Quantum Software Engineering (QSE) has recently emerged as a potential research and development direction frequently practiced by many tech joints. However, quantum developers face challenges in optimizing quantum computing and QSE concepts. Quantum developers use the Stack Overflow (SO) platform to report and discuss quantum-related challenges. Also, quantum practitioners use specialized quantum tags to label quantum-related posts in developers' forums. However, these quantum tags referred to more technical quantum aspects than the developer posts. Therefore, categorizing quantum practitioners' questions based on quantum concepts can help quantum developers better identify frequently occurring challenges to QSE. For this purpose, we conducted qualitative and quantitative studies to classify quantum developers' questions into various frequently occurring quantum-related challenges. We extracted 2829 developers' questions from various Q&A platforms using queries and filters based on quantum-related tags. Next, the developers' posts on the Q&A forums were critically analyzed to identify frequently discussed quantum-related challenges and develop a novel grounded theory. The frequent quantum developer challenges identified by analyzing practitioners' discussions in the Q&A forums include Tooling, Theoretical, Learning, Conceptual, Errors, and API Usage. Moreover, using content analysis and grounded theory, the developers' discussions were annotated with commonly reported quantum challenges to develop a ground truth and a novel dataset. A Large Language model (ChatGPT) was used to validate the human annotation and overcome disagreements. Finally, various fine-tuned Deep and Machine learning (D&ML) classifiers automatically classify developer discussions into commonly reported quantum challenges. Additionally, to improve the classification results, we utilized textual data augmentation approaches, such as random deletion, swapping, and insertion with the D&ML classifiers. We obtained average accuracies of 89%, 86%, 84%, 84%, and 80% with FNN, CNN, LSTM, GRU, and RNN classifiers, respectively. This helps quantum researchers and vendors propose solutions and tools to frequently occurring issues for quantum developers.
Quantum Software Engineering (QSE) has recently emerged as a potential research and development direction frequently practiced by many tech joints. However, quantum developers face challenges in optimizing quantum computing and QSE concepts. Quantum developers use the Stack Overflow (SO) platform to report and discuss quantum-related challenges. Also, quantum practitioners use specialized quantum tags to label quantum-related posts in developers' forums. However, these quantum tags referred to more technical quantum aspects than the developer posts. Therefore, categorizing quantum practitioners' questions based on quantum concepts can help quantum developers better identify frequently occurring challenges to QSE. For this purpose, we conducted qualitative and quantitative studies to classify quantum developers' questions into various frequently occurring quantum-related challenges. We extracted 2829 developers' questions from various Q&A platforms using queries and filters based on quantum-related tags. Next, the developers' posts on the Q&A forums were critically analyzed to identify frequently discussed quantum-related challenges and develop a novel grounded theory. The frequent quantum developer challenges identified by analyzing practitioners' discussions in the Q&A forums include Tooling, Theoretical, Learning, Conceptual, Errors, and API Usage. Moreover, using content analysis and grounded theory, the developers' discussions were annotated with commonly reported quantum challenges to develop a ground truth and a novel dataset. A Large Language model (ChatGPT) was used to validate the human annotation and overcome disagreements. Finally, various fine-tuned Deep and Machine learning (D&ML) classifiers automatically classify developer discussions into commonly reported quantum challenges. Additionally, to improve the classification results, we utilized textual data augmentation approaches, such as random deletion, swapping, and insertion with the D&ML classifiers. We obtained average accuracies of 89%, 86%, 84%, 84%, and 80% with FNN, CNN, LSTM, GRU, and RNN classifiers, respectively. This helps quantum researchers and vendors propose solutions and tools to frequently occurring issues for quantum developers.
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.
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.
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.
Abstract Quantum computing is steadily transitioning from theoretical exploration to experimental deployment, bringing with it distinct engineering challenges. This shift has led to the emergence of Quantum Software Engineering (QSE), a research area that adapts and extends software engineering principles to the quantum domain. In parallel, Large Language Models (LLMs) have become widely adopted in classical software development workflows. Although their potential to assist QSE tasks is increasingly discussed, existing evidence remains fragmented across research venues and practitioner sources. This study presents a Multivocal Literature Review that systematically synthesizes peer-reviewed and grey literature to characterize how LLMs are used in QSE and assess their reported limitations. We identified and analyzed 24 primary sources, mapped their contributions onto the quantum software development lifecycle, examined the distribution of adopted LLM families, and synthesized recurring limitations. The results show that LLM adoption in QSE is predominantly code-centric, with most studies focusing on program synthesis, repair, and refactoring, while lifecycle phases such as requirements engineering, architectural reasoning, and comprehensive validation receive limited attention. Reported challenges include semantic inaccuracies, execution failures on real quantum backends, sensitivity to prompt formulation, and limited domain coverage. Moreover, the empirical landscape is largely shaped by proprietary models, raising concerns regarding reproducibility and longitudinal comparability. Overall, current evidence suggests that LLMs function primarily as assistive tools for bounded tasks rather than as robust, lifecycle-spanning engineering agents in quantum software development.
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.