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 ($\mathbf{1 8. 5 4 \%}$) 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 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 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.
This repository provides the open dataset accompanying the study on developer challenges in quantum computing, based on an empirical analysis of 1,404 Stack Overflow posts related to quantum computing technologies. The dataset supports the investigation of practitioner-reported challenges, their underlying causes, their variation across development contexts, and their resolution difficulty. The dataset includes coded information extracted from Stack Overflow posts, including challenge categories, underlying cause categories, related quantum tools and platforms, discussed topics, associated quantum algorithms where applicable, and resolution-related metadata such as accepted-answer status and time to accepted answer. The coding was conducted using a taxonomy-guided empirical analysis to support systematic classification and reproducibility. This dataset is intended to support transparency, replication, and further research on quantum software engineering, quantum programming education, toolchain usability, and developer support in quantum computing. It may be reused by researchers, educators, and tool developers interested in understanding the practical difficulties faced by developers when adopting quantum technologies.
This repository provides the open dataset accompanying the study on developer challenges in quantum computing, based on an empirical analysis of 1,404 Stack Overflow posts related to quantum computing technologies. The dataset supports the investigation of practitioner-reported challenges, their underlying causes, their variation across development contexts, and their resolution difficulty. The dataset includes coded information extracted from Stack Overflow posts, including challenge categories, underlying cause categories, related quantum tools and platforms, discussed topics, associated quantum algorithms where applicable, and resolution-related metadata such as accepted-answer status and time to accepted answer. The coding was conducted using a taxonomy-guided empirical analysis to support systematic classification and reproducibility. This dataset is intended to support transparency, replication, and further research on quantum software engineering, quantum programming education, toolchain usability, and developer support in quantum computing. It may be reused by researchers, educators, and tool developers interested in understanding the practical difficulties faced by developers when adopting quantum technologies.
Quantum computing (QC) is increasingly transitioning toward practical and industrial adoption, highlighting the need to understand how developers engage with quantum technologies. In this study, we analyze 1,404 Stack Overflow posts related to quantum computing topics, including quantum programming, tools, and algorithms, to investigate real-world developer discussions. Using topic modeling and quantitative analysis, we identify the main discussion topics, their popularity, and the tools, programming languages, and quantum algorithms referenced by practitioners. We further assess the difficulty of developer questions using two metrics: (i) the percentage of questions without accepted answers and (ii) the median time required to receive an accepted answer. Our findings reveal seven main topics, with hybrid quantum--classical computing and quantum circuit implementation emerging as the most prevalent. We observe that Qiskit and Q-sharp dominate developer discussions, while Grover's and Shor's algorithms are the most frequently referenced. Moreover, our analysis highlights differences in engagement and difficulty across topics, tools, and algorithms, indicating varying levels of maturity and community support. These findings provide actionable insights for researchers, tool developers, and educators, supporting improvements in usability, documentation, and learning resources in quantum software engineering. To support transparency and reproducibility, the open-source dataset used in this study is publicly available at Zenodo.
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.
With the advance of quantum computing, quantum software becomes critical for exploring the full potential of quantum computing systems. Recently, quantum software engineering (QSE) becomes an emerging area attracting more and more attention. However, it is not clear what are the challenges and opportunities of quantum computing facing the software engineering community. This work aims to understand the QSE-related challenges perceived by developers. We perform an empirical study on Stack Exchange forums where developers post-QSE-related questions & answers and Github issue reports where developers raise QSE-related issues in practical quantum computing projects. Based on an existing taxonomy of question types on Stack Overflow, we first perform a qualitative analysis of the types of QSE-related questions asked on Stack Exchange forums. We then use automated topic modeling to uncover the topics in QSE-related Stack Exchange posts and GitHub issue reports. Our study highlights some particularly challenging areas of QSE that are different from that of traditional software engineering, such as explaining the theory behind quantum computing code, interpreting quantum program outputs, and bridging the knowledge gap between quantum computing and classical computing, as well as their associated opportunities.