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 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 software engineering faces unique challenges due to the interaction of classical and quantum components, which produce complex and often poorly understood bug patterns. Characterizing these bugs is essential for advancing testing, debugging, and quality assurance in quantum ecosystems. This paper presents a comparative study of 12,910 issues from Qiskit and 4,613 issues from 11 additional repositories, including Cirq and PyQuil. Using a rule-based classification framework, we analyze bugs by type, category, severity, quality attributes, and quantum-specific subtypes. Results show that classical bugs consistently dominate (67%) across ecosystems, while quantum-specific bugs account for 27-30%. Ecosystem-specific trends emerge: Qiskit repositories exhibit more compatibility related bugs, whereas other ecosystems show higher syntax and quantum-specific bug rates. Across both ecosystems, gate and circuit issues dominate quantum-specific bugs, though non-Qiskit projects reveal broader diversity, including algorithmic, resource, and hybrid-interface issues. Statistical validation confirms that the framework generalizes at the bug-type level while detecting significant variations at finer levels. Benchmarking against four supervised machine-learning baselines further shows that the rule-based framework consistently outperforms data-driven models, particularly for fine-grained quantum-specific subtypes, while longitudinal analysis (2017-2025) indicates that quantum- specific bugs remain relatively stable over time rather than exhibiting a steady increase. This study provides the first cross- ecosystem comparison of bug distributions in quantum software, demonstrating the utility of an interpretable, automation-ready, rule-based framework for guiding testing, debugging, and quality assurance.
Quantum simulators are a foundational component of the quantum software ecosystem. They are widely used to develop and debug quantum programs, validate compiler transformations, and support empirical claims about correctness and performance. In the absence of large-scale quantum hardware, simulator outputs are often treated as ground truth for algorithm development and system evaluation. However, quantum simulators also introduce unique implementation challenges. They must faithfully emulate quantum behavior while executing on classical hardware, requiring complex representations of quantum state evolution, operator composition, and noise modeling. Yet, we still lack a large-scale and in-depth study of failures in quantum simulators.
To bridge this gap, this work presents a comprehensive empirical study of bugs in widely used open-source quantum simulators. We analyze 394 confirmed bugs from 12 simulators and manually categorize them based on root causes, failure manifestations, affected components, and discovery mechanisms. Our study reveals several key findings. First, bug discovery is largely user-driven, with most crashes, exceptions, and resource-related failures not detected by automated testing and identified after deployment. Second, logical correctness failures are widespread and often silent, producing plausible but incorrect outputs without triggering crashes or explicit error signals. Third, many critical failures originate in classical simulator infrastructure, such as memory management, indexing, configuration, and dependency compatibility, rather than in core quantum execution logic. These findings provide new insights into the reliability challenges of quantum simulators and highlight opportunities to improve testing and validation practices in the quantum software ecosystem.
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.
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.
Abstract As quantum computers continue to improve in quality and scale, there is a growing need for accessible software frameworks for programming them. However, the unique behavior of quantum systems means specialized approaches, beyond traditional software development, are required. This is particularly true for debugging due to quantum bugs, i.e., bugs that occur precisely because an algorithm is a quantum algorithm. Pinpointing a quantum bug’s root cause often requires significant developer time, as there is little established guidance for quantum debugging techniques. Developing such guidance is the main challenge we sought to address. In this work, we describe a set of 14 quantum bugs, sourced primarily from our experience as quantum software developers, and supplemented by analysis of open-source GitHub repositories. We detail their context, symptoms, and the techniques applied to identify and fix them. While classifying these bugs based on existing schemes, we observed that most emerged due to unique interactions between multiple aspects of an algorithm or workflow. In other words, they occurred because more than one thing went wrong, which provided important insight into why quantum debugging is more challenging. Furthermore, based on this clustering, we found that—unexpectedly—there is no clear relationship between debugging strategies and bug classes. Further research is needed to develop effective and systematic quantum debugging strategies.
Quantum software relies on compilers, intermediate representations, and execution frameworks that transform programs before execution. Faults in these components can change the behavior of otherwise correct quantum programs. Yet quantum testing techniques are often evaluated using mutation-generated faults and curated bug benchmarks. We investigate how well the faults represented by these artifacts reflect those encountered in modern quantum software. We audit four quantum mutation-testing tools and three curated bug benchmarks, and collect 410 verified bugs from ten quantum software repositories over two years. We classify faults from all three sources into a common taxonomy of 56 bug families and compare their representation across mutation operators, curated benchmarks, and recent real-world bugs. The mutation tools support 20 bug families, and curated benchmarks add 15 more. Yet only 56 of the 410 real-world bugs (13.7%) belong to families represented by either source; the remaining 354 (86.3%) belong to families absent from both. The largest unrepresented families concern compiler transformations (66 bugs), type handling (53), and input validation (47). Unlike local gate or statement changes, these faults often depend on intermediate representations, compiler pipelines, input types, or dependency versions. Existing mutation operators primarily modify circuits and program constructs, whereas many real-world faults arise in the compilers, representations, and framework interfaces that process them. Addressing this gap requires looking beyond circuit-level faults toward testing semantic preservation across compiler transformations, correctness at representation boundaries, and interactions between software components. Our study provides empirical evidence to guide fault models and benchmarks that capture these mechanisms and preserve the contexts needed to reproduce them.