@inproceedings{SchmidbauerWinterspergerLobeetal., author = {Schmidbauer, Lukas and Wintersperger, Karen and Lobe, Elisabeth and Mauerer, Wolfgang}, title = {Polynomial Reduction Methods and their Impact on QAOA Circuits}, series = {IEEE International Conference on Quantum Software (QSW), 7-13 July 2024, Shenzhen, China}, booktitle = {IEEE International Conference on Quantum Software (QSW), 7-13 July 2024, Shenzhen, China}, doi = {10.1109/QSW62656.2024.00018}, pages = {35 -- 45}, abstract = {Abstraction layers are of paramount importance in software architecture, as they shield the higher-level formulation of payload computations from lower-level details. Since quantum computing (QC) introduces many such details that are often unaccustomed to computer scientists, an obvious desideratum is to devise appropriate abstraction layers for QC. For discrete optimisation, one such abstraction is to cast problems in quadratic unconstrained binary optimisation (QUBO) form, which is amenable to a variety of quantum approaches. However, different mathematically equivalent forms can lead to different behaviour on quantum hardware, ranging from ease of mapping onto qubits to performance scalability. In this work, we show how using higher-order problem formulations (that provide better expressivity in modelling optimisation tasks than plain QUBO formulations) and their automatic transformation into QUBO form can be used to leverage such differences to prioritise between different desired non-functional properties for quantum optimisation. Based on a practically relevant use-case and a graph-theoretic analysis, we evaluate how different transformation approaches influence widely used quantum performance metrics (circuit depth, gates count, gate distribution, qubit scaling), and also consider the classical computational efforts required to perform the transformations, as they influence possibilities for achieving future quantum advantage. Furthermore, we establish more general properties and invariants of the transformation methods. Our quantitative study shows that the approach allows us to satisfy different trade-offs, and suggests various possibilities for the future construction of general-purpose abstractions and automatic generation of useful quantum circuits from high-level problem descriptions.}, language = {en} } @incollection{CarbonelliFeldererJungetal., author = {Carbonelli, Cecilia and Felderer, Michael and Jung, Matthias and Lobe, Elisabeth and Lochau, Malte and Luber, Sebastian and Mauerer, Wolfgang and Ramler, Rudolf and Sch{\"a}fer, Ina and Schroth, Christoph}, title = {Challenges for Quantum Software Engineering: An Industrial Application Scenario Perspective}, series = {Quantum Software: Aspects of Theory and System Design}, booktitle = {Quantum Software: Aspects of Theory and System Design}, editor = {Exman, Iaakov and Perez-Castillo, Ricardo and Piattini, Mario and Felderer, Michael}, publisher = {Springer-Nature}, isbn = {978-3-031-64135-0}, doi = {10.1007/978-3-031-64136-7_12}, abstract = {Quantum software is becoming a key enabler for applying quantum computing to industrial use cases. This poses challenges to quantum software engineering in providing efficient and effective means to develop such software. Eventually, this must be reliably achieved in time, on budget, and in quality, using sound and well-principled engineering approaches. Given that quantum computers are based on fundamentally different principles than classical machines, this raises the question if, how, and to what extent established techniques for systematically engineering software need to be adapted. In this chapter, we analyze three paradigmatic application scenarios for quantum software engineering from an industrial perspective. The respective use cases center around (1) optimization and quantum cloud services, (2) quantum simulation, and (3) embedded quantum computing. Our aim is to provide a concise overview of the current and future applications of quantum computing in diverse industrial settings. We derive presumed challenges for quantum software engineering and thus provide research directions for this emerging field.}, language = {en} } @incollection{Mauerer, author = {Mauerer, Wolfgang}, title = {Superoperators for Quantum Software Engineering}, series = {Quantum Software: Aspects of Theory and System Design}, booktitle = {Quantum Software: Aspects of Theory and System Design}, editor = {Exman, Iaakov and Perez-Castillo, Ricardo and Piattini, Mario and Felderer, Michael}, publisher = {Springer Nature}, issn = {978-3-031-64136-7}, doi = {10.1007/978-3-031-64136-7_3}, abstract = {As implementations of quantum computers grow in size and maturity, the question of how to program this new class of machines is attracting increasing attention in the software engineering domain. Yet, many questions from how to design expressible quantum languages augmented with formal semantics via implementing appropriate optimizing compilers to abstracting details of machine properties in software systems remain challenging. Performing research at this intersection of quantum computing and software engineering requires sufficient knowledge of the physical processes underlying quantum computations, and how to model these. In this chapter, we review a superoperator-based approach to quantum dynamics, as it can provide means that are sufficiently abstract, yet concrete enough to be useful in quantum software and systems engineering, and outline how it is used in several important applications in the field.}, language = {en} } @inproceedings{GreiweKruegerMauerer, author = {Greiwe, Felix and Kr{\"u}ger, Tom and Mauerer, Wolfgang}, title = {Effects of Imperfections on Quantum Algorithms}, series = {2023 IEEE International Conference on Quantum Software (QSW), Chicago, IL, USA, 02-08 July 2023}, booktitle = {2023 IEEE International Conference on Quantum Software (QSW), Chicago, IL, USA, 02-08 July 2023}, publisher = {IEEE}, doi = {10.1109/QSW59989.2023.00014}, pages = {31 -- 42}, abstract = {Quantum computers promise considerable speedups over classical approaches, which has raised interest from many disciplines. Since any currently available implementations suffer from noise and imperfections, achieving concrete speedups for meaningful problem sizes remains a major challenge. Yet, imperfections and noise may remain present in quantum computing for a long while. Such limitations play no role in classical software computing, and software engineers are typically not well accustomed to considering such imperfections, albeit they substantially influence core properties of software and systems. In this paper, we show how to model imperfections with an approach tailored to (quantum) software engineers. We intuitively illustrate, using numerical simulations, how imperfections influence core properties of quantum algorithms on NISQ systems, and show possible options for tailoring future NISQ machines to improve system performance in a co-design approach. Our results are obtained from a software framework that we provide in form of an easy-to-use reproduction package. It does not require computer scientists to acquire deep physical knowledge on noise, yet provide tangible and intuitively accessible means of interpreting the influence of noise on common software quality and performance indicators.}, language = {en} }