- author = "Felix Guenther Gemeinhardt",
- title = "A Model-Driven Quantum Software Development Framework for Combinatorial Optimization",
- school = "Institut fuer Wirtschaftsinformatik (Software Engineering), Faculty of Social Sciences, Economics and Business, University Linz",
- year = "2023",
- address = "Austria",
- keywords = "genetic algorithms, genetic programming, Quantencomputing, modellgetriebene Softwareentwicklung, suchbasierte Softwareentwicklung, Quantum computing, model-driven engineering, search-based software engineering, SBSE",
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URL = "
https://epub.jku.at/obvulihs/content/titleinfo/9621944",
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URL = "https://permalink.obvsg.at/ULI/AC17094889",
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URL = "https://epub.jku.at/obvulihs/download/pdf/9621944",
- size = "245 pages",
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abstract = "Context: Quantum computers harness quantum mechanical
effects to process information, i.e., they rely on
fundamentally different physical principles compared to
classical computers. This raises the need for novel
software to optimally exploit the potential of quantum
devices, giving birth to the emerging field of quantum
software engineering. Here, the goal is to transfer
concepts developed over decades from classical software
engineering to the quantum realm.
Problem: An identified key problem lies in the low levels of abstraction and automation in current quantum software engineering. This causes several practical problems for the user, e.g., quantum programming language-specific development and limited reusability, as well as the neglect of present trade-offs between different quantum program variants. These problems hold true for quantum computing in general, but also for the field of quantum combinatorial optimization. The latter refers to the application of quantum computing to solve combinatorial optimization problems. As quantum combinatorial optimization is considered a near-term application area of quantum computing, the illustrated problem is rendered even more urgent in this context.
Solution: The solution proposed within this dissertation is a quantum software development framework that directly tackles these problems by raising abstraction and automation levels using methods from model-driven engineering and search-based software engineering. In particular, the solution framework advocates the use of higher-level composite quantum operations and comprises features to model, automatically synthesize, and automatically improve such quantum software.
Research Method: This dissertation is framed as a design science project and follows established guidelines for design science research. Thus, within a first pilot project, experience regarding the problem domain, i.e., quantum software engineering for combinatorial optimization, was gathered. In combination with a systematic review of quantum combinatorial optimization, the foundation for the upcoming development of the solution framework was laid. The latter development was conducted in an iterative manner by alternating between design and evaluation phases. The solution framework has been validated by demonstration and comparison studies as well as experiments on representative use cases.
Contributions: The core contribution of the dissertation is represented by the solution artifact, i.e., the instantiated quantum software development framework for raising abstraction and automation levels, and the corresponding developed model-driven and search-based approaches. During the research process, additional contributions have been elaborated. First, within the pilot project, two novel hybrid quantum-classical algorithms have been developed and evaluated, and a framework for applying the latter has been designed. Second, the systematic mapping study on quantum combinatorial optimization illustrates the current state of the art with respect to solution approaches and common optimization problems.
Note: This cumulative dissertation includes 2 published journal articles, 1 conference paper, 2 workshop papers, and 2 submitted journal articles. In the following, a summary of the conducted research for this dissertation is provided by presenting the overall research project. Thus, the aim is to convey the problem, methodology, contributions, and results of the underlying design science project. In the appendix, the following articles contributing to this dissertation are attached:
* Gemeinhardt, F. G., Wille, R., & Wimmer, M. (2021). Quantum k-community detection: algorithm proposals and cross-architectural evaluation. In Quantum Information Processing, 20(9), 302.
* Gemeinhardt, F. G., Garmendia, A., Wimmer, M., Weder, B., & Leymann, F. (2023). Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study. In ACM Computing Surveys, 56(3), 1-36.
* Gemeinhardt, F. G., Garmendia, A., Wimmer, M., & Wille, R. (2022). A Model-Driven Framework for Composition-Based Quantum Circuit Design. Resubmitted after Major Revision for publication to ACM Transactions on Quantum Computing in November 2023.
* Gemeinhardt, F. G., Klikovits, S., & Wimmer, M. (2023). GeQuPI: Quantum Program Improvement with Multi-Objective Genetic Programming. Submitted for publication to the Journal of Systems and Software in September 2023.
* Gemeinhardt, F. G., Klikovits, S., & Wimmer, M. (2023, July). Hybrid Multi-Objective Genetic Programming for Parameterized Quantum Operator Discovery. In Proceedings of the Conference on Genetic and Evolutionary Computation: In Companion Proceedings (pp. 795-798).
* Gemeinhardt, F. G., Eisenberg, M., Klikovits, S., & Wimmer, M. (2023, October). Model-Driven Optimization for Quantum Program Synthesis with MOMoT. In Proceedings of the 26th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings. In print.
* Gemeinhardt, F. G., Garmendia, A., & Wimmer, M. (2021, June). Towards model-driven quantum software engineering. In 2021 IEEE/ACM Proceedings of the 2nd International Workshop on Quantum Software Engineering (Q-SE) (pp. 13-15). IEEE.",
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notes = "urn:nbn:at:at-ubl:1-72420
Supervisors: Manuel Wimmer and Robert Wille",
