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
    Automatic generation of Grover quantum oracles for arbitrary data structures
    ( 2023)
    Seidel, Raphael
    ;
    Becker, Colin Kai-Uwe
    ;
    Bock, Sebastian
    ;
    Tcholtchev, Nikolay Vassilev
    ;
    Gheorghe-Pop, Ilie-Daniel
    ;
    Hauswirth, Manfred
    The steadily growing research interest in quantum computing-together with the accompanying technological advances in the realization of quantum hardware-fuels the development of meaningful real-world applications, as well as implementations for well-known quantum algorithms. One of the most prominent examples till today is Grover’s algorithm, which can be used for efficient search in unstructured databases. Quantum oracles that are frequently masked as black boxes play an important role in Grover’s algorithm. Hence, the automatic generation of oracles is of paramount importance. Moreover, the automatic generation of the corresponding circuits for a Grover quantum oracle is deeply linked to the synthesis of reversible quantum logic, which-despite numerous advances in the field-still remains a challenge till today in terms of synthesizing efficient and scalable circuits for complex Boolean functions. In this paper, we present a flexible method for automatically encoding unstructured databases into oracles, which can then be efficiently searched with Grover’s algorithm. Furthermore, we develop a tailor-made method for quantum logic synthesis, which vastly improves circuit complexity over other current approaches. Finally, we present another logic synthesis method that considers the requirements of scaling onto real world backends. We compare our method with other approaches through evaluating the oracle generation for random databases and analyzing the resulting circuit complexities using various metrics.
  • 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
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    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
    Enabling short-term energy flexibility markets through Blockchain
    ( 2022)
    Boerger, Michell
    ;
    Lämmel, Philipp
    ;
    Tcholtchev, Nikolay Vassilev
    ;
    Hauswirth, Manfred
    Climate change has put significant pressure on energy markets. Political decisions such as the plan of the German government to shut down coal power plants by 2038 are shifting electricity production towards renewable and distributed energy resources. The share of these resources will continue to grow significantly in the coming years. This trend changes the ways how energy markets work which mandates fundamental changes in the underlying IT infrastructure. In this paper, we propose a blockchain-based solution which enables an economically viable and grid-serving integration of distributed energy resources into the existing energy system. Our blockchain-based approach targets intraday and day-ahead operating reserve markets, on which energy grid operators and operators of distributed energy resources can trade flexibilities within the schedulable energy production and consumption of their resources. By utilizing these flexibilities as an operating reserve, renewable and climate-friendly technologies can contribute to maintaining the grid stability and security of supply while simultaneously creating economically interesting business models for their operators. We propose to define blockchain-based short-term energy markets by utilizing the concept of general-purpose smart contracts and cryptocurrencies. This enables direct and decentralized trading of energy flexibilities without any intermediary or central instance. We demonstrate the feasibility of our approach through an implementation of a prototype of the proposed markets based on the Ethereum blockchain and provide a detailed evaluation of its efficiency and scalability.
  • Publication
    Design and specification of a privacy-preserving registration for Blockchain-based energy markets
    ( 2022)
    Boerger, Michell
    ;
    Lämmel, Philipp
    ;
    Tcholtchev, Nikolay Vassilev
    ;
    Hauswirth, Manfred
    The challenges of climate change and the related demand to integrate non-plannable and weather-dependent renewable energy resources pose enormous challenges for the entire energy domain, e.g. in the context of grid control. These challenges reveal the need for new technical solutions and new business models while they indicate the required and inevitable transition to smart grids. Many blockchain-based solutions are being discussed in this context, ranging from peer-to-peer energy trading to grid-serving applications. However, especially in connection with public blockchains, clear security privacy challenges arise since the security and privacy of private data must be guaranteed while traceability must be avoided. Therefore, in this paper, we will specify privacy-protecting registration processes for blockchain-based flexibility markets that enable pseudonymous access to the latter. Furthermore, in collaboration with a governmental regulating institution named DGA, we will show that using an existing X.509-based PKI and RSA-based cryptographic processes, the integrity of all market participants can be guaranteed. This integrity is essential for the security-critical use of operating reserve. In addition, we will evaluate the specified processes in terms of efficiency, scalability, security, and privacy protection.
  • Publication
    Efficient Floating Point Arithmetic for Quantum Computers
    ( 2022)
    Seidel, Raphael
    ;
    Tcholtchev, Nikolay Vassilev
    ;
    Bock, Sebastian
    ;
    Becker, Colin Kai-Uwe
    ;
    Hauswirth, Manfred
    One of the major promises of quantum computing is the realization of SIMD (single instruction - multiple data) operations using the phenomenon of superposition. Since the dimension of the state space grows exponentially with the number of qubits, we can easily reach situations where we pay less than a single quantum gate per data point for data-processing instructions which would be rather expensive in classical computing. Formulating such instructions in terms of quantum gates, however, still remains a challenging task. Laying out the foundational functions for more advanced data-processing is therefore a subject of paramount importance for advancing the realm of quantum computing. In this paper, we introduce the formalism of encoding so called-semi-boolean polynomials. As it turns out, arithmetic Z/2nZ ring operations can be formulated as semi-boolean polynomial evaluations, which allows convenient generation of unsigned integer arithmetic quantum circuits. For arithmetic evaluations, the resulting algorithm has been known as Fourier-arithmetic. We extend this type of algorithm with additional features, such as ancilla-free in-place multiplication and integer coefficient polynomial evaluation. Furthermore, we introduce a tailormade method for encoding signed integers succeeded by an encoding for arbitrary floating-point numbers. This representation of floating-point numbers and their processing can be applied to any quantum algorithm that performs unsigned modular integer arithmetic. We discuss some further performance enhancements of the semi boolean polynomial encoder and finally supply a complexity estimation. The application of our methods to a 32-bit unsigned integer multiplication demonstrated a 90% circuit depth reduction compared to carry-ripple approaches.