Bitte benutzen Sie diese Kennung, um auf die Ressource zu verweisen:
https://doi.org/10.21256/zhaw-30917| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | QardEst : using quantum machine learning for cardinality estimation of join queries |
| Autor/-in: | Kittelmann, Florian Sulimov, Pavel Stockinger, Kurt |
| et. al: | No |
| DOI: | 10.21256/zhaw-30917 |
| Angaben zur Konferenz: | 1st Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications (Q-Data), ACM SIGMOD/PODS 2024, Santiago, Chile, 9 June 2024 |
| Erscheinungsdatum: | Jun-2024 |
| Verlag / Hrsg. Institution: | ZHAW Zürcher Hochschule für Angewandte Wissenschaften |
| Sprache: | Englisch |
| Schlagwörter: | Quantum computing; Quantum machine learning; Database; Optimization |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | Classical and learned query optimizers (LQOs) use cardinality estimations as one of the critical inputs for query planning. Thus, accurately predicting the cardinality of arbitrary queries plays a vital role in query optimization. A recent boom in novel deep learning methods stimulated not only the rise of LQOs but also contributed to the appearance of learned cardinality estimators (LCEs). However, the majority of them are based on classical neural networks, ignoring that multivariate correlations between attributes across different tables could be naturally represented via entanglements in quantum circuits. In this paper, we introduce QardEst - Quantum Cardinality Estimator - a novel quantum neural network approach to estimate the cardinality of join queries. Our experiments conducted with a similar number of trainable parameters suggest that quantum neural networks executed on a quantum simulator outperform classical neural networks in terms of mean squared error as well as the q-error. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/30917 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) |
| Publiziert im Rahmen des ZHAW-Projekts: | GraphQueryML – Verwendung von maschinellem Lernen zur Optimierung von Abfragen in Graphdatenbanken (SNF/DFG) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2024_Kittelmann-etal_QardEst-Evaluation-of-quantum-machine-learning-algorithms.pdf | 743.47 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Kittelmann, F., Sulimov, P., & Stockinger, K. (2024, June). QardEst : using quantum machine learning for cardinality estimation of join queries. 1st Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications (Q-Data), ACM SIGMOD/PODS 2024, Santiago, Chile, 9 June 2024. https://doi.org/10.21256/zhaw-30917
Kittelmann, F., Sulimov, P. and Stockinger, K. (2024) ‘QardEst : using quantum machine learning for cardinality estimation of join queries’, in 1st Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications (Q-Data), ACM SIGMOD/PODS 2024, Santiago, Chile, 9 June 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-30917.
F. Kittelmann, P. Sulimov, and K. Stockinger, “QardEst : using quantum machine learning for cardinality estimation of join queries,” in 1st Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications (Q-Data), ACM SIGMOD/PODS 2024, Santiago, Chile, 9 June 2024, Jun. 2024. doi: 10.21256/zhaw-30917.
KITTELMANN, Florian, Pavel SULIMOV und Kurt STOCKINGER, 2024. QardEst : using quantum machine learning for cardinality estimation of join queries. In: 1st Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications (Q-Data), ACM SIGMOD/PODS 2024, Santiago, Chile, 9 June 2024. Conference paper. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Juni 2024
Kittelmann, Florian, Pavel Sulimov, and Kurt Stockinger. 2024. “QardEst : Using Quantum Machine Learning for Cardinality Estimation of Join Queries.” Conference paper. In 1st Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications (Q-Data), ACM SIGMOD/PODS 2024, Santiago, Chile, 9 June 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-30917.
Kittelmann, Florian, et al. “QardEst : Using Quantum Machine Learning for Cardinality Estimation of Join Queries.” 1st Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications (Q-Data), ACM SIGMOD/PODS 2024, Santiago, Chile, 9 June 2024, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2024, https://doi.org/10.21256/zhaw-30917.
Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt, soweit nicht anderweitig angezeigt.