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  <doc>
    <id>5816</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <pageLast/>
    <pageNumber>8</pageNumber>
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    <publisherName>ACM</publisherName>
    <publisherPlace>New York</publisherPlace>
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    <title language="eng">Quantum Machine Learning: Foundation, New Techniques, and Opportunities for Database Research</title>
    <abstract language="eng">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 havethe 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 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 optimization of join order problem for databases.</abstract>
    <parentTitle language="eng">SIGMOD '23, proceedings of the 2023 International Conference on Management of Data: June 18-23, 2023, Seattle, WA, USA</parentTitle>
    <identifier type="doi">10.1145/3555041.3589404</identifier>
    <enrichment key="opus.import.date">2023-02-14T16:16:10+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Tobias Winker</author>
    <author>Sven Groppe</author>
    <author>Valter Johan Edvard Uotila</author>
    <author>Zhengtong Yan</author>
    <author>Jiaheng Lu</author>
    <author>Franz Maja</author>
    <author>Wolfgang Mauerer</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor für Digitalisierung (LFD)</collection>
  </doc>
  <doc>
    <id>7687</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>409</pageFirst>
    <pageLast>420</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
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    <title language="eng">Hype or Heuristic? Quantum Reinforcement Learning for Join Order Optimisation</title>
    <abstract language="eng">Identifying optimal join orders (JOs) stands out as a key challenge in database research and engineering. Owing to the large search space, established classical methods rely on approximations and heuristics. Recent efforts have successfully explored reinforcement learning (RL) for JO. Likewise, quantum versions of RL have received considerable scientific attention. Yet, it is an open question if they can achieve sustainable, overall practical advantages with improved quantum processors.&#13;
In this paper, we present a novel approach that uses quantum reinforcement learning (QRL) for JO based on a hybrid variational quantum ansatz. It is able to handle general bushy join trees instead of resorting to simpler left-deep variants as compared to approaches based on quantum(-inspired) optimisation, yet requires multiple orders of magnitudes fewer qubits, which is a scarce resource even for post-NISQ systems.&#13;
Despite moderate circuit depth, the ansatz exceeds current NISQ capabilities, which requires an evaluation by numerical simulations. While QRL may not significantly outperform classical approaches in solving the JO problem with respect to result quality (albeit we see parity), we find a drastic reduction in required trainable parameters. This benefits practically relevant aspects ranging from shorter training times compared to classical RL, less involved classical optimisation passes, or better use of available training data, and fits data-stream and low-latency processing scenarios. Our comprehensive evaluation and careful discussion delivers a balanced perspective on possible practical quantum advantage, provides insights for future systemic approaches, and allows for quantitatively assessing trade-offs of quantum approaches for one of the most crucial problems of database management systems.</abstract>
    <parentTitle language="eng">2024 IEEE International Conference on Quantum Computing and Engineering (QCE),  15-20 September 2024,  Montreal, QC, Canada</parentTitle>
    <identifier type="doi">10.1109/QCE60285.2024.00055</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-76877</identifier>
    <note>Corresponding author der OTH Regensburg: Maja Franz</note>
    <enrichment key="opus.import.date">2024-09-11T22:10:01+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
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    <enrichment key="CorrespondingAuthor">Maja Franz</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Maja Franz</author>
    <author>Tobias Winker</author>
    <author>Sven Groppe</author>
    <author>Wolfgang Mauerer</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="funding" number="">Publikationsfonds der OTH Regensburg</collection>
    <collection role="institutes" number="">Labor für Digitalisierung (LFD)</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/7687/Franz_Mauerer_Hype_or_Heuristic_IEEE.pdf</file>
  </doc>
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