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Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part II

Research Article

Quantum Data Management and Quantum Machine Learning for Data Management: State-of-the-Art and Open Challenges

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BibTeX Plain Text
  • @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
Sven Groppe1,*, Jinghua Groppe1, Umut Çalıkyılmaz1, Tobias Winker1, Le Gruenwal
  • 1: Institute of Information Systems (IFIS)
*Contact email: groppe@ifis.uni-luebeck.de

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.

Keywords
Quantum Computing Data Management Quantum Machine Learning Databases
Published
2023-07-10
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-35081-8_20
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