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    <title language="eng">Out of the Loop: Structural Approximation of Optimisation Landscapes and non-Iterative Quantum Optimisation</title>
    <abstract language="eng">The Quantum Approximate Optimisation Algorithm (qaoa) is a widely studied quantum-classical iterative heuristic for combinatorial optimisation. While qaoa targets problems in complexity class NP, the classical optimisation procedure required in every iteration is itself known to be NP-hard. Still, advantage over classical approaches is suspected for certain scenarios, but nature and origin of its computational power are not yet satisfactorily understood. By introducing means of efficiently and accurately approximating the qaoa optimisation landscape from solution space structures, we derive a new algorithmic variant: Instead of performing an iterative quantum-classical computation for each input instance, our non-iterative method is based on a quantum circuit that is instance-independent, but problem-specific. It matches or outperforms unit-depth qaoa for key combinatorial problems, despite reduced computational effort. Our approach is based on proving a long-standing conjecture regarding instance-independent structures in qaoa. By ensuring generality, we link existing empirical observations on qaoa parameter clustering to established approaches in theoretical computer science, and provide a sound foundation for understanding the link between structural properties of solution spaces and quantum optimisation.</abstract>
    <identifier type="arxiv">2408.06493</identifier>
    <note>er Aufsatz wurde peer-reviewed veröffentlicht und ist ebenfalls in diesem Repositorium verzeichnet unter: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/start/3/rows/10/sortfield/score/sortorder/desc/searchtype/simple/query/out+of+the+loop/docId/8843</note>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Tom Krüger</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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    <volume>295</volume>
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    <publisherName>EDP Sciences</publisherName>
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    <title language="eng">Co-Design of Quantum Hardware and Algorithms in Nuclear and High Energy Physics</title>
    <abstract language="eng">Quantum computing (QC) has emerged as a promising technology, and is believed to have the potential to advance nuclear and high energy physics (NHEP) by harnessing quantum mechanical phenomena to accelerate computations. In this paper, we give a brief overview of the current state of quantum computing by highlighting challenges it poses and opportunities it offers to the NHEP community. Noisy intermediate-scale quantum (NISQ) computers, while limited by imperfections and small scale, may hold promise for near-term quantum advantages when coupled with co-designed quantum algorithms and special-purpose quantum processing units (QPUs). We explore various applications in NHEP, including quantum simulation, event classification, and realtime experiment control, emphasising the potential of variational quantum circuits and related techniques. To identify current interests of the community, we perform an analysis of recent literature in NHEP related to QC.</abstract>
    <parentTitle language="deu">EPJ Web of Conferences</parentTitle>
    <identifier type="doi">10.1051/epjconf/202429512002</identifier>
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    <enrichment key="ConferenceStatement">26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Maja Franz</author>
    <author>Pía Zurita</author>
    <author>Markus Diefenthaler</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>7937</id>
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    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
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    <title language="deu">It's Quick to be Square: Fast Quadratisation for Quantum Toolchains</title>
    <abstract language="deu">Many of the envisioned use-cases for quantum computers involve optimisation processes. While there are many algorithmic primitives to perform the required calculations, all eventually lead to quantum gates operating on quantum bits, with an order as determined by the structure of the objective function and the properties of target hardware. When the structure of the problem representation is not aligned with structure and boundary conditions of the executing hardware, various overheads degrading the computation may arise, possibly negating any possible quantum advantage. Therefore, automatic transformations of problem representations play an important role in quantum computing when descriptions (semi-)targeted at humans must be cast into forms that can be executed on quantum computers. Mathematically equivalent formulations are known to result in substantially different non-functional properties depending on hardware, algorithm and detail properties of the problem. Given the current state of noisy intermediate-scale quantum (NISQ) hardware, these effects are considerably more pronounced than in classical computing. Likewise, efficiency of the transformation itself is relevant because possible quantum advantage may easily be eradicated by the overhead of transforming between representations. In this paper, we consider a specific class of higher-level representations, i.e. polynomial unconstrained binary optimisation problems, and devise novel automatic transformation mechanisms into widely used quadratic unconstrained binary optimisation problems that substantially improve efficiency and versatility over the state of the art. We also identify what influence factors of lower-level details can be abstracted away in the transformation process, and which details must be made available to higher-level abstractions.</abstract>
    <identifier type="arxiv">2411.19934</identifier>
    <note>Der Aufsatz wurde peer-reviewed veröffentlicht und ist ebenfalls in diesem Repositorium verzeichnet unter: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/9006</note>
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    <licence>Creative Commons - CC BY-NC-SA - Namensnennung - Nicht kommerziell -  Weitergabe unter gleichen Bedingungen 4.0 International</licence>
    <author>Lukas Schmidbauer</author>
    <author>Elisabeth Lobe</author>
    <author>Ina Schäfer</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>7689</id>
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    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <title language="eng">QCEDA: Using Quantum Computers for EDA</title>
    <abstract language="eng">The field of Electronic Design Automation (EDA) is crucial for microelectronics, but the increasing complexity of Integrated Circuits (ICs) poses challenges for conventional EDA: Corresponding problems are often NP-hard and are therefore in general solved by heuristics, not guaranteeing optimal solutions. Quantum computers may offer better solutions due to their potential for optimization through entanglement, superposition, and interference. Most of the works in the area of EDA and quantum computers focus on how to use EDA for building quantum circuits. However, almost no research focuses on exploiting quantum computers for solving EDA problems. Therefore, this paper investigates the feasibility and potential of quantum computing for a typical EDA optimization problem broken down to the Min-k-Union problem. The problem is mathematically transformed into a Quadratic Unconstrained Binary Optimization (QUBO) problem, which was successfully solved on an IBM quantum computer and a D-Wave quantum annealer.</abstract>
    <identifier type="arxiv">2403.12998</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>Matthias Jung</author>
    <author>Sven O. Krumke</author>
    <author>Christof Schroth</author>
    <author>Elisabeth Lobe</author>
    <author>Wolfgang Mauerer</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
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  <doc>
    <id>7687</id>
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    <publishedYear>2024</publishedYear>
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    <language>eng</language>
    <pageFirst>409</pageFirst>
    <pageLast>420</pageLast>
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    <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>
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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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    <id>7686</id>
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    <language>eng</language>
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    <title language="eng">Guided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rule</title>
    <abstract language="eng">The study of variational quantum algorithms (VQCs) has received significant attention from the quantum computing community in recent years. These hybrid algorithms, utilizing both classical and quantum components, are well-suited for noisy intermediate-scale quantum devices. Though estimating exact gradients using the parameter-shift rule to optimize the VQCs is realizable in NISQ devices, they do not scale well for larger problem sizes. The computational complexity, in terms of the number of circuit evaluations required for gradient estimation by the parameter-shift rule, scales linearly with the number of parameters in VQCs. On the other hand, techniques that approximate the gradients of the VQCs, such as the simultaneous perturbation stochastic approximation (SPSA), do not scale with the number of parameters but struggle with instability and often attain suboptimal solutions. In this work, we introduce a novel gradient estimation approach called Guided-SPSA, which meaningfully combines the parameter-shift rule and SPSA-based gradient approximation. The Guided-SPSA results in a 15% to 25% reduction in the number of circuit evaluations required during training for a similar or better optimality of the solution found compared to the parameter-shift rule. The Guided-SPSA outperforms standard SPSA in all scenarios and outperforms the parameter-shift rule in scenarios such as suboptimal initialization of the parameters. We demonstrate numerically the performance of Guided-SPSA on different paradigms of quantum machine learning, such as regression, classification, and reinforcement learning.</abstract>
    <identifier type="arxiv">2404.15751</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Maniraman Periyasamy</author>
    <author>Axel Plinge</author>
    <author>Christopher Mutschler</author>
    <author>Daniel D. Scherer</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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    <id>7690</id>
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    <language>eng</language>
    <pageFirst>27</pageFirst>
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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>
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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>
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    <language>eng</language>
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    <title language="eng">Polynomial Reduction Methods and their Impact on QAOA Circuits</title>
    <abstract language="eng">Abstraction layers are of paramount importance in software architecture, as they shield the higher-level formulation of payload computations from lower-level details. Since quantum computing (QC) introduces many such details that are often unaccustomed to computer scientists, an obvious desideratum is to devise appropriate abstraction layers for QC. For discrete optimisation, one such abstraction is to cast problems in quadratic unconstrained binary optimisation (QUBO) form, which is amenable to a variety of quantum approaches. However, different mathematically equivalent forms can lead to different behaviour on quantum hardware, ranging from ease of mapping onto qubits to performance scalability. In this work, we show how using higher-order problem formulations (that provide better expressivity in modelling optimisation tasks than plain QUBO formulations) and their automatic transformation into QUBO form can be used to leverage such differences to prioritise between different desired non-functional properties for quantum optimisation. Based on a practically relevant use-case and a graph-theoretic analysis, we evaluate how different transformation approaches influence widely used quantum performance metrics (circuit depth, gates count, gate distribution, qubit scaling), and also consider the classical computational efforts required to perform the transformations, as they influence possibilities for achieving future quantum advantage. Furthermore, we establish more general properties and invariants of the transformation methods. Our quantitative study shows that the approach allows us to satisfy different trade-offs, and suggests various possibilities for the future construction of general-purpose abstractions and automatic generation of useful quantum circuits from high-level problem descriptions.</abstract>
    <parentTitle language="eng">IEEE International Conference on Quantum Software (QSW), 7-13 July 2024, Shenzhen, China</parentTitle>
    <identifier type="doi">10.1109/QSW62656.2024.00018</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Lukas Schmidbauer</author>
    <author>Karen Wintersperger</author>
    <author>Elisabeth Lobe</author>
    <author>Wolfgang Mauerer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>HW-SW co design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NISQ systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>noisy quantum computing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>quantum software engineering</value>
    </subject>
    <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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    <id>7685</id>
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    <publishedYear>2024</publishedYear>
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    <language>eng</language>
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    <publisherName>Springer-Nature</publisherName>
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    <title language="eng">Challenges for Quantum Software Engineering: An Industrial Application Scenario Perspective</title>
    <abstract language="deu">Quantum software is becoming a key enabler for applying quantum computing to industrial use cases. This poses challenges to quantum software engineering in providing efficient and effective means to develop such software. Eventually, this must be reliably achieved in time, on budget, and in quality, using sound and well-principled engineering approaches. Given that quantum computers are based on fundamentally different principles than classical machines, this raises the question if, how, and to what extent established techniques for systematically engineering software need to be adapted. In this chapter, we analyze three paradigmatic application scenarios for quantum software engineering from an industrial perspective. The respective use cases center around (1) optimization and quantum cloud services, (2) quantum simulation, and (3) embedded quantum computing. Our aim is to provide a concise overview of the current and future applications of quantum computing in diverse industrial settings. We derive presumed challenges for quantum software engineering and thus provide research directions for this emerging field.</abstract>
    <parentTitle language="eng">Quantum Software: Aspects of Theory and System Design</parentTitle>
    <identifier type="doi">10.1007/978-3-031-64136-7_12</identifier>
    <identifier type="isbn">978-3-031-64135-0</identifier>
    <identifier type="isbn">978-3-031-64136-7</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Cecilia Carbonelli</author>
    <author>Michael Felderer</author>
    <author>Matthias Jung</author>
    <author>Elisabeth Lobe</author>
    <author>Malte Lochau</author>
    <author>Sebastian Luber</author>
    <author>Wolfgang Mauerer</author>
    <author>Rudolf Ramler</author>
    <author>Ina Schäfer</author>
    <author>Christoph Schroth</author>
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      <value>industrial use cases</value>
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      <value>quantum computing</value>
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      <value>quantum software engineering</value>
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      <value>software development</value>
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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>
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    <author>Sebastian Schmidl</author>
    <author>Yangshen Deng</author>
    <author>Pingchuan Ma</author>
    <author>Manuel Schönberger</author>
    <author>Wolfgang Mauerer</author>
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    <title language="eng">Towards View-based Development of Quantum Software</title>
    <abstract language="eng">Quantum computing is an interdisciplinary field that relies on the expertise of many&#13;
different stakeholders. The views of various stakeholders on the subject of quantum computing may differ, thereby complicating communication. To address this, we propose a view-based quantum development approach based on a Single Underlying Model (SUM) and a supporting quantumIntegrated Development Environment (IDE). We highlight emerging challenges for future research.</abstract>
    <identifier type="arxiv">2406.18363</identifier>
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    <enrichment key="ConferenceStatement">Proceedings of the GI Quantum Computing Workshop. - (GIQC ’24)</enrichment>
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    <author>Joshua Ammermann</author>
    <author>Wolfgang Mauerer</author>
    <author>Ina Schäfer</author>
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      <value>Quantum Computing</value>
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    <subject>
      <language>eng</language>
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      <value>View-based Development</value>
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      <language>eng</language>
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      <value>Integrated Development Environment</value>
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    <title language="eng">Approximating under the Influence of Quantum Noise and Compute Power</title>
    <abstract language="deu">The quantum approximate optimisation algorithm (QAOA) is at the core of many scenarios that aim to combine the power of quantum computers and classical high-performance computing appliances for combinatorial optimisation. Several obstacles challenge concrete benefits now and in the foreseeable future: Imperfections quickly degrade algorithmic performance below practical utility; overheads arising from alternating between classical and quantum primitives can counter any advantage; and the choice of parameters or algorithmic variant can substantially influence runtime and result quality. Selecting the optimal combination is a non-trivial issue, as it not only depends on user requirements, but also on details of the hardware and software stack. Appropriate automation can lift the burden of choosing optimal combinations for end-users: They should not be required to understand technicalities like differences between QAOA variants, required number of QAOA layers, or necessary measurement samples. Yet, they should receive best-possible satisfaction of their non-functional requirements, be it performance or other. We determine factors that affect solution quality and temporal behaviour of four QAOA variants using comprehensive density-matrix-based simulations targeting three widely studied optimisation problems. Our simulations consider ideal quantum computation, and a continuum of scenarios troubled by realistic imperfections. Our quantitative results, accompanied by a comprehensive reproduction package, show strong differences between QAOA variants that can be pinpointed to narrow and specific effects. We identify influential co-variables and relevant non-functional quality goals that, we argue, mark the relevant ingredients for designing appropriate software engineering abstraction mechanisms and automated tool-chains for devising quantum solutions from high-level problem specifications.</abstract>
    <parentTitle language="eng">Proceedings of WIHPQC@IEEE QCE</parentTitle>
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    <author>Simon Thelen</author>
    <author>Hila Safi</author>
    <author>Wolfgang Mauerer</author>
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    <publisherName>Springer Nature</publisherName>
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    <title language="eng">Superoperators for Quantum Software Engineering</title>
    <abstract language="deu">As implementations of quantum computers grow in size and maturity, the question of how to program this new class of machines is attracting increasing attention in the software engineering domain. Yet, many questions from how to design expressible quantum languages augmented with formal semantics via implementing appropriate optimizing compilers to abstracting details of machine properties in software systems remain challenging. Performing research at this intersection of quantum computing and software engineering requires sufficient knowledge of the physical processes underlying quantum computations, and how to model these. In this chapter, we review a superoperator-based approach to quantum dynamics, as it can provide means that are sufficiently abstract, yet concrete enough to be useful in quantum software and systems engineering, and outline how it is used in several important applications in the field.</abstract>
    <parentTitle language="eng">Quantum Software: Aspects of Theory and System Design</parentTitle>
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    <author>Wolfgang Mauerer</author>
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      <value>quantum computing</value>
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    <subject>
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