Now showing 1 - 10 of 74
  • Publication
    Extracting GHZ states from linear cluster states
    ( 2024)
    Jong, Dirk J. de
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    Hahn, F.
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    Tcholtchev, Nikolay Vassilev
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    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
    Extracting GHZ states from linear cluster states
    ( 2023-11)
    Jong, J. de
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    Hahn, F.
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    Tcholtchev, Nikolay Vassilev
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    ;
    Pappa, Anna
    Quantum information processing architectures typically only allow for nearest-neighbour entanglement creation. In many cases, this prevents the direct generation of states, which are commonly used for many communication and computation tasks. Here, we show how to obtain 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 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 state obtained within this setting. Finally, we demonstrate these transformations on the quantum device for linear cluster states of up to n=19 qubits.
  • Publication
    VisionKG: Unleashing the Power of Visual Datasets via Knowledge Graph
    ( 2023-09)
    Yuan, Jicheng
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    Le-Tuan, Anh
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    Nguyen-Duc, Manh
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    Tran, Trung-Kien
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    Phuoc, Danh Le
    The availability of vast amounts of visual data with heterogeneous features is a key factor for developing, testing, and benchmarking of new computer vision (CV) algorithms and architectures. Most visual datasets are created and curated for specific tasks or with limited image data distribution for very specific situations, and there is no unified approach to manage and access them across diverse sources, tasks, and taxonomies. This not only creates unnecessary overheads when building robust visual recognition systems, but also introduces biases into learning systems and limits the capabilities of data-centric AI. To address these problems, we propose the Vision Knowledge Graph (VisionKG), a novel resource that interlinks, organizes and manages visual datasets via knowledge graphs and Semantic Web technologies. It can serve as a unified framework facilitating simple access and querying of state-of-the-art visual datasets, regardless of their heterogeneous formats and taxonomies. One of the key differences between our approach and existing methods is that ours is knowledge-based rather than metadatabased. It enhances the enrichment of the semantics at both image and instance levels and offers various data retrieval and exploratory services via SPARQL. VisionKG currently contains 519 million RDF triples that describe approximately 40 million entities, and are accessible at https://vision.semkg.org and through APIs. With the integration of 30 datasets and four popular CV tasks, we demonstrate its usefulness across various scenarios when working with CV pipelines.
  • 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
    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
    Towards a decentralized data hub and query system for federated dynamic data spaces
    ( 2023)
    Phuoc, Danh Le
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    Le-Tuan, Anh
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    Kuehn, Uwe A.
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    This position paper proposes a hybrid architecture for secure and efficient data sharing and processing across dynamic data spaces. On the one hand, current centralized approaches are plagued by issues such as lack of privacy and control for users, high costs, and bad performance, making these approaches unsuitable for the decentralized data spaces prevalent in Europe and various industries (decentralized on the conceptual and physical levels while centralized in the underlying implementation). On the other hand, decentralized systems face challenges with limited knowledge of/control over the global system, fair resource utilization, and data provenance. Our proposed Semantic Data Ledger (SDL) approach combines the advantages of both architectures to overcome their limitations. SDL allows users to choose the best combination of centralized and decentralized features, providing a decentralized infrastructure for the publication of structured data with machine-readable semantics. It supports expressive structured queries, secure data sharing, and payment mechanisms based on an underlying autonomous ledger, enabling the implementation of economic models and fair-use strategies.
  • Publication
    Automatic generation of Grover quantum oracles for arbitrary data structures
    ( 2023)
    Seidel, Raphael
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    Tcholtchev, Nikolay Vassilev
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    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
    Uncomputation in the Qrisp High-Level Quantum Programming Framework
    ( 2023)
    Seidel, Raphael
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    Tcholtchev, Nikolay Vassilev
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    Uncomputation is an essential part of reversible computing and plays a vital role in quantum computing. Using this technique, memory resources can be safely deallocated without performing a non-reversible deletion process. For the case of quantum computing, several algorithms depend on this as they require disentangled states in the course of their execution. Thus, uncomputation is not only about resource management, but is also required from an algorithmic point of view. However, synthesizing uncomputation circuits is tedious and can be automated. In this paper, we describe the interface for automated generation of uncomputation circuits in our Qrisp framework. Our algorithm for synthesizing uncomputation circuits in Qrisp is based on an improved version of “Unqomp”, a solution presented by Paradis et al. Our paper also presents some improvements to the original algorithm, in order to make it suitable for the needs of a high-level programming framework. Qrisp itself is a fully compilable, high-level programming language/framework for gate-based quantum computers, which abstracts from many of the underlying hardware details. Qrisp’s goal is to support a high-level programming paradigm as known from classical software development.
  • Publication
    A decentralised persistent identification layer for DCAT datasets
    The Data Catalogue Vocabulary (DCAT) standard is a popular RDF vocabulary for publishing metadata about data catalogs and a valuable foundation for creating Knowledge Graphs. It has widespread application in the (Linked) Open Data and scientific communities. However, DCAT does not specify a robust mechanism to create and maintain persistent identifiers for the datasets. It relies on Internationalized Resource Identifiers (IRIs), that are not necessarily unique, resolvable and persistent. This impedes findability, citation abilities, and traceability of derived and aggregated data artifacts. As a remedy, we propose a decentralized identifier registry where persistent identifiers are managed by a set of collaborative distributed nodes. Every node gives full access to all identifiers, since an unambiguous state is shared across all nodes. This facilitates a common view on the identifiers without the need for a (virtually) centralized directory. To support this architecture, we propose a data model and network methodology based on a distributed ledger and the W3C recommendation for Decentralized Identifiers (DID). We implemented our approach as a working prototype on a five-peer test network based on Hyperledger Fabric.
  • Publication
    Design and specification of a privacy-preserving registration for Blockchain-based energy markets
    ( 2022) ; ;
    Tcholtchev, Nikolay Vassilev
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    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
    Computer Scientist's and Programmer's View on Quantum Algorithms: Mapping Functions' APIs and Inputs to Oracles
    ( 2022) ;
    Tcholtchev, Nikolay Vassilev
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    Quantum Computing (QC) is a promising approach which is expected to boost the development of new services and applications. Specific addressable problems can be tackled through acceleration in computational time and advances with respect to the complexity of the problems, for which QC algorithms can support the solution search. However, QC currently remains a domain that is strongly dominated by a physics' perspective. Indeed, in order to bring QC to industrial grade applications we need to consider multiple perspectives, especially the one of software engineering and software application/service programming. Following this line of thought, the current paper presents our computer scientist's view on the aspect of black-box oracles, which are a key construct for the majority of currently available QC algorithms. Thereby, we observe the need for the input of API functions from the traditional world of software engineering and (web-)services to be mapped to the above mentioned black-box oracles. Hence, there is a clear requirement for automatically generating oracles for specific types of problems/algorithms based on the concrete input to the belonging APIs. In this paper, we discuss the above aspects and illustrate them on two QC algorithms, namely Deutsch-Jozsa and the Grover's algorithm.