Quantum Software Bugs: Analysis of 32,296 Defects Advances Reliability

Understanding the reliability of quantum software is crucial as these systems become increasingly integrated with classical computing, yet detailed empirical studies of real-world quantum code have been lacking. Mir Mohammad Yousuf and Shabir Ahmad Sofi, from the National Institute of Technology Srinagar, along with their colleagues, address this gap by presenting the first large-scale analysis of software defects across 123 open-source quantum repositories spanning over a decade. Their investigation of over 32,000 verified bug reports reveals that full-stack libraries and compilers exhibit the highest defect rates, linked to issues in circuit design and code translation, while simulators struggle with accurate modelling of quantum phenomena. Importantly, the team demonstrates that automated testing significantly improves code quality, reducing expected defect incidence by approximately 60 percent, and provides valuable data-driven insights to guide future development and maintenance practices in the emerging field of quantum software engineering.

Quantum Software Bugs And Reliability Trends

This research presents an empirical study of bugs and quality attributes in quantum software, aiming to understand the types of errors present, how they change over time, and what factors contribute to software reliability in this emerging field. The authors analyzed a large dataset of open-source quantum software projects to draw conclusions about software quality and identify key challenges for developers. The study highlights the need for dedicated software engineering practices tailored to the unique challenges of quantum programming, given the significant number of bugs currently present. Researchers developed a classification scheme for quantum bugs, categorizing them as classical, quantum-specific, or hybrid, reflecting the interplay between classical and quantum components.

The study uses standard software engineering metrics, such as bug counts and code complexity, alongside quantum-specific considerations like circuit fidelity, to assess software quality. Testing is crucial for identifying and addressing quantum-related errors, and the research emphasizes the need for specialized benchmarks and tools to facilitate robust testing and debugging. The methodology involved analyzing 123 open-source quantum software repositories on GitHub, identifying and classifying bugs through code analysis and review. Tools like Radon and Pylint were used to measure code complexity and quality, and statistical techniques were applied to identify significant trends and relationships. Investigations focused on bug trends over time, the relationship between code quality and bugs, and the characteristics of quantum-specific errors, providing a valuable empirical foundation for the field of quantum software engineering.

Mining and Analyzing Quantum Software Bug Reports

This study pioneers a large-scale investigation into quantum software defects, analyzing over 32,000 verified bug reports from 123 open-source repositories spanning 2012 to 2024. Researchers combined repository mining, static code analysis, and issue metadata extraction to build a comprehensive dataset of software defects, enabling detailed analysis of their characteristics and impact on quality attributes like performance, maintainability, and reliability. The team developed a validated rule-based classification framework to distinguish between classical software bugs and those unique to quantum systems, as well as to identify specific quantum-related subtypes. Statistical analysis, including negative binomial regression, revealed that repositories utilizing automated tests experienced an approximate 60 percent reduction in expected defects, demonstrating the effectiveness of proactive quality assurance. Longitudinal analysis tracked defect densities over time, showing a period of peak activity between 2017 and 2021, followed by a decline, suggesting ecosystem maturation and improved software quality practices.

Quantum Software Defects Across Ecosystem and Time

Scientists conducted the first ecosystem-scale longitudinal analysis of software defects, examining 123 open-source quantum repositories from 2012 to 2024. Analyzing over 32,000 verified bug reports using a combined approach of repository mining, static code analysis, and a validated classification framework, the team established a comprehensive dataset for quantum software engineering research. Results demonstrate that full-stack libraries and compilers exhibit the highest defect rates, with issues frequently related to circuit design and transpilation processes. The study revealed distinct impacts of different bug types, showing that classical bugs predominantly affect usability and interoperability, whereas quantum-specific bugs significantly degrade performance, maintainability, and reliability. Longitudinal analysis indicates a maturing ecosystem, with defect densities peaking between 2017 and 2021 before declining, suggesting improvements in development practices and quality control. Automated testing consistently detected more defects and resolved issues faster, and negative binomial regression quantified this benefit, showing a 60 percent reduction in expected defect incidence.

Quantum Software Defects, A Decade Analyzed

This research presents the first large-scale, longitudinal analysis of software defects across 123 open-source quantum software repositories spanning over a decade. Through a detailed examination of over 32,000 verified bug reports, scientists identified patterns in how defects emerge and impact software quality, categorizing them as either classical or quantum-specific. The findings reveal that full-stack libraries and compilers exhibit the highest concentration of bugs, due to their complex architecture and the interplay between classical and quantum components. The study demonstrates a clear link between defect type and quality attributes, with classical bugs primarily affecting usability and interoperability, while quantum-specific bugs more significantly degrade performance and reliability. The team observed a maturing ecosystem, with defect densities peaking between 2017 and 2021 and subsequently declining, suggesting improvements in development practices and standardization. Automated testing proved a strong indicator of software reliability, with repositories employing such testing showing approximately a 60 percent reduction in expected defect incidence.