Research Article
Quantum Data Management and Quantum Machine Learning for Data Management: State-of-the-Art and Open Challenges
@INPROCEEDINGS{10.1007/978-3-031-35081-8_20, author={Sven Groppe and Jinghua Groppe and Umut \`{E}alıkyılmaz and Tobias Winker and Le Gruenwal}, title={Quantum Data Management and Quantum Machine Learning for Data Management: State-of-the-Art and Open Challenges}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part II}, proceedings_a={ICISML PART 2}, year={2023}, month={7}, keywords={Quantum Computing Data Management Quantum Machine Learning Databases}, doi={10.1007/978-3-031-35081-8_20} }- Sven Groppe
Jinghua Groppe
Umut Çalıkyılmaz
Tobias Winker
Le Gruenwal
Year: 2023
Quantum Data Management and Quantum Machine Learning for Data Management: State-of-the-Art and Open Challenges
ICISML PART 2
Springer
DOI: 10.1007/978-3-031-35081-8_20
Abstract
Quantum computing is an emerging technology and has yet to be exploited by industries to implement practical applications. Research has already laid the foundation for figuring out the benefits of quantum computing for these applications. In this paper, we provide a short overview of the state-of-the-art in data management issues that can be solved by quantum computers and especially by quantum machine learning approaches. Furthermore, we discuss what data management can do to support quantum computing and quantum machine learning.
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