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.
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.
Quantum computing is rapidly advancing, but quantum software development faces significant challenges, including a steep learning curve, high hardware error rates, and a lack of mature engineering practices. This study conducts a large-scale mining analysis of over 21,000 GitHub repositories, containing 1.2 million commits from more than 10,000 developers, to examine the evolution and maintenance of quantum software. We analyze repository growth, programming language and framework adoption, and contributor trends, revealing a 200 % increase in repositories and a 150 % rise in contributors since 2017. Additionally, we investigate software development and maintenance practices, showing that perfective commits dominate (51.76 %), while the low occurrence of corrective commits (18.54%) indicates potential gaps in bug resolution. Furthermore, 34% of reported issues are quantum-specific, highlighting the need for specialized debugging tools beyond conventional software engineering approaches. This study provides empirical insights into the software engineering challenges of quantum computing, offering recommendations to improve development workflows, tooling, and documentation. We are also open-sourcing our dataset to support further analysis by the community and to guide future research and tool development for quantum computing. The dataset is available at: https://github.com/kriss-u/QRepoAnalysis-Paper.
Quantum computing is moving from research laboratories towards early commercialization and broader socio-technical adoption, supported by sustained hardware progress and a rapidly expanding open-source software ecosystem. This momentum is especially visible on GitHub, where many quantum and hybrid software projects coexist around frameworks such as Qiskit, Cirq, PennyLane and Amazon Braket. However, this ecosystem remains fragmented, making it difficult to understand who shapes quantum software, where expertise is concentrated, how collaboration flows across organizations and disciplines, and which actors connect otherwise separated communities. This paper presents Entangle, a data-driven analysis of the open-source quantum computing ecosystem on GitHub. Starting from 71 domain keywords, Entangle identifies more than 1,500 quantum repositories, 27,000 contributors and 400 organizations, revealing an ecosystem strongly organized around four leading industrial vendors, but also supported by 2,387 contributors who connect projects, organizations and domains. These findings provide practical evidence for responsible quantum innovation by making visible patterns of influence, dependency, collaboration and knowledge transfer. They also offer actionable indicators for strategic decisions on investment, hiring, partnerships, ecosystem stewardship and capacity building. More broadly, Entangle shows how open-source intelligence can support a more transparent, measurable and governable quantum software ecosystem, helping align technical development with responsible innovation, public--private coordination and long-term sustainability.
Quantum Software Engineering (QSE) is essential for ensuring the reliability and maintainability of hybrid quantum-classical systems, yet empirical evidence on how bugs emerge and affect quality in real-world quantum projects remains limited. This study presents the first ecosystem-scale longitudinal analysis of software bugs across 123 open source quantum repositories from 2012 to 2024, spanning eight functional categories, including full-stack libraries, simulators, annealing, algorithms, compilers, assembly, cryptography, and experimental computing. Using a mixed method approach combining repository mining, static code analysis, issue metadata extraction, and a validated rule-based classification framework, we analyze 32,296 verified bug reports. Results show that full-stack libraries and compilers are the most bug-prone categories due to circuit, gate, and transpilation-related issues, while simulators are mainly affected by measurement and noise modeling errors. Classical bugs primarily impact usability and interoperability, whereas quantum-specific bugs disproportionately degrade performance, maintainability, and reliability. Longitudinal analysis indicates ecosystem maturation, with bug densities peaking between 2017 and 2021 and declining thereafter. High-severity bugs cluster in cryptography, experimental computing, and compiler toolchains. Repositories employing automated testing detect more bugs and resolve issues faster. A negative binomial regression further shows that automated testing is associated with an approximate 60 percent reduction in expected bug incidence. Overall, this work provides the first large-scale data-driven characterization of quantum software bugs and offers empirical guidance for improving testing, documentation, and maintainability practices in QSE.
Quantum open-source software provides essential infrastructure for quantum science, and artificial intelligence (AI) is reshaping its capabilities and transforming its development. However, verifying quantum software at scale remains difficult, and AI-generated contributions can increase the workload of maintainers. In this Perspective, we examine verification as a central requirement for reliable AI-assisted quantum software development and research. We analyze public repositories, records covering 110 repositories from five annual quantum open-source hackathons that we organize, and responses from more than 1600 participants in a community survey. Our analysis connects limitations in automated verification with the review burden on quantum software maintainers. We propose verification practices and contribution standards that require contributors to support their changes with reproducible evidence for maintainer review. We also examine how quantum open-source software can provide infrastructure for AI-driven research whose results the community can independently verify and build upon. We maintain an open-source companion repository to support reproducibility and help projects adopt and refine these proposals. Together, these contributions help the quantum open-source software community translate advances in AI into reliable quantum research through reproducible verification.
Quantum computing is no longer only a scientific interest but is rapidly becoming an industrially available technology that can potentially overcome the limits of classical computation. Over the last years, all major companies have provided frameworks and programming languages that allow developers to create their quantum applications. This shift has led to the definition of a new discipline called quantum software engineering, which is demanded to define novel methods for engineering large-scale quantum applications. While the research community is successfully embracing this call, we notice a lack of systematic investigations into the state of the practice of quantum programming. Understanding the challenges that quantum developers face is vital to precisely define the aims of quantum software engineering. Hence, in this paper, we first mine all the GitHub repositories that make use of the most used quantum programming frameworks currently on the market and then conduct coding analysis sessions to produce a taxonomy of the purposes for which quantum technologies are used. In the second place, we conduct a survey study that involves the contributors of the considered repositories, which aims to elicit the developers' opinions on the current adoption and challenges of quantum programming. On the one hand, the results highlight that the current adoption of quantum programming is still limited. On the other hand, there are many challenges that the software engineering community should carefully consider: these do not strictly pertain to technical concerns but also socio-technical matters.
Quantum computing has become an active research field in recent years, as its applications in fields such as cryptography, optimization, and materials science are promising. Along with these developments, challenges and opportunities exist in the field of Quantum Software Engineering, as the development of frameworks and higher-level abstractions has attracted practitioners from diverse backgrounds. Unlike initial quantum frameworks based on the circuit model, recent frameworks and libraries leverage higher-level abstractions for creating quantum programs. This paper presents an empirical study of 985 Jupyter Notebooks from 80 open-source projects to investigate how quantum patterns are applied in practice. Our work involved two main stages. First, we built a knowledge base from three quantum computing frameworks (Qiskit, PennyLane, and Classiq). This process led us to identify and document 9 new patterns that refine and extend the existing quantum computing pattern catalog. Second, we developed a reusable semantic search tool to automatically detect these patterns across our large-scale dataset, providing a practitioner-focused analysis. Our results show that developers use patterns in three levels: from foundational circuit utilities, to common algorithmic primitives (e.g., Amplitude Amplification), up to domain-specific applications for finance and optimization. This indicates a maturing field where developers are increasingly using high-level building blocks to solve real-world problems.