Empirical Investigation of Quantum Computing Toolchains and Algorithms : Mining Stack Overflow Repository

2026-04-16

AI Summary

This study addresses the lack of systematic understanding regarding the challenges faced by quantum computing developers when using practical toolchains and algorithms. Through the first large-scale empirical analysis of 1,404 Stack Overflow posts, combining topic modeling, quantitative content analysis, and evaluation of answer acceptance rates and response times, the work reveals the topical landscape, tool adoption patterns, and algorithmic references within the developer community. Seven core themes are identified, with hybrid quantum-classical computing and quantum circuit implementation emerging as the most prominent. Qiskit and Q# dominate as the primary development frameworks, while Grover’s and Shor’s algorithms are the most frequently cited. The study further quantifies significant differences across topics in terms of problem-solving difficulty and levels of community support.

Technology Category

• Machine Learning: Quantum Machine Learning
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Constraint Satisfaction and Optimization: Solvers and Tools

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Community question answering
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

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.

Problem

• quantum computing
• developer discussion
• quantum programming
• quantum algorithms
• software engineering

Innovation

• quantum computing
• developer discussion
• Stack Overflow
• topic modeling
• software engineering