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    <completedYear/>
    <publishedYear>2024</publishedYear>
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    <language>eng</language>
    <pageFirst>27</pageFirst>
    <pageLast>37</pageLast>
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    <publisherName>ACM</publisherName>
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    <title language="eng">Quantum Data Encoding Patterns and their Consequences</title>
    <abstract language="eng">The use of quantum processing units (QPUs) promises speed-ups for solving computational problems, in particular for discrete optimisation. While a few groundbreaking algorithmic approaches are known that can provably outperform classical computers, we observe a scarcity of programming abstractions for constructing efficient quantum algorithms. A good fraction of the literature that addresses solving concrete problems related to database management concentrates on casting them as quadratic unconstrained binary optimisation problems (QUBOs), which can then, among others, be processed on gate-based machines (using the quantum approximate optimisation algorithm), or quantum annealers. A critical aspect that affects efficiency and scalability of either of these approaches is how classical data are loaded into qubits, respectively how problems are encoded into QUBO representation. The effectiveness of encodings is known to be of crucial importance for quantum computers, especially since the amount of available qubits is strongly limited in the era of noisy, intermediate-size quantum computers.&#13;
In this paper, we present three encoding patterns, discuss their impact on scalability, and their ease of use. We consider the recreational (yet computationally challenging) Sudoku problem and its reduction to graph colouring as an illustrative example to discuss their individual benefits and disadvantages. Our aim is enable database researchers to choose an appropriate encoding scheme for their purpose without having to acquire in-depth knowledge on quantum peculiarities, thus easing the path towards applying quantum acceleration on data management systems.</abstract>
    <parentTitle language="eng">Q-Data '24: Proceedings of the 1st Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications, June 9 - 15, 2024, Santiago AA Chile</parentTitle>
    <identifier type="doi">10.1145/3665225.3665446</identifier>
    <identifier type="isbn">979-8-4007-0553-3</identifier>
    <enrichment key="opus.import.date">2024-09-11T22:10:01+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Martin Gogeißl</author>
    <author>Hila Safi</author>
    <author>Wolfgang Mauerer</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor für Digitalisierung (LFD)</collection>
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