QDBO: A Real-time Quantum-augmented Database System Optimizer
Summary: QDBO is a white-box quantum-annealing optimizer that reduces database problems to embeddable QUBOs and iteratively corrects them using sampling feedback under time budgets. Integrated with PostgreSQL, it accelerates join ordering and index selection while matching black-box solver quality. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Hanwen Liu (University of Southern California)
- 2. Abhishek Kumar (University of Southern California)
- 3. Federico Spedalieri (Capital One)
- 4. Ibrahim Sabek (University of Southern California)
BibTeX Citation
@article{liu_vldb26,
title = {{QDBO: A Real-time Quantum-augmented Database System Optimizer}},
author = {Liu, Hanwen and Kumar, Abhishek and Spedalieri, Federico and Sabek, Ibrahim},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {11},
pages = {3606--3620},
doi = {10.14778/3836663.3836712},
url = {https://doi.org/10.14778/3836663.3836712},
year = {2026}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,017 | QFusion: A Demonstration of Boundary-Aware Fusion Planning and Execution for Large-Scale QUBO Optimization | 2026 | VLDB | 4.9793485e-05 |
| 11,030 | How Can Quantum Computing and Databases Meet "in Practice"? From Algorithms to Systems | 2026 | VLDB | 4.9793485e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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