Prof. Dr.

Hauswirth, Manfred

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  • Publication
    Extracting GHZ states from linear cluster states
    ( 2024)
    Jong, Dirk J. de
    ;
    Hahn, F.
    ;
    Tcholtchev, Nikolay Vassilev
    ;
    Hauswirth, Manfred
    ;
    Pappa, Anna
    Quantum information processing architectures typically only allow for nearest-neighbor entanglement creation. In many cases, this prevents the direct generation of GHZ states, which are commonly used for many communication and computation tasks. Here, we show how to obtain GHZ states between nodes in a network that are connected in a straight line, naturally allowing them to initially share linear cluster states. We prove a strict upper bound of ⌊(n+3)/2⌋ on the size of the set of nodes sharing a GHZ state that can be obtained from a linear cluster state of n qubits, using local Clifford unitaries, local Pauli measurements, and classical communication. Furthermore, we completely characterize all selections of nodes below this threshold that can share a GHZ state obtained within this setting. Finally, we demonstrate these transformations on the IBMQ Montreal quantum device for linear cluster states of up to n=19 qubits.
  • Publication
    Design and development of a short-term photovoltaic power output forecasting method based on Random Forest, Deep Neural Network and LSTM using readily available weather features
    ( 2023)
    Rangelov, Denis
    ;
    Boerger, Michell
    ;
    Tcholtchev, Nikolay Vassilev
    ;
    Lämmel, Philipp
    ;
    Hauswirth, Manfred
    Renewable energy sources (RES) are an essential part of building a more sustainable future, with higher diversity of clean energy, reduced emissions and less dependence on finite fossil fuels such as coal, oil and natural gas. The advancements in the renewable energy sources domain bring higher hardware efficiency and lower costs, which improves the likelihood of wider RES adoption. However, integrating renewables such as photovoltaic (PV) systems in the current grid is still a major challenge. The main reason is the volatile, intermittent nature of RES, which increases the complexity of the grid management and maintenance. Having access to accurate PV power output forecasting could reduce the number of power supply disruptions, improve the planning of the available and reserve capacities and decrease the management and operational costs. In this context, this paper explores and evaluates three Artificial Intelligence (AI) methods - random forest (RF), deep neural network (DNN) and long short-term memory network (LSTM), which are applied for the task of short-term PV output power forecasting. Following a statistical forecasting approach, the selected models are trained on weather and PV output data collected in Berlin, Germany. The assembled data set contains predominantly broadly accessible weather features, which makes the proposed approach more cost efficient and easily applicable even for geographic locations without access to specialized hardware or hard-to-obtain input features. The performance achieved by two of the selected algorithms indicates that the RF and the DNN models are able to generate accurate solar power forecasts and are also able to handle sudden changes and shifts in the PV power output.
  • Publication
    Open 5G campus networks: key drivers for 6G innovations
    ( 2022)
    Emmelmann, Marc
    ;
    Corici, Marius-Iulian
    ;
    Eichhorn, Fabian
    ;
    Hauswirth, Manfred
    ;
    Magedanz, Thomas
    5G was designed to enable and unify Industrial Internet communication. Emerging 5G campus networks, in particular, provide a flexible communication infrastructure option addressing the specific needs of industry verticals regarding low latency, resilience, security, and operation models. Network Function Virtualization (NFV) and Edge Computing have paved the way for vendor-independent, customized, and scalable network designs for the past decade. Today, Open Radio Access Network (Open RAN) principles extend this architectural thinking toward an innovative and open 5G end-to-end infrastructure. 5G campus networks, in particular, might benefit from this envisaged openness. One key driver for boosting the global interest in private campus networks was the allocation of a dedicated 5G spectrum in Germany in 2019. In addition to permanent spectrum allocations for static campus network deployments, nomadic ad hoc campus network deployments using novel mechanisms, such as dynamic spectrum access and trading, also emerge. Both network types are enabled by the inherent flexibility of combining or disaggregating the desired open 5G RAN and core components in appropriate network deployments. Building upon years of experience in developing and operating 5G network cores and 5G testbeds, the authors provide an overview of the emerging global campus network market, available spectrum options, use cases for nomadic campus network deployments, and the need for open campus networks and open end-to-end technology testbeds. Utilizing the Fraunhofer FOKUS Open5GCore, the 5G Playground testbed, and the 5G+ Nomadic Node as examples, the paper sketches a blueprint for campus networks for international, applied research and development. Ending with an outlook on the evolution of campus networks, namely the transition toward higher spectrums and the integration of non-terrestrial networks, but also the adoption of more agile software principles and the deeper integration of AI/ML technologies for network control and management, it will become obvious that open campus network innovations will pave the way toward 6G.