Browsing by Author "Radic, Marco"

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    Quantum-enhanced machine learning in the NISQ era
    (2019) Radic, Marco
    Quantum computation technologies have reached a new level of sophistication with the release of the first commercial offerings. Likewise, Machine Learning is popular for use-cases in both industry and research. With Quantum Machine Learning, one hopes to combine both areas in a symbiotic relationship to achieve an advantage in artificial intelligence with the use of quantum technologies. Recently presented approaches make use of quantum technologies in combination with classical hardware resources in order to mitigate the problems imposed by shortcomings of quantum computers of the current generation. Some of these approaches use quantum circuits with free parameters, which are optimized to solve problems and objectives in Machine Learning. This work presents a concept for automated modelling of these quantum circuits, with the goal of constructing suitable circuits for the task of classification. The concept is implemented in a prototype and validated in experiments.
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    Transformation of TOSCA to natural language texts
    (2017) Radic, Marco
    Cloud computing changes the way businesses plan, use and manage their IT systems and resources. Different cloud providers offer distinctive interfaces for the deployment and management of applications in their respective cloud environments. The organization OASIS addresses these circumstances with the Topology and Orchestration Specification for Cloud Applications (TOSCA). This standard offers a language to express applications as directed graphs and their management behavior in a standardized and vendor-independent manner. In numerous roles in the development, a textual description of the application, its entities and their relationships, for instance to serve as textual documentation, is of use. The TOSCA standard places no restriction on the complexity of a topology graph. Therefore, a textual representation of the graph can also get arbitrarily large and complex. Additionally, every change has to be reflected in the documentation accordingly. Consequently, an automated approach to the generation of such textual representations is preferable. This work describes a concept for the automated generation of textual descriptions of TOSCA topology graphs. This is accomplished by combining typical tasks from natural language generation with domain-specific information in order to generate appropriate textual descriptions. The concept is implemented in a prototype and validated in a use-case scenario.