针对门控量子计算机的连接顺序优化问题，采用SPIQ初始化的QAOA算法，提升了小规模连接问题的优化稳定性与最优连接顺序采样频率，为数据库查询优化提供了量子优化的概念验证。
AI 中文摘要
连接顺序优化（JOO）是关系型查询优化中计算量最大的任务之一，原因是随着查询规模增大，可能的连接计划数量呈指数级增长。近期研究探索了量子及类量子方法解决JOO，将问题重构为适合量子硬件优化的二次无约束二进制优化（QUBO）问题。然而，现有许多方法在当前门控量子设备上的可扩展性有限，且很少有研究关注初始化策略对提升门控量子数据库工作负载优化性能的作用。本研究使用针对量子近似优化算法（QAOA）的可扩展参数初始化方法（SPIQ），探究门控量子连接顺序优化问题；SPIQ用于在门控量子计算机上执行的QAOA的量子解空间中高效识别高质量初始点。我们在包含3个和4个关系的小规模连接顺序问题上，评估QUBO编码、SPIQ初始化与门控优化之间的相互作用。结果显示，与无信息初始化方法相比，结构化初始化提升了优化稳定性，并增加了向高质量连接计划的收敛性；在这些基于模拟的小规模实例中，SPIQ使最优连接顺序的采样频率提升了约5倍，且最终态能量显著低于随机初始化的QAOA。总体而言，本研究改进了现有的门控量子优化，同时为将SPIQ初始化应用于数据库查询优化工作负载提供了初步的概念验证。
英文摘要
Join Order Optimization (JOO) is one of the most computationally expensive tasks in relational query optimization due to the exponential growth of possible join plans with increasing query size. Recent work has explored quantum and quantum-inspired approaches for solving JOO by reformulating the problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem suitable for optimization using quantum hardware. However, many existing approaches have limited scalability on current gate-based quantum devices. In addition, little work has investigated the role of initialization strategies in improving the performance of gate-based quantum optimization for database workloads. In this work, we investigate gate-based quantum join order optimization using the Quantum Approximate Optimization Algorithm (QAOA) initialized with Scalable Parameter Initialization for QAOA (SPIQ). SPIQ is used to efficiently identify high-quality initial points in the quantum solution landscape for QAOA executed on a gate-based quantum computer. We evaluate the interaction between QUBO encoding, SPIQ initialization, and gate-based optimization on small-scale join ordering problems involving 3 and 4 relations. Our results show that structured initialization improves optimization stability and increases convergence toward high-quality join plans compared to uninformed initialization approaches. Across these small-scale, simulation-based instances, SPIQ increases the sampling frequency of the optimal join order by up to approximately 5$\times$ and yields final-state energies significantly lower than a randomly initialized QAOA. Overall, this work enhances existing gate-based quantum optimization while providing an initial proof of concept for applying SPIQ initialization to database query optimization workloads.
CommentsVLDB 2026 Workshop:QC&DKM 2026 - 2nd Workshop on Quantum Computing and Data/Knowledge Management
