Year: 2025
Type: conference-paper
Abstract: As quantum software engineering (QSE) matures, practitioners are adopting classical software engineering practices. This paper proposes a quantum experiment tracking infrastructure that aids quantum researchers in making informed decisions while developing quantum software incrementally and iteratively. By leveraging tools and best practices from classical machine learning communities, we provide a mature environment for quantum software practitioners to enhance their practices. Our approach involves collecting a comprehensive quantum provenance dataset from each experiment execution for analysis and decision-making. To validate our approach, we developed an experimental environment where a program was iteratively developed on quantum simulators and an IQM computer, and we demonstrate how the development workflow backed by the tracking infrastructure meets expectations for quantum experiment reporting.
Authors: Otso Kinanen, Mahee Gamage, Vlad Stirbu
Institution: University of Jyväskylä
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: Academy of Finland, Business Finland
Awards: G3300112470, G4402159100
OpenAlex ID: W4416874861