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Large-Scale Multiple Query Optimisation with Incremental Quantum(-Inspired) Annealing

Summary: Incremental MQO framework that partitions large multiple-query optimization instances and reuses discarded partial solutions via dynamic search-steering, offloading subproblems to Fujitsu’s Digital Annealer (quantum-inspired annealing). Scales MQO to ~1,000 queries, substantially outperforms prior methods, and generalises to other DB workloads and future quantum accelerators. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID
7518
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,319 | 29.21%
DOI
10.1145/3749171

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BibTeX Citation

@inproceedings{schonberger_sigmod26,
        title = {{Large-Scale Multiple Query Optimisation with Incremental Quantum(-Inspired) Annealing}},
        author = {Schönberger, Manuel and Trummer, Immanuel and Mauerer, Wolfgang},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3749171},
        url = {https://dl.acm.org/doi/10.1145/3749171},
        year = {2026}
}

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9,969 Hybrid Mixed Integer Linear Programming for Large-Scale Join Order Optimisation 2026 VLDB 5.1845938e-05
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