Browsing by Author "Weber, Jan"

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    An approach for benchmarking quantum computers to determine the executability of quantum circuits
    (2021) Weber, Jan
    Quantum computers in this day and age are characterized by high error rates and their limited amount of qubits. This introduces errors to the execution of quantum circuits. Consequently, quantum computers currently cannot be expected to run arbitrary circuits successfully. In this context, the size of a circuit heavily influences the outcome of the execution, as large circuits are prone to errors. The size of a circuit is defined by w*d where w is its width and d its depth. Metrics can be used to judge the computational power of quantum computers and allow predictions on whether a circuit is expected to run successfully or not. In this thesis, gate-based quantum computers were benchmarked by executing randomized circuits and comparing the results to the quantum simulator’s result. Four different metrics were considered to evaluate whether the quantum computer’s result is too erroneous to consider the benchmark successful or not. After comparing the metrics and discussing possibilities as to how they can be used to evaluate benchmarks, it was decided that the histogram intersection is the most appropriate to use. Using this metric, it is possible to benchmark quantum computers with randomized circuits of different sizes, evaluate the results and use that data to find upper limits on the circuit size. The data in this thesis suggests that, for IBM’s quantum computer imbq_athens, circuits of size w*d <= 20 are expected to return acceptable results while circuits of width equal to 4 or 5 deliver acceptable results for even larger circuits (up to w*d = 40). The framework provided in this thesis is the foundation to determine the metric w*d < k*1/epsilon_eff which will be used in the automated selection of an appropriate quantum computer for a given quantum circuit.
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    Assisted data migrations for MUSE4Anything
    (2025) Weber, Jan
    Maintaining data in large repositories requires a lot of effort. Type schemas change over time due to changing requirements or mistakes in the initial abstraction process. When these changes happen, objects of that type need to be updated as well to ensure they conform to the new schema. Otherwise, a program using the data would have to treat every object differently, depending on the schema version. The transformation of all objects to conform to a new schema is called data migration. MUSE (Muster Suchen und Erkennen, eng.: Search and Recognize Patterns) and MUSE4Music are specific repositories for costumes and classical music. These repositories are used to prepare categorical data for automatic analyses. MUSE4Anything, successor of these implementations, is a generic data repository that allows managing domain specific data conforming to user-defined ontologies. In this environment, defined types and their requirements may also change over time. While type and object modifications are already supported in MUSE4Anything, there is no implementation of automatic migration of objects after a type update yet. This thesis aims to extend the MUSE4Anything system with a migration engine that performs these processes. By automating data migration, the required manual effort is significantly reduced, and the user experience, especially for huge repositories, is improved.