QDBO: A Real-time Quantum-augmented Database System Optimizer

Quantum annealers offer a promising sampling-based computing paradigm for large-scale combinatorial optimization. However, directly using them to solve database optimization problems in real-time scenarios remains challenging due to strict hardware constraints and the opacity of commercial hybrid solvers. To address these challenges, we present QDBO , a white-box quantum-augmented database optimizer that directly leverages low-level quantum sampling on a quantum annealer. It first performs an initial reduction to produce embeddable QUBO instances via multiple mapping strategies that trade off efficiency and quality. It then applies an online iterative relaxation correction procedure that uses sampling feedback to correct the reduced QUBO across iterations, enabling controllable quality improvements under varying time budgets. We integrate QDBO with the PostgreSQL query optimizer to construct

Q 2 O-QDBO

for join order optimization. Evaluations on the Join Order Benchmark (JOB) and the Cardinality Estimation Benchmark (CEB) show that

Q 2 O-QDBO

improves 62 out of 113 JOB queries, and achieves up to 49.36× execution-time speedup and 36.88× end-to-end speedup on the CEB workload. Evaluations on synthetic queries with up to 50 relations show that

Q 2 O-QDBO

achieves significantly less end-to-end latency while maintaining similar solution quality compared with black-box quantum solvers. To demonstrate generality, we further integrate QDBO with PostgreSQL for index selection on TPC-H, where QDBO achieves up to 2.64× workload speedup over the empty-index baseline and outperforms classical and black-box solver baselines. These results demonstrate that using QDBO to solve large-scale database optimization problems in real-time settings is promising.