We investigate how quantum, quantum-inspired, and hybrid quantum-classical computing approaches can be applied to challenging software engineering problems.
← Back to ResearchOur research explores the use of quantum and quantum-inspired computing to address complex software engineering problems. A major focus is software engineering optimization, where problems such as test selection and minimization can be formulated and solved using quantum and hybrid optimization approaches.
Beyond developing and applying these methods, we investigate their practical feasibility and empirical evaluation. Our work examines when quantum-based solutions are appropriate and how different optimization approaches can be benchmarked and evaluated in a rigorous and reproducible manner.
Explore our tools on Quantum Software Engineering.
Research publications on quantum software engineering.
This work proposes Q-READY, a model-based approach for assessing the feasibility of hybrid quantum-classical applications before implementation. It supports requirements modeling, problem formulation, workflow design, and hardware-aware evaluation of candidate solutions under realistic constraints.
VenuearXiv · Preprint
This work presents QPipe, an LLM-based multi-agent architecture that transforms natural-language requirements into executable quantum applications for test optimization. Specialized agents collaborate on requirement parsing, problem formulation, code generation, review, execution, and verification.
VenuearXiv · Preprint
This systematic literature review examines how quantum and quantum-inspired optimization approaches have been applied to software engineering problems. It maps existing research across software engineering activities and identifies areas of concentrated research as well as gaps for future investigation.
VenueACM Transactions on Software Engineering and Methodology
This work systematically analyzes how quantum, quantum-inspired, and hybrid optimization approaches are empirically evaluated in software engineering. It examines experimental designs, hyperparameter settings, case studies, baselines, tools, and evaluation metrics, and identifies gaps in current empirical practices.
VenuearXiv · Preprint
