A Demonstration of Q^2O: Quantum-augmented Query Optimizer
Summary: Demonstrates Q^2O, the first hybrid quantum-classical query optimizer deployed in a real database for join-order optimization. Interactive scenarios compare Q^2O with PostgreSQL and expose quantum-parameter effects, yielding up to 13× faster execution. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Hanwen Liu (University of Southern California)
- 2. Federico Spedalieri (University of Southern California)
- 3. Ibrahim Sabek (University of Southern California)
BibTeX Citation
@article{liu_vldb25,
title = {{A Demonstration of Q\^{}2O: Quantum-augmented Query Optimizer}},
author = {Liu, Hanwen and Spedalieri, Federico and Sabek, Ibrahim},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5439--5443},
doi = {10.14778/3750601.3750691},
url = {https://doi.org/10.14778/3750601.3750691},
year = {2025}
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Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1 | Access Path Selection in a Relational Database Management System | 1979 | SIGMOD | 0.0024089429 |
| 18 | How Good Are Query Optimizers, Really? | 2016 | VLDB | 0.00059284255 |
| 5,626 | Ready to Leap (by Co-Design)? Join Order Optimisation on Quantum Hardware | 2023 | SIGMOD | 6.1440728e-05 |
| 7,910 | Quantum-Inspired Digital Annealing for Join Ordering | 2024 | VLDB | 5.5181056e-05 |
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