Advancements in Quantum Computing—Viewpoint - SpringerLink
The two most advanced are superconducting and ion-based systems [21]. They all fundamentally have qubits—two-level energy systems—that can have ...
https://doi.org/10.1145/3555041.3589404
Winker T, Groppe S, Uotila V, Zhengtong Y, Jiaheng L, Franz M, Mauerer W (2023) Quantum machine learning: foundation new techniques, and opportunities for database research. Companion of the 2023 International Conference on Management of Data, {SIGMOD/PODS} 2023, Seattle, June 18–23, 2023, pp 45–52 <anchor>https://doi.org/10.1145/3555041.3589404</anchor> (Ippokratis Pandis, K. Selçuk Candan, Sihem Amer{-}Yahia Sudipto Das. Seattle, WA, USA : ACM)
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# Quantum Machine Learning: Foundation, New Techniques, and Opportunities for Database Research

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Quantum Machine Learning: Foundation, New Techniques, and Opportunities for Database Research

Pages 45 - 52

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## Abstract

In the last few years, the field of quantum computing has experienced remarkable progress. The prototypes of quantum computers already exist and have been made available to users through cloud services (e.g., IBM Q experience, Google quantum AI, or Xanadu quantum cloud). While fault-tolerant and large-scale quantum computers are not available yet (and may not be for a long time, if ever), the potential of this new technology is undeniable. Quantum algorithms have the proven ability to either outperform classical approaches for several tasks, or are impossible to be efficiently simulated by classical means under reasonable complexity-theoretic assumptions. Even imperfect current-day technology is speculated to exhibit computational advantages over classical systems. Recent research is using quantum computers to solve machine learning tasks. Meanwhile, the database community has already successfully applied various machine learning algorithms for data management tasks, so combining the fields seems to be a promising endeavour. However, quantum machine learning is a new research field for most database researchers. In this tutorial, we provide a fundamental introduction to quantum computing and quantum machine learning and show the potential benefits and applications for database research. In addition, we demonstrate how to apply quantum machine learning to the join order optimization problem in databases.

## Supplemental Material

MP4 File

Quantum machine learning is a new research field for most database researchers. In this tutorial, we will first provide a dive into the basics of quantum computing. Afterward, we will introduce quantum machine learning by comparing it with classical machine learning. In a hands-on tutorial, we will show how to apply quantum computing with a special focus on quantum machine learning for database tasks like join order optimization. Finally, we will discuss quantum machine learning opportunities and future work for database research.

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## Cited By

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Quantum machine learning is a new research field for most database researchers. In this tutorial, we will first provide a dive into the basics of quantum computing. Afterward, we will introduce quantum machine learning by comparing it with classical machine learning. In a hands-on tutorial, we will show how to apply quantum computing with a special focus on quantum machine learning for database tasks like join order optimization. Finally, we will discuss quantum machine learning opportunities and future work for database research. https://dl.acm.org/doi/10.1145/3555041.3589404#SIGMOD23-modtt009.mp4

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  https://dl.acm.org/doi/10.14778/3681954.3682025

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  https://dl.acm.org/doi/10.1145/3665225.3665446

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  https://doi.org/10.1007/s13222-024-00467-4

- Schönberger MTrummer IMauerer W(2023)Quantum-Inspired Digital Annealing for Join OrderingProceedings of the VLDB Endowment10.14778/3632093.363211217:3(511-524)Online publication date: 1-Nov-2023
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- Çalikyilmaz UGroppe SGroppe JWinker TPrestel SShagieva FArya DPreis FGruenwald L(2023)Opportunities for Quantum Acceleration of Databases: Optimization of Queries and Transaction SchedulesProceedings of the VLDB Endowment10.14778/3598581.359860316:9(2344-2353)Online publication date: 1-May-2023
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- Nayak NRehfeld JWinker TWarnke BÇalikyilmaz UGroppe S(2023)Constructing Optimal Bushy Join Trees by Solving QUBO Problems on Quantum Hardware and SimulatorsProceedings of the International Workshop on Big Data in Emergent Distributed Environments10.1145/3579142.3594298(1-7)Online publication date: 18-Jun-2023
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#### Affiliations



TobiasWinker

University of Lübeck, Lübeck, Germany



SvenGroppe

University of Lübeck, Lübeck, Germany



ValterUotila

University of Helsinki, Helsinki, Finland



ZhengtongYan

University of Helsinki, Helsinki, Finland



JiahengLu

University of Helsinki, Helsinki, Finland



MajaFranz

Technical University of Applied Science Regensburg, Regensburg, Germany



WolfgangMauerer

Technical University of Applied Science Regensburg, Regensburg, Germany

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