P326 - INFORMATIK 2022 - Informatik in den Naturwissenschaften

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    Influence Estimation In Multi-Step Process Chains Using Quantum Bayesian Networks
    (INFORMATIK 2022, 2022) Selch,Maximilian; Müssig,Daniel; Hänel,Albrecht; Lässig,Jörg; Ihlenfeldt,Steffen; Demmler, Daniel; Krupka, Daniel; Federrath, Hannes
    Digital representatives of physical assets and process steps play a decisive role in analysing properties and evaluating the quality of the process. So-called digital twins acquire all relevant planning and process data, which provide the basis, for example, to investigate path accuracies in manufacturing. Each single process step aims to perform an ideal machining after the specification of a target geometry. However, the practical implementation of a step usually shows deviations from the targeted shape. The machine-learning based method of probabilistic Bayesian networks enables the quality estimation of the holistic process chain as well as improvements by targeted considerations of single steps and influence factors. However, the handling of large-scale Bayesian networks requires a high computational effort, whereas the processing with quantum algorithms holds potential improvements in storage and performance. Based on the issue of path accuracy, this paper considers the modelling and influence estimation for a milling operation including experiments on superconducting quantum hardware.
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    A new Pattern for Quantum Evolutionary Algorithms
    (INFORMATIK 2022, 2022) Reers,Volker; Lässig,Jörg; Demmler, Daniel; Krupka, Daniel; Federrath, Hannes
    Quantum Evolutionary Algorithms have been discussed in the literature in different forms. One branch in these efforts studies approaches for the representation of genetic information, i.e., problem information, in terms of qubits. A typical downside of this representation has been the loss of quantum information in the evaluation and selection steps of the algorithm. I.e., algorithms are implemented in a hybrid-classical setup and require measurements in each iteration. This inevitably destroys superpositions and entanglement structures in the genome representation. In this work, we propose a new implementation approach for genetic information and the evaluation and selection phase, which realizes those steps within the quantum circuit. To achieve this, we utilize qudits for representing the evolving entities. Additionally, we make use of patterns for the design of quantum sub-circuits to compose control structures known from the classical realm. As a result, we show a quantum circuit design for anytime algorithms that does not have to be measured in every iteration and that does not depend on classical control. The overall progress of the evolutionary process only needs to be checked occasionally on a flag-qubit. The approach currently comes with some limitations e.g., in the objective function. It is presented here for the toy problem Leading Ones.
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    Real-world application benchmark for QAOA algorithm for an electromobility use case
    (INFORMATIK 2022, 2022) Federer,Marika; Müssig,Daniel; Lenk,Steve; Lässig,Jörg; Demmler, Daniel; Krupka, Daniel; Federrath, Hannes
    To reduce $CO_2$ emissions in the mobility sector, battery electric service vehicles might play an important role in the future. Here, an optimal charging scheduling use case will be presented which includes local solar power generation for minimizing the power grid usage for electric service vehicles. Different formulations of the use case are given to illustrate the differences for classical and quantum-based optimization using a mixed integer linear program and a quadratic unconstrained binary optimization program, respectively. Addtionally, we study the complexity of our benchmark experiments by characterizing the respective QUBO matrices and the optimization landscapes. It is shown how the setting of the parameters of a certain experiment and its penalty function influences the complexity for a quantum-based optimizer. Additionally, we present a comparison of the computing times and summarize the current state of gate-based quantum computing for electromobility.
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    Implementations for Shor's algorithm for the DLP
    (INFORMATIK 2022, 2022) Mandl,Alexander; Egly,Uwe; Demmler, Daniel; Krupka, Daniel; Federrath, Hannes
    Shor's algorithm for solving the discrete logarithm problem is one of the most celebrated works in quantum computing. It builds upon a quantum circuit performing modular exponentiation. As this is a comparatively expensive process, many approaches for reducing both the number of used qubits and the number of applied gate operations have been proposed. We provide quantum circuits in Qiskit for three different implementation proposals aiming to reduce space complexity and compare their performance regarding their asymptotic gate complexity. We make use of the circuit implementations and Qiskit’s simulation capabilities to compare the actual number of applied gate operations in compiled circuits for small problem instances to aid future applications of this algorithm.
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    Towards Model-Driven Engineering for Quantum AI
    (INFORMATIK 2022, 2022) Moin,Armin; Challenger,Moharram; Badii,Atta; Günnemann,Stephan; Demmler, Daniel; Krupka, Daniel; Federrath, Hannes
    Over the past decade, Artificial Intelligence (AI) has provided enormous new possibilities and opportunities, but also new demands and requirements for software systems. In particular, Machine Learning (ML) has proven useful in almost every vertical application domain. In the decade ahead, an unprecedented paradigm shift from classical computing towards Quantum Computing (QC), with perhaps a quantum-classical hybrid model, is expected. We argue that the Model-Driven Engineering (MDE) paradigm can be an enabler and a facilitator, when it comes to the quantum and the quantum-classical hybrid applications. This includes not only automated code generation, but also automated model checking and verification, as well as model analysis in the early design phases, and model-to-model transformations both at the design-time and at the runtime. In this paper, the vision is focused on MDE for Quantum AI, particularly Quantum ML for the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS) applications.
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    Geodaten als Open Data für die Künstliche Intelligenz
    (INFORMATIK 2022, 2022) Hoffmann,Anna; Kahle,Reinhard; Demmler, Daniel; Krupka, Daniel; Federrath, Hannes
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    Mapping Quantum Circuits to 2-Dimensional Quantum Architectures
    (INFORMATIK 2022, 2022) Datta,Kamalika; Kole,Abhoy; Sengupta,Indranil; Drechsler,Rolf; Demmler, Daniel; Krupka, Daniel; Federrath, Hannes
    We have been witnessing a rapid growth in quantum computing research over the years, with the emergence of demonstrable quantum computers of moderate size. The major issues that are faced to run a quantum algorithm reliably on these systems are: (i) lower qubit coherence period, (ii) noisy primitive gate operations, (iii) limited number of available physical qubits, and (iv) support of restricted set of 2-qubit operations. Overcoming these issues mandates physical resources that exceeds the capabilities of these noisy intermediate scale quantum (NISQ) systems. On the other hand, computation using bare qubits get further disturbed due to the inclusion of additional gates to mitigate the nearest neighbor constraints. In the present work, the 2-dimensional square, heavy-hex and fully hexagonal qubit coupling lattices are considered for mapping quantum circuits. The benefits are assessed in terms of minimal additional gates needed to satisfy the nearest neighbor (NN) constraint and the compilation complexity of mapping circuits on these architectures. From the experiments by mapping benchmark circuits on 16-qubit square and 65-qubit heavy-hex architectures from IBM as well as on a 64-qubit fully hexagonal architecture, it is observed that none of the square or heavy-hex lattice architecture provides uniform compilation advantage compared to the fully hexagonal architecture. It is expected that beyond the NISQ era, strongly connected lattices like hexagonal will become practically feasible.
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    Prescriptive and descriptive quality metrics for the quality assessment of operational data
    (INFORMATIK 2022, 2022) Viedt,Isabell; Mädler,Jonathan; Khaydarov,Valentin; Urbas,Leon; Demmler, Daniel; Krupka, Daniel; Federrath, Hannes
    In the process industry data-driven and hybrid modeling approaches are increasingly popular in regards to process monitoring, optimization and control. The major problem with process data is that the data collected in process plants during operation, even though available in vast amounts, might generally be low in information content. The collected data usually represents certain operating points while anomalies, ramp-up and shut-down are rare occurrences and therefore only seldom covered. Due to its possibly low quality, the use of such data might lead to an inadequate model coverage and overall low model performance. Data quality assessment prior to modeling is crucial to allow an estimation of model quality prior to the model development. Therefore, the following paper discusses prescriptive and descriptive assessment metrics for the quality assessment of process data and their potential application in the quality assurance of data-driven and hybrid models. This approach will in later application support the user in their choice of modeling approach.
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    Workshop zum Einsatz agiler Methoden im Informatikunterricht
    (INFORMATIK 2022, 2022) Bahr,Tobias; Zinn,Bernd; Demmler, Daniel; Krupka, Daniel; Federrath, Hannes
    In diesem Bericht zum Workshop für Informatiklehrpersonen wird eine Unterrichtseinheit zum Themenbereich Algorithmen der Klassenstufe 8 für ein Programmierprojekt mit Scratch vorgestellt. Die teilnehmenden Informatiklehrpersonen sollen in dem Workshop die Grundlagen des agilen Prozesses lernen und reflektieren unterrichtsrelevante Aspekte, indem diese die Schülerperspektive einnehmen sowie strukturgebend die Vor- und Nachteile zum Einsatz von agilen Methoden im Informatikunterricht diskutieren. Abschließend werden Erkenntnisse aus der Unterrichtspraxis vorgestellt sowie methodisch-didaktische Aspekte zur Umsetzung von Agilen Methoden diskutiert.
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    Teilautomatisierte Datenqualitätsbewertung und Fehlerkorrektur zur Senkung der Einstiegshürde von Datenanalysemethoden
    (INFORMATIK 2022, 2022) Schlunder,Philipp; Demmler, Daniel; Krupka, Daniel; Federrath, Hannes
    Zur Nutzung aktueller Methoden der Datenanalyse müssen Daten häufig zunächst aufbereitet werden. Hierzu werden Kenntnisse benötigt, die in viele Organisationen noch nicht vorliegen. Um diese Einstiegshürde zu verringern, wird ein Konzept zur interaktiven, teilautomatisierten Behebung von Datenqualitätsproblemen vorgestellt, dessen Anwendung weniger Vorkenntnisse bedarf, indem eine automatische Bewertung der Datenqualität und des Informationsgehalts bereitgestellt wird, mit der Option, erkannte Probleme durch verschiedene Ansätze direkt korrigieren zu lassen.