Building quantum applications requires deep knowledge of quantum computing and software engineering. Hence, an abstraction layer reducing the complexity for non-experts is needed. Patterns are an established concept for the abstract description of proven solutions to recurring problems. Therefore, the quantum computing patterns, a pattern language for the quantum computing domain, can be used to define the building blocks and the structure of hybrid quantum applications. Furthermore, concrete software artifacts can be associated with patterns to solve the corresponding problem. However, these software artifacts are usually heterogeneous, e.g., using different data formats. Quantum workflows enable a robust and scalable orchestration of these heterogeneous software artifacts. However, manually modeling and configuring such quantum workflows is a complex, error-prone, and time-consuming task. To overcome this issue, we present an approach that automates the generation and adaptation of quantum workflows using the quantum computing patterns. We provide an architecture realizing our approach, a corresponding prototype, as well as an evaluation comprising different use cases, a runtime comparison, and a user study.

As quantum computers advance, the complexity of the software they can execute increases as well. To ensure this software is efficient, maintainable, reusable, and cost-effective—key qualities of any industry-grade software—mature software engineering practices must be applied throughout its design, development, and operation. However, the significant differences between classical and quantum software make it challenging to directly apply classical software engineering methods to quantum systems. This challenge has led to the emergence of Quantum Software Engineering (QSE) as a distinct field within the broader software engineering landscape. In this work, a group of active researchers analyze in depth the current state of QSE research. From this analysis, the key areas of QSE are identified and explored in order to determine the most relevant open challenges that should be addressed in the next years. These challenges help identify necessary breakthroughs and future research directions for advancing QSE.

Quantum computers can solve certain problems faster than classical computers. The number of quantum computers offered via the cloud increases such that a variety is accessible for the public. However, current quantum computers still suffer from a small number of qubits and high error rates. Selecting a quantum computer providing enough qubits and low error rates is tremendously important to receive stable execution results for a given quantum circuit. The execution results are also influenced by the compilation result of the selected quantum compiler for hardware mapping and optimization. For computing time, the access is regulated by vendors of quantum computers resulting in waiting times for the user. Thus, a deep and manifold analysis of quantum computers and compiled circuits of different quantum compilers is required to estimate in advance which compiled circuit will return stable execution results also in consideration of non-functional requirements such as waiting times. To address this, we present a framework that analyzes and prioritizes compiled circuits for different quantum computers using several compilers based on the requirements of the user. We show the practical feasibility of our framework by a prototype and two use cases.

Quantum applications are most often hybrid, i.e., they are not only made of implementations of pure quantum algorithms but also of classical programs as well as workflows and topologies as key artifacts, and data they process. Since workflows and topologies are referred to as “orchestrations” in modern terminology (but with very different meanings), two orchestrations that go hand-in-hand are required to realize quantum applications. We motivate this by means of a nontrivial example, sketch these orchestration technologies, and reveal the overall structure of non-trivial quantum applications. Furthermore, we discuss the implied architecture of a runtime environment for such quantum applications. To validate the introduced architecture, we present a prototypical implementation based on the Camunda workflow engine, its associated modeling tool, as well as the OpenTOSCA ecosystem.

Quantum computers are becoming real, and they have the inherent potential to significantly impact many application domains. We sketch the basics about programming quantum computers, showing that quantum programs are typically hybrid consisting of a mixture of classical parts and quantum parts. With the advent of quantum computers in the cloud, the cloud is a fine environment for performing quantum programs. The tool chain available for creating and running such programs is sketched. As an exemplary problem we discuss efforts to implement quantum programs that are hardware independent. A use case from machine learning is outlined. Finally, a collaborative platform for solving problems with quantum computers that is currently under construction is presented.