Vincent Gierisch

Driven by contributions from academia, industry, and open-source communities, the quantum software ecosystem is rapidly growing. Across this ecosystem, new concepts often emerge through software artefacts accompanying scientific publications as well as through sustained development in larger communities. However, many repositories receive development efforts only over a limited period of time, raising the question whether their concepts persist beyond individual repositories.

In this paper, we apply established empirical software engineering techniques to analyse a set of foundational quantum software repositories. We combine cross-repository activity with contributor relationships to study the evolution of communities. Our analysis provides empirical evidence of contributor migration patterns and indications of cross-project knowledge transfer. We observe multiple development paths: projects may evolve into sustained communities, contributors may integrate concepts into established ecosystems, or activity may continue through new and follow-up software artefacts. Our observations provide an initial empirical perspective on how concepts and influence persist across repository boundaries in quantum software ecosystems.

Commercially available Noisy Intermediate-Scale Quantum (NISQ) devices now make small hybrid quantum-classical experiments practical, but many tools hide configuration or demand ad-hoc scripting.

We introduce the Quantum Experiment Framework (QEF): A lightweight framework designed to support the systematic, hypothesis-driven study of quantum algorithms. Unlike many existing approaches, QEF emphasises iterative, exploratory analysis of evolving experimental strategies rather than exhaustive empirical evaluation of fixed algorithms using predefined quality metrics. The framework's design is informed by a comprehensive review of the literature, identifying principal parameters and measurement practices currently reported in the field.

QEF captures all key aspects of quantum software and algorithm experiments through a concise specification that expands into a Cartesian product of variants for controlled large-scale parameter sweeps. This design enables rigorous and systematic evaluation, as well as precise reproducibility. Large sweeps are automatically partitioned into asynchronous jobs across simulators or cloud hardware, and ascertain full hyper-parameter traceability. QEF supports parameter reuse to improve overall experiment runtimes, and collects all metrics and metadata into a form that can be conveniently explored with standard statistical and visualisation software.

By combining reproducibility and scalability while avoiding the complexities of full workflow engines, QEF seeks to lower the practical barriers to empirical research on quantum algorithms, whether these are designed for current NISQ devices or future error-corrected quantum systems.