QEC lake: Data Lake for AI-enhanced Quantum Error Correction
Summary: QEC Lake unifies fragmented, heterogeneous quantum-error-correction datasets via continuous crawling, simulation, and synthetic augmentation. Its AI-assisted exploration and model-oriented preparation produce comparable, training-ready data for decoder benchmarking and development. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Aadi Patwardhan (Delft University of Technology)
- 2. Max de Groot (Delft University of Technology)
- 3. Andrei Ilinescu (Delft University of Technology)
- 4. Aditya Shankar (Delft University of Technology)
- 5. Yuandou Wang (Delft University of Technology)
- 6. Wenbo Sun (Delft University of Technology)
- 7. Floris Geerts (University of Antwerp)
- 8. Rihan Hai (Delft University of Technology)
BibTeX Citation
@article{patwardhan_vldb26,
title = {{QEC lake: Data Lake for AI-enhanced Quantum Error Correction}},
author = {Patwardhan, Aadi and de Groot, Max and Ilinescu, Andrei and Shankar, Aditya and Wang, Yuandou and Sun, Wenbo and Geerts, Floris and Hai, Rihan},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4574--4577},
doi = {10.14778/3827998.3828069},
url = {https://doi.org/10.14778/3827998.3828069},
year = {2026}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 624 | Data Lake Management: Challenges and Opportunities | 2019 | VLDB | 0.00015479826 |
| 5,701 | Data Discovery in Data Lakes: Operations, Indexes, Systems | 2025 | VLDB | 6.0315322e-05 |
| 10,607 | WaveStitch: Flexible and Fast Conditional Time Series Generation With Diffusion Models | 2026 | SIGMOD | 4.9793485e-05 |
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