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    <pageNumber>8</pageNumber>
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    <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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    <enrichment key="CorrespondingAuthor">Franz, Maja</enrichment>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Maja Franz</author>
    <author>Manuel Schönberger</author>
    <author>Melvin Strobl</author>
    <author>Eileen Kühn</author>
    <author>Achim Streit</author>
    <author>Pía Zurita</author>
    <author>Markus Diefenthaler</author>
    <author>Wolfgang Mauerer</author>
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  <doc>
    <id>8996</id>
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    <publishedYear>2025</publishedYear>
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    <language>eng</language>
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    <pageNumber>32</pageNumber>
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    <title language="eng">Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models</title>
    <abstract language="eng">Variational quantum algorithms have received substantial theoretical and empirical attention. As the underlying variational quantum circuit (VQC) can be represented by Fourier series that contain an exponentially large spectrum in the number of input features, hope for quantum advantage remains. Nevertheless, it remains an open problem if and how quantum Fourier models (QFMs) can concretely outperform classical alternatives, as the eventual sources of non-classical computational power (for instance, the role of entanglement) are far from being fully understood. Likewise, hardware noise continues to pose a challenge that will persist also along the path towards fault tolerant quantum computers. In this work, we study VQCs with Fourier lenses, which provides possibilities to improve their understanding, while also illuminating and quantifying constraints and challenges. We seek to elucidate critical characteristics of QFMs under the influence of noise. Specifically, we undertake a systematic investigation into the impact of noise on the Fourier spectrum, expressibility, and entangling capability of QFMs through extensive numerical simulations and link these properties to training performance. The insights may inform more efficient utilisation of quantum hardware and support the design of tailored error mitigation and correction strategies. Decoherence imparts an expected and broad detrimental influence across all Ansätze. Nonetheless, we observe that the severity of these deleterious effects varies among different model architectures, suggesting that certain configurations may exhibit enhanced robustness to noise and show computational utility.</abstract>
    <identifier type="doi">10.48550/arXiv.2506.09527</identifier>
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    <licence>Creative Commons - CC BY-SA - Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International</licence>
    <author>Maja Franz</author>
    <author>Melvin Strobl</author>
    <author>Leonid Chaichenets</author>
    <author>Eileen Kühn</author>
    <author>Achim Streit</author>
    <author>Wolfgang Mauerer</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="">Labor für Digitalisierung (LFD)</collection>
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  <doc>
    <id>8997</id>
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    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>11</pageNumber>
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    <type>article</type>
    <publisherName>IEEE</publisherName>
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    <title language="eng">Make Some Noise! Measuring Noise Model Quality in Real-World Quantum Software</title>
    <abstract language="eng">Noise and imperfections are among the prevalent challenges in quantum software engineering for current NISQ systems. They will remain important in the post-NISQ area, as logical, error-corrected qubits will be based on software mechanisms. As real quantum hardware is still limited in size and accessibility, noise models for classical simulation-that in some cases can exceed dimensions of actual systems-play a critical role in obtaining insights into quantum algorithm performance, and the properties of mechanisms for error correction and mitigation. We present, implement and validate a tunable noise model building on the Kraus channel formalism on a large scale quantum simulator system (Qaptiva). We use empirical noise measurements from IBM quantum (IBMQ) systems to calibrate the model and create a realistic simulation environment. Experimental evaluation of our approach with Greenberger-Horne-Zeilinger (GHZ) state preparation and QAOA applied to an industrial usecase validate our approach, and demonstrate accurate simulation of hardware behaviour at reasonable computational cost. We devise and utilise a method that allows for determining the quality of noise models for larger problem instances than is possible with existing metrics in the literature. To identify potentials of future quantum software and algorithms, we extrapolate the noise model to future partially fault-tolerant systems, and give insights into the interplay between hardware-specific noise modelling and hardware-aware algorithm development.</abstract>
    <parentTitle language="eng">Proceedings of the IEEE International Conference on Quantum Software (QSW), 07-12 July 2025, Helsinki</parentTitle>
    <identifier type="doi">10.1109/QSW67625.2025.00010</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Stefan Raimund Maschek</author>
    <author>Jürgen Schwittalla</author>
    <author>Maja Franz</author>
    <author>Wolfgang Mauerer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Industrial Application</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Noise Model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Quantum Computing</value>
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    <id>8998</id>
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    <publisherName>IEEE</publisherName>
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    <title language="eng">QML-Essentials: A Framework for Working with Quantum Fourier Models</title>
    <abstract language="eng">In this work, we propose a framework in the form of a Python package, specifically designed for the analysis of Quantum Machine Learning models. This framework is based on the PennyLane simulator and facilitates the evaluation and training of Variational Quantum Circuits. It provides additional functionality ranging from the ability to add different types of noise to the classical simulation, over different parameter initialisation strategies, to the calculation of expressibility and entanglement for a given model. As an intrinsic property of Quantum Fourier Models, it provides two methods for calculating the corresponding Fourier spectrum: one via the Fast Fourier Transform and another analytical method based on the expansion of the expectation value using trigonometric polynomials. It also provides a set of predefined approaches that allow a fast and straightforward implementation of Quantum Machine Learning models. With this framework, we extend the PennyLane simulator with a set of tools that allow researchers a more convenient start with Quantum Fourier Models and aim to unify the analysis of Variational Quantum Circuits.</abstract>
    <parentTitle language="eng">Proceedings of the IEEE International Conference on Quantum Software (QSW), 07-12 July 2025, Helsinki</parentTitle>
    <identifier type="doi">10.1109/QSW67625.2025.00035</identifier>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Melvin Strobl</author>
    <author>Maja Franz</author>
    <author>Eileen Kühn</author>
    <author>Wolfgang Mauerer</author>
    <author>Achim Streit</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
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  <doc>
    <id>9013</id>
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    <publishedYear>2026</publishedYear>
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    <language>eng</language>
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    <pageNumber>22</pageNumber>
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    <title language="eng">Pattern or Not? QAOA Parameter Heuristics and Potentials of Parsimony</title>
    <abstract language="eng">Structured variational quantum algorithms such as the Quantum Approximate Optimisation Algorithm (QAOA) have emerged as leading candidates for exploiting advantages of near-term quantum hardware. They interlace classical computation, in particular optimisation of variational parameters, with quantum-specific routines, and combine problem-specific advantages -- sometimes even provable -- with adaptability to the constraints of noisy, intermediate-scale quantum (NISQ) devices. While circuit depth can be parametrically increased and is known to improve performance in an ideal (noiseless) setting, on realistic hardware greater depth exacerbates noise: The overall quality of results depends critically on both, variational parameters and circuit depth. Although identifying optimal parameters is NP-hard, prior work has suggested that they may exhibit regular, predictable patterns for increasingly deep circuits and depending on the studied class of problems. In this work, we systematically investigate the role of classical parameters in QAOA performance through extensive numerical simulations and suggest a simple, yet effective heuristic scheme to find good parameters for low-depth circuits. Our results demonstrate that: (i) optimal parameters often deviate substantially from expected patterns; (ii) QAOA performance becomes progressively less sensitive to specific parameter choices as depth increases; and (iii) iterative component-wise fixing performs on par with, and at shallow depth may even outperform, several established parameter-selection strategies. We identify conditions under which structured parameter patterns emerge, and when deviations from the patterns warrant further consideration. These insights for low-depth circuits may inform more robust pathways to harnessing QAOA in realistic quantum compute scenarios.</abstract>
    <identifier type="doi">10.48550/arXiv.2510.08153</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Vincent Eichenseher</author>
    <author>Maja Franz</author>
    <author>Christian Wolff</author>
    <author>Wolfgang Mauerer</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
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    <publishedYear>2024</publishedYear>
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    <language>eng</language>
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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>
    <enrichment key="opus.import.date">2025-01-24T17:34:31+00:00</enrichment>
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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>
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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>
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    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/7687/Franz_Mauerer_Hype_or_Heuristic_IEEE.pdf</file>
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  <doc>
    <id>3472</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
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    <language>eng</language>
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    <pageLast/>
    <pageNumber/>
    <edition>In Press, Corrected Proof</edition>
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    <type>article</type>
    <publisherName>Elsevier</publisherName>
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    <title language="eng">Uncovering Instabilities in Variational-Quantum Deep Q-Networks</title>
    <abstract language="eng">Deep Reinforcement Learning (RL) has considerably advanced over the past decade. At the same time, state-of-the-art RL algorithms require a large computational budget in terms of training time to converge. Recent work has started to approach this problem through the lens of quantum computing, which promises theoretical speed-ups for several traditionally hard tasks. In this work, we examine a class of hybrid quantumclassical RL algorithms that we collectively refer to as variational quantum deep Q-networks (VQ-DQN). We show that VQ-DQN approaches are subject to instabilities that cause the learned policy to diverge, study the extent to which this afflicts reproduciblity of established results based on classical simulation, and perform systematic experiments to identify potential explanations for the observed instabilities. Additionally, and in contrast to most existing work on quantum reinforcement learning, we execute RL algorithms on an actual quantum processing unit (an IBM Quantum Device) and investigate differences in behaviour between simulated and physical quantum systems that suffer from implementation deficiencies. Our experiments show that, contrary to opposite claims in the literature, it cannot be conclusively decided if known quantum approaches, even if simulated without physical imperfections, can provide an advantage as compared to classical approaches. Finally, we provide a robust, universal and well-tested implementation of VQ-DQN as a reproducible testbed for future experiments.</abstract>
    <parentTitle language="eng">Journal of the Franklin Institute</parentTitle>
    <identifier type="issn">0016-0032</identifier>
    <identifier type="doi">10.1016/j.jfranklin.2022.08.021</identifier>
    <note>Corresponding author: Maja Franz</note>
    <enrichment key="opus.import.date">2022-04-10T09:34:41+00:00</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>Lucas Wolf</author>
    <author>Maniraman Periyasamy</author>
    <author>Christian Ufrecht</author>
    <author>Daniel D. Scherer</author>
    <author>Axel Plinge</author>
    <author>Christopher Mutschler</author>
    <author>Wolfgang Mauerer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Computer Science</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Quantum Physics</value>
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    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
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    <collection role="oaweg" number="">Corresponding author der OTH Regensburg</collection>
    <collection role="institutes" number="">Labor für Digitalisierung (LFD)</collection>
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    <publisherName>IEEE</publisherName>
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    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <enrichment key="opus.import.date">2022-04-10T09:34:41+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>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>
    </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>
  </doc>
</export-example>
