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Shor’s algorithm for prime factorization is a hybrid algorithm consisting of a quantum part and a classical part. The main focus of the classical part is a continued fraction analysis. The presentation of this is often short, pointing to text books on number theory. In this contribution, we present the relevant results and proofs from the theory of continued fractions in detail (even in more detail than in text books), filling the gap to allow a complete comprehension of Shor’s algorithm. Similarly, we provide a detailed computation of the estimation of the probability that convergents will provide the period required for determining a prime factor.

Abstract As quantum computers are based on the laws of quantum mechanics, they are capable of solving certain problems faster than their classical counterparts. However, quantum algorithms with a theoretical speed‐up often assume that data can be loaded efficiently. In general, the runtime complexity of the loading routine depends on (i) the data encoding that defines how the data is represented by the state of the quantum computer and (ii) the data itself. In some cases, loading the data requires at least exponential time that destroys a potential speed‐up. And especially for the first generation of devices that are currently available, the resources (qubits and operations) needed to encode the data are limited. In this work, we, therefore, present six patterns that describe how data is handled by quantum computers.

Abstract Quantum computing promises breakthroughs in various application areas, such as machine learning, chemistry, or simulations. However, today’s quantum computers are error‐prone and have limited capabilities. This leads to various challenges when developing and executing quantum algorithms, for example, the mitigation of occurring errors or the selection of a suitable quantum computer to execute a certain quantum circuit. To address these challenges, detailed information about the quantum circuit to be executed as well as past executions, and the up‐to‐date information about the available quantum computers are required. Thus, this data must be continuously collected and stored in the long‐term, which is currently not supported. To overcome this problem, a provenance approach is introduced for quantum computing. Therefore, relevant provenance attributes that should be gathered in the area of quantum computing are identified. Furthermore, QProv, a provenance system that automatically collects the identified provenance attributes and provides them in a uniform manner to the user is introduced. Finally, a case study with the collected provenance data and corresponding use cases that can benefit from this provenance data are presented here.

Film costumes as diegetic design elements are only rudimentarily explored. There is especially a lack of an overarching concept to make costumes describable in a systematic way and thus analysable beyond individual film analyses. The MUSE dataset aims to fill this gap by providing detailed data on film costumes embedded in a comprehensive ontology. The costume data set contains both, data on the pieces of clothes that compose the costume, their arrangement, and their attributes such as colour, materials, and condition, as well as contextualizing data on the occurrence of the costumes in the film and the role wearing the costume.

For automating the deployment of composite applications, typically, declarative deployment models are used. Depending on the context, the deployment of an application has to fulfill different requirements, such as costs and elasticity. As a consequence, one and the same application, i.e., its components, and their dependencies, often need to be deployed in different variants. If each different variant of a deployment is described using an individual deployment model, it quickly results in a large number of models, which are error prone to maintain. Deployment technologies, such as Terraform or Ansible, support conditional components and dependencies which allow modeling different deployment variants of a composite application in a single deployment model. However, there are deployment technologies, such as TOSCA and Docker Compose, which do not support such conditional elements. To address this, we extend the Essential Deployment Metamodel (EDMM) by conditional components and dependencies. EDMM is a declarative deployment model which can be mapped to several deployment technologies including Terraform, Ansible, TOSCA, and Docker Compose. Preprocessing such an extended model, i.e., conditional elements are evaluated and either preserved or removed, generates an EDMM conform model. As a result, conditional elements can be integrated on top of existing deployment technologies that are unaware of such concepts. We evaluate this by implementing a preprocessor for TOSCA, called OpenTOSCA Vintner, which employs the open-source TOSCA orchestrators xOpera and Unfurl to execute the generated TOSCA conform models.

Cloud-based applications require a high degree of automation regarding their IT resource management, for example, to handle scalability or resource failures. This automation is enabled by cloud providers offering management interfaces accessed by applications without human interaction. The properties of clouds, especially pay-per-use billing and low availability of individual resources, demand such a timely system management. We call the automated steps to perform one of these management tasks a “management flow”. Because the emerging behavior of the overall system is comprised of many such management flows and is often hard to predict, we propose defining abstract management flows, describing common steps handling the management tasks. These abstract management flows may then be refined for each individual use case. We cover abstract management flows describing how to make an application elastic, resilient regarding IT resource failure, and how to move application components between different runtime environments. The requirements of these management flows for handled applications are expressed using architectural patterns that have to be implemented by the applications. These dependencies result in abstract management flows being interrelated with architectural patterns in a uniform pattern catalog. We propose a method by use of a catalog to guide application managers during the refinement of abstract management flows at the design stage of an application. Following this method, runtime-specific management functionality and management interfaces are used to obtain automated management flows for a developed application.