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    <completedYear/>
    <publishedYear>2023</publishedYear>
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    <language>eng</language>
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    <pageLast>27</pageLast>
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    <publisherName>ACM</publisherName>
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    <title language="eng">Ready to Leap (by Co-Design)? Join Order Optimisation on Quantum Hardware</title>
    <abstract language="eng">The prospect of achieving computational speedups by exploiting quantum phenomena makes the use of quantum processing units (QPUs) attractive for many algorithmic database problems. Query optimisation, which concerns problems that typically need to explore large search spaces, seems like an ideal match for the known quantum algorithms. We present the first quantum implementation of join ordering, which is one of the most investigated and fundamental query optimisation problems, based on a reformulation to quadratic binary unconstrained optimisation problems. We empirically characterise our method on two state-of-the-art approaches (gate-based quantum computing and quantum annealing), and identify speed-ups compared to the best know classical join ordering approaches for input sizes that can be processed with current quantum annealers. However, we also confirm that limits of early-stage technology are quickly reached.&#13;
Current QPUs are classified as noisy, intermediate scale quantum computers (NISQ), and are restricted by a variety of limitations that reduce their capabilities as compared to ideal future quantum computers, which prevents us from scaling up problem dimensions and reaching practical utility. To overcome these challenges, our formulation accounts for specific QPU properties and limitations, and allows us to trade between achievable solution quality and possible problem size.&#13;
In contrast to all prior work on quantum computing for query optimisation and database-related challenges, we go beyond currently available QPUs, and explicitly target the scalability limitations: Using insights gained from numerical simulations and our experimental analysis, we identify key criteria for co-designing QPUs to improve their usefulness for join ordering, and show how even relatively minor physical architectural improvements can result in substantial enhancements. Finally, we outline a path towards practical utility of custom-designed QPUs.</abstract>
    <parentTitle language="eng">Proceedings of the ACM on Management of Data, PACMMOD</parentTitle>
    <identifier type="doi">10.1145/3588946</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-56634</identifier>
    <note>Corresponding author: Manuel Schönberger</note>
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    <enrichment key="CorrespondingAuthor">Manuel Schönberger</enrichment>
    <licence>Creative Commons - CC BY-NC-SA - Namensnennung - Nicht kommerziell -  Weitergabe unter gleichen Bedingungen 4.0 International</licence>
    <author>Manuel Schönberger</author>
    <author>Stefanie Scherzinger</author>
    <author>Wolfgang Mauerer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Hardware</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Quantum cmputation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Informations systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Join algorithms</value>
    </subject>
    <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="oaweg" number="">Corresponding author der OTH Regensburg</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/5663/Schoenberger_ACM.pdf</file>
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  <doc>
    <id>6475</id>
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    <publishedYear>2023</publishedYear>
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    <language>eng</language>
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    <publisherName>RWTH Aachen, Sun SITE Central Europe</publisherName>
    <publisherPlace>Aachen</publisherPlace>
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    <title language="eng">Quantum Optimisation of General Join Trees</title>
    <abstract language="eng">Recent advances in the manufacture of quantum computers attract much attention over a wide range of fields, as early-stage quantum processing units (QPU) have become accessible. While contemporary quantum machines are very limited in size and capabilities, mature QPUs are speculated to eventually excel at optimisation problems. This makes them an attractive technology for database problems, many of which are based on complex optimisation problems with large solution spaces. Yet, the use of quantum approaches on database problems remains largely unexplored. In this paper, we address the long-standing join ordering problem, one of the most extensively researched database problems. Rather than running arbitrary code, QPUs require specific mathematical problem encodings. An encoding for the join ordering problem was recently proposed, allowing first small-scale queries to be optimised on quantum hardware. However, it is based on a faithful transformation of a mixed integer linear programming (MILP) formulation for JO, and inherits all limitations of the MILP method. Most strikingly, the existing encoding only considers a solution space with left-deep join trees, which tend to yield larger costs than general, bushy join trees. We propose a novel QUBO encoding for the join ordering problem. Rather than transforming existing formulations, we&#13;
construct a native encoding tailored to quantum systems, which allows us to process general bushy join trees. This makes the&#13;
full potential of QPUs available for solving join order optimisation problems.</abstract>
    <parentTitle language="eng">Joint Workshops at 49th International Conference on Very Large Data Bases (VLDBW’23) — International Workshop on Quantum Data Science and Management (QDSM’23), August 28 - September 1, 2023, Vancouver, Canada (CEUR Workshop Proceedings)</parentTitle>
    <identifier type="url">https://ceur-ws.org/Vol-3462/QDSM2.pdf</identifier>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Manuel Schönberger</author>
    <author>Immanuel Trummer</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>
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  <doc>
    <id>5618</id>
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    <language>eng</language>
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    <title language="eng">Quantum Computing for DB - Applicability on Multi Query Optimization and Join Order Optimization</title>
    <parentTitle language="deu">Frühjahrstreffen Fachgruppe Datenbanken in Potsdam, 2022</parentTitle>
    <identifier type="url">https://www.lfdr.de/Publications/2022/FGDB_Poster_Schoenberger.pdf</identifier>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Manuel Schönberger</author>
    <author>Stefanie Scherzinger</author>
    <author>Wolfgang Mauerer</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
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  <doc>
    <id>3470</id>
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    <publishedYear>2022</publishedYear>
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    <language>eng</language>
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    <publisherName>IEEE</publisherName>
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    <title language="eng">Peel | Pile? Cross-Framework Portability of Quantum Software</title>
    <abstract language="eng">In recent years, various vendors have made quantum software frameworks available. Yet with vendor-specific frameworks, code portability seems at risk, especially in a field where hardware and software libraries have not yet reached a consolidated state, and even foundational aspects of the technologies are still in flux. Accordingly, the development of vendor-independent quantum programming languages and frameworks is often suggested. This follows the established architectural pattern of introducing additional levels of abstraction into software stacks, thereby piling on layers of abstraction. Yet software architecture also provides seemingly less abstract alternatives, namely to focus on hardware-specific formulations of problems that peel off unnecessary layers. In this article, we quantitatively and experimentally explore these strategic alternatives, and compare popular quantum frameworks from the software implementation perspective. We find that for several specific, yet generalisable problems, the mathematical formulation of the problem to be solved is not just sufficiently abstract and serves as precise description, but is likewise concrete enough to allow for deriving framework-specific implementations with little effort. Additionally, we argue, based on analysing dozens of existing quantum codes, that porting between frameworks is actually low-effort, since the quantum- and framework-specific portions are very manageable in terms of size, commonly in the order of mere hundreds of lines of code. Given the current state-of-the-art in quantum programming practice, this leads us to argue in favour of peeling off unnecessary abstraction levels.</abstract>
    <parentTitle language="eng">2022 IEEE 19th International Conference on Software Architecture Companion (ICSA-C), 12-15 March 2022, Honolulu, HI, USA</parentTitle>
    <identifier type="doi">10.1109/ICSA-C54293.2022.00039</identifier>
    <note>Preprint unter: https://arxiv.org/abs/2203.06289</note>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Manuel Schönberger</author>
    <author>Maja Franz</author>
    <author>Stefanie Scherzinger</author>
    <author>Wolfgang Mauerer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Computer Science</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Quantum Physics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Software Engineering</value>
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  <doc>
    <id>6649</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>14</pageNumber>
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    <title language="eng">Quantum-Inspired Digital Annealing for Join Ordering</title>
    <abstract language="eng">Finding the optimal join order (JO) is one of the most important problems in query optimisation, and has been extensively considered in research and practise. As it involves huge search spaces, approximation approaches and heuristics are commonly used, which explore a reduced solution space at the cost of solution quality. To explore even large JO search spaces, we may consider special-purpose software, such as mixed-integer linear programming (MILP) solvers, which have successfully solved JO problems. However, even mature solvers cannot overcome the limitations of conventional hardware prompted by the end of Moore’s law. We consider quantum-inspired digital annealing hardware, which takes inspiration from quantum processing units (QPUs). Unlike QPUs, which likely remain limited in size and reliability in the near and mid-term future, the digital annealer (DA) can solve large instances of mathematically encoded optimisation problems today. We derive a novel, native encoding for the JO problem tailored to this class of machines that substantially improves over known MILP and quantum-based encodings, and reduces encoding size over the state-of-the-art. By augmenting the computation with a novel readout method, we derive valid join orders for each solution obtained by the (probabilistically operating) DA. Most importantly and despite an extremely large solution space, our approach scales to practically relevant dimensions of around 50 relations and improves result quality over conventionally employed approaches, adding a novel alternative to solving the long-standing JO problem.</abstract>
    <parentTitle language="eng">Proceedings of the VLDB Endowment</parentTitle>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Manuel Schönberger</author>
    <author>Immanuel Trummer</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>
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  <doc>
    <id>7691</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <title language="eng">Reproducibility Report for ACM SIGMOD 2023 Paper: Ready to Leap (by Co-Design)? Join Order Optimisation</title>
    <abstract language="eng">The paper “Ready to Leap (by Co-Design)? Join Order Optimisation on Quantum Hardware” proposes the first approach to solve the problem of join order optimization on quantum hardware. The authors characterize the applicability and limitations of current state-of-the-art quantum hardware, i. e. gate-based quantum computing and quantum annealing, for join ordering and recommend key improvements to the physical hardware to reach practical utility. Based on the provided database queries and QPU system processing data, we have been able to reproduce the original paper’s key insights and quantum problem characteristics reported in its experimental section. The authors provided a self-contained and fully automated reproduction package, including data (database queries, statistics, and collected QPU processing data), experiment scripts, and plotting routines that&#13;
allowed the identical reconstruction of the three main figures in the paper.</abstract>
    <identifier type="url">https://www.lfdr.de/Publications/2024/SIGMOD_ARI_2023___Paper_37.pdf</identifier>
    <enrichment key="opus.import.date">2024-09-11T22:10:01+00:00</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Sebastian Schmidl</author>
    <author>Yangshen Deng</author>
    <author>Pingchuan Ma</author>
    <author>Manuel Schönberger</author>
    <author>Wolfgang Mauerer</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
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    <id>7682</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
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    <title language="eng">Applicability of Quantum Computing on Database Query Optimization</title>
    <parentTitle language="eng">Frühjahrstreffen Fachgruppe Datenbanken in Potsdam (Poster Presentation)</parentTitle>
    <enrichment key="opus.import.date">2024-09-11T22:10:01+00:00</enrichment>
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    <author>Manuel Schönberger</author>
    <author>Stefanie Scherzinger</author>
    <author>Wolfgang Mauerer</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
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  <doc>
    <id>8840</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>25</pageNumber>
    <edition/>
    <issue>4</issue>
    <volume>3</volume>
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    <publisherName>ACM</publisherName>
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    <title language="eng">Large-Scale Multiple Query Optimisation with Incremental Quantum(-Inspired) Annealing</title>
    <abstract language="eng">Multiple-query optimization (MQO) seeks to reduce redundant work across query batches. While MQO offers opportunities for dramatic performance improvements, the problem is NP-hard, limiting the sizes of problems that can be solved on generic hardware. We propose to leverage specialized hardware solvers for optimization, such as Fujitsu's Digital Annealer (DA), to scale up MQO to problem sizes formerly out of reach.&#13;
We present a novel incremental processing approach that combines classical computation with DA acceleration. By efficiently partitioning MQO problems into sets of partial problems, and by applying a dynamic search steering strategy that reapplies initially discarded information to incrementally process individual problems, our method overcomes capacity limitations, and scales to extremely large MQO instances (up to νm1000 queries). A thorough and comprehensive empirical evaluation finds our method substantially outperforms existing approaches. Our generalisable framework lays the ground for other database use-cases on quantum-inspired hardware, and bridges towards future quantum accelerators.</abstract>
    <parentTitle language="eng">Proceedings of the ACM on Management of Data</parentTitle>
    <identifier type="doi">10.1145/3749171</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Manuel Schönberger</author>
    <author>Immanuel Trummer</author>
    <author>Wolfgang Mauerer</author>
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    <language>eng</language>
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    <edition/>
    <issue>3</issue>
    <volume>19</volume>
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    <publisherName>VLDB Endowment</publisherName>
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    <title language="eng">Hybrid Mixed Integer Linear Programming for Large-Scale Join Order Optimisation</title>
    <abstract language="eng">Finding optimal join orders is among the most crucial steps to be performed by query optimisers. Though extensively studied in data management research, the problem remains far from solved: While query optimisers rely on exhaustive search methods to determine ideal solutions for small problems, such methods reach their limits once queries grow in size. Yet, large queries become increasingly common in real-world scenarios, and require suitable methods to generate efficient execution plans. While a variety of heuristics have been proposed for large-scale query optimisation, they suffer from degrading solution quality as queries grow in size, or feature highly sub-optimal worst-case behavior, as we will show.&#13;
We propose a novel method based on the paradigm of mixed integer linear programming (MILP): By deriving a novel MILP model capable of optimising arbitrary bushy tree structures, we address the limitations of existing MILP methods for join ordering, and can rely on highly optimised MILP solvers to derive efficient tree structures that elude competing methods. To ensure optimisation efficiency, we embed our MILP method into a hybrid framework, which applies MILP solvers precisely where they provide the greatest advantage over competitors, while relying on more efficient methods for less complex optimisation steps. Thereby, our approach gracefully scales to extremely large query sizes joining up to 100 relations, and consistently achieves the most robust plan quality among a large variety of competing join ordering methods.</abstract>
    <parentTitle language="eng">Proceedings of the VLDB Endowment</parentTitle>
    <identifier type="doi">10.14778/3778092.3778097</identifier>
    <enrichment key="opus.import.date">2026-03-30T12:30:30+00:00</enrichment>
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    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Manuel Schönberger</author>
    <author>Immanuel Trummer</author>
    <author>Wolfgang Mauerer</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
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    <pageNumber>8</pageNumber>
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    <issue/>
    <volume>337</volume>
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    <title language="eng">From Hope to Heuristic: Realistic Runtime Estimates for Quantum Optimisation in NHEP</title>
    <abstract language="eng">Noisy Intermediate-Scale Quantum (NISQ) computers, despite their limitations, present opportunities for near-term quantum advantages in Nuclear and High-Energy Physics (NHEP) when paired with specially designed quantum algorithms and processing units. This study focuses on core algorithms that solve optimization problems through the quadratic Ising or Quadratic Unconstrained Binary Optimisation model, specifically Quantum Annealing and the Quantum Approximate Optimisation Algorithm (QAOA).&#13;
&#13;
In particular, we estimate runtimes and scalability for the task of particle Track Reconstruction (TR), a key computing challenge in NHEP, and investigate how the classical parameter space in QAOA, along with techniques like a Fourieranalysis based heuristic, can facilitate future quantum advantages. The findings indicate that lower frequency components in the parameter space are crucial for effective annealing schedules, suggesting that heuristics can improve resource efficiency while achieving near-optimal results. Overall, the study highlights the potential of NISQ computers in NHEP and the significance of co-design approaches and heuristic techniques in overcoming challenges in quantum algorithms.</abstract>
    <parentTitle language="eng">EPJ Web of Conferences; 27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)</parentTitle>
    <identifier type="doi">10.1051/epjconf/202533701282</identifier>
    <note>Corresponding author der OTH Regensburg: Maja Franz</note>
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