http://dl.gi.de/handle/20.500.12116/19085 2026-07-21T13:23:12Z http://dl.gi.de/handle/20.500.12116/39604 Influence Estimation In Multi-Step Process Chains Using Quantum Bayesian Networks 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. 2022-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/39603 A new Pattern for Quantum Evolutionary Algorithms 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. 2022-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/39602 Real-world application benchmark for QAOA algorithm for an electromobility use case 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. 2022-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/39601 Implementations for Shor's algorithm for the DLP 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. 2022-01-01T00:00:00Z