pith. sign in

arxiv: 2604.15512 · v1 · submitted 2026-04-16 · 💻 cs.SE

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

Maryam Tavassoli Sabzevari , Arif Ali Khan This is my paper

Pith reviewed 2026-05-10 10:20 UTC · model grok-4.3

classification 💻 cs.SE
keywords quantum computingstack overflowtopic modelingdeveloper discussionsquantum algorithmsqiskithybrid quantum classical
0
0 comments X

The pith

Analysis of 1,404 Stack Overflow posts identifies hybrid quantum-classical computing and quantum circuit implementation as the leading discussion topics.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper mines real-world developer questions posted on Stack Overflow to map what practitioners actually discuss when working with quantum technologies. It applies topic modeling to surface seven recurring areas and uses two simple metrics to gauge how difficult those questions prove to be in practice. The work matters because quantum computing is shifting from research labs toward industrial use, so knowing which tools and problems generate the most friction can guide better documentation, education, and tool design. Findings show clear patterns in which platforms and algorithms attract the most attention.

Core claim

By analyzing 1,404 Stack Overflow posts on quantum programming, tools, and algorithms, the study identifies seven main topics, with hybrid quantum-classical computing and quantum circuit implementation as the most common. Qiskit and Q-sharp appear most frequently in discussions, while Grover's and Shor's algorithms are the algorithms mentioned most often. The analysis further shows that engagement levels and question difficulty vary across topics, tools, and algorithms, suggesting uneven maturity and community support in different parts of quantum software engineering.

What carries the argument

Topic modeling of Stack Overflow posts paired with two difficulty metrics: the share of questions lacking an accepted answer and the median time until an accepted answer appears.

If this is right

  • Tool builders can allocate more effort to improving hybrid integration and circuit tools to address the most common developer questions.
  • Training resources should emphasize Qiskit, Q-sharp, Grover's algorithm, and Shor's algorithm given their high visibility in discussions.
  • Topics with higher rates of unanswered questions point to areas where additional documentation or examples would reduce friction for new users.
  • Differences in response times across algorithms suggest varying levels of community expertise that could inform targeted outreach or tutorials.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The dominance of hybrid topics may signal that current barriers to quantum adoption lie more in system integration than in standalone quantum algorithms.
  • Repeating the same analysis on GitHub issues or developer forums could test whether Stack Overflow captures the full spectrum of practical difficulties.
  • Clearer identification of mature versus emerging areas could help prioritize which quantum software components need accelerated standardization.

Load-bearing premise

The collected Stack Overflow posts represent the typical challenges and interests of quantum computing developers at large without strong selection bias in posting behavior.

What would settle it

A broad survey of practicing quantum developers that reports substantially different top topics, dominant tools, or difficulty patterns than those extracted from the 1,404 posts would undermine the central findings.

Figures

Figures reproduced from arXiv: 2604.15512 by Arif Ali Khan, Maryam Tavassoli Sabzevari.

Figure 1
Figure 1. Figure 1: Tool–Topic Percentage T0 T1 T2 T3 T4 T5 T6 Topics grover shor quantum fourier transform vqe qaoa deutsch hhl deutsch-jozsa phase estimation Algorithms 17.4% 4.3% 4.3% 4.3% 39.1% 13.0% 17.4% 9.1% 9.1% 0.0% 0.0% 27.3% 45.5% 9.1% 0.0% 0.0% 20.0% 20.0% 10.0% 40.0% 10.0% 0.0% 10.0% 30.0% 0.0% 10.0% 40.0% 10.0% 0.0% 22.2% 44.4% 0.0% 22.2% 0.0% 11.1% 80.0% 0.0% 0.0% 0.0% 20.0% 0.0% 0.0% 20.0% 0.0% 0.0% 0.0% 0.0% … view at source ↗
Figure 2
Figure 2. Figure 2: Algorithm–Topic Percentage indicating increased conceptual difficulty. However, the shorter time to receive accepted answers suggests that expertise is available within the community. This implies that while common algorithms [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Tool topic difficulty are well supported, educational resources and tooling for more advanced or less frequently used algorithms may still be limited. Finally, this study contributes a comprehensive, practitioner￾centered perspective on quantum computing by jointly analyzing topics, tools, and algorithms. These findings can support future research in quantum software engineering, including the design of de… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. This paper presents an empirical analysis of 1,404 Stack Overflow posts on quantum computing topics. Using topic modeling, it identifies seven main discussion topics, with hybrid quantum-classical computing and quantum circuit implementation as the most prevalent. It reports that Qiskit and Q# dominate tool discussions and that Grover's and Shor's algorithms are the most frequently referenced. Difficulty is assessed via two proxies (percentage of questions without accepted answers; median time to accepted answer), revealing differences across topics, tools, and algorithms. The authors release the dataset publicly on Zenodo to support reproducibility.

Significance. If the findings hold after validation, the work supplies actionable, data-driven insights for quantum software engineering researchers, tool builders, and educators by highlighting practitioner pain points and maturity gaps in an emerging field. The public dataset release is a clear strength that enables direct replication and extension.

major comments (3)
  1. [§3] §3 (Data Collection and Topic Modeling): The identification of exactly seven topics and their prevalence rankings (hybrid QC-classical and circuit implementation dominant) rests on an LDA-style model with no reported coherence scores (e.g., C_v or NPMI), no stability analysis across random seeds, and no human validation of topic labels. These omissions are load-bearing because the central claims about topic structure and dominance cannot be assessed for robustness without them.
  2. [§4] §4 (Tool and Algorithm Frequency Analysis): Claims that Qiskit/Q# dominate and that Grover's/Shor's algorithms are most referenced are derived solely from the 1,404 SO posts without any triangulation against external sources (GitHub issues, surveys, or other forums). This is load-bearing for the representativeness assumption stated in the abstract, as SO data alone may reflect platform-specific biases rather than broader developer engagement.
  3. [§5] §5 (Difficulty Metrics): The two difficulty proxies (unaccepted-answer rate; median time-to-answer) are presented without statistical tests for differences across topics/tools/algorithms and without discussion of confounders such as question visibility, asker reputation, or topic popularity. These metrics are central to the claim of 'varying levels of maturity and community support,' yet their validity as difficulty measures is untested.
minor comments (2)
  1. [Abstract] Abstract: 'Q-sharp' should be written as 'Q#' to match standard quantum-computing notation used elsewhere in the manuscript.
  2. [Results] Results section: Several figures mapping topics to prevalence or difficulty lack explicit legend entries linking back to the seven topic labels, reducing readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback, which highlights important opportunities to strengthen the methodological rigor of our empirical analysis. We address each major comment below and commit to revisions that directly respond to the concerns raised while preserving the paper's focus on Stack Overflow data.

read point-by-point responses

  1. Referee: [§3] §3 (Data Collection and Topic Modeling): The identification of exactly seven topics and their prevalence rankings (hybrid QC-classical and circuit implementation dominant) rests on an LDA-style model with no reported coherence scores (e.g., C_v or NPMI), no stability analysis across random seeds, and no human validation of topic labels. These omissions are load-bearing because the central claims about topic structure and dominance cannot be assessed for robustness without them.

    Authors: We agree that reporting model validation metrics is essential for assessing topic robustness. In the revised manuscript, we will add coherence scores (C_v and NPMI) for the chosen seven-topic model, conduct stability analysis by rerunning LDA across multiple random seeds and reporting topic overlap, and include human validation via two annotators independently labeling a stratified sample of posts with inter-annotator agreement statistics. These results will be presented in an expanded Section 3. revision: yes

  2. Referee: [§4] §4 (Tool and Algorithm Frequency Analysis): Claims that Qiskit/Q# dominate and that Grover's/Shor's algorithms are most referenced are derived solely from the 1,404 SO posts without any triangulation against external sources (GitHub issues, surveys, or other forums). This is load-bearing for the representativeness assumption stated in the abstract, as SO data alone may reflect platform-specific biases rather than broader developer engagement.

    Authors: Our analysis is intentionally scoped to Stack Overflow as the largest public developer Q&A repository for quantum computing discussions. We acknowledge that external triangulation would further support generalizability claims. In revision, we will expand the limitations section and Section 4 to explicitly discuss potential SO-specific biases (e.g., self-selection of question askers) and qualify the representativeness statement in the abstract. However, adding new data collection from GitHub or surveys exceeds the current study scope and resources. revision: partial

  3. Referee: [§5] §5 (Difficulty Metrics): The two difficulty proxies (unaccepted-answer rate; median time-to-answer) are presented without statistical tests for differences across topics/tools/algorithms and without discussion of confounders such as question visibility, asker reputation, or topic popularity. These metrics are central to the claim of 'varying levels of maturity and community support,' yet their validity as difficulty measures is untested.

    Authors: We agree that formal statistical testing and confounder discussion are needed to support the difficulty claims. In the revised Section 5, we will apply chi-squared tests to compare unaccepted-answer proportions and Kruskal-Wallis tests (with post-hoc Dunn tests) for median time-to-answer differences across topics, tools, and algorithms. We will also add a dedicated paragraph discussing potential confounders (visibility, reputation, popularity) and note where they may influence results, while retaining the two proxies as established measures in prior SO mining studies. revision: yes

Circularity Check

0 steps flagged

No circularity: purely observational empirical analysis

full rationale

This paper performs data mining on 1,404 Stack Overflow posts followed by standard topic modeling and descriptive statistics. No equations, fitted parameters, predictions, or derivations are present. All reported topics, tool frequencies, algorithm references, and difficulty metrics are computed directly from the mined corpus without any self-referential definitions or loops. The methodology relies on external data and established techniques (LDA-style modeling, simple percentages and medians) rather than any self-citation chain or ansatz. The open dataset further supports independent verification, confirming the results are not forced by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the assumption that Stack Overflow data and topic modeling reflect genuine developer engagement patterns in quantum computing.

axioms (2)
  • domain assumption The collected 1,404 posts represent a sufficient and unbiased sample of quantum computing developer discussions.
    Invoked when generalizing findings from forum posts to broader practitioner behavior.
  • domain assumption Topic modeling produces meaningful and stable groupings of discussion content.
    Central to identifying the seven main topics and their prevalence.

pith-pipeline@v0.9.0 · 5529 in / 1303 out tokens · 36640 ms · 2026-05-10T10:20:14.549249+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

33 extracted references · 33 canonical work pages

  1. [1]

    Ahmad Abdellatif, Diego Costa, Khaled Badran, Rabe Abdalkareem, and Emad Shihab. 2020. Challenges in chatbot development: A study of stack overflow posts. InProceedings of the 17th international conference on mining software repositories. 174–185

    work page 2020

  2. [2]

    Syed Ahmed and Mehdi Bagherzadeh. 2018. What do concurrency develop- ers ask about? a large-scale study using stack overflow. InProceedings of the 12th ACM/IEEE international symposium on empirical software engineering and measurement. 1–10

    work page 2018

  3. [3]

    PS Aithal. 2023. Advances and New Research Opportunities in Quantum Com- puting Technology by Integrating it with Other ICCT Underlying Technologies. International Journal of Case Studies in Business, IT and Education (IJCSBE)7, 3 (2023), 314–358

    work page 2023

  4. [4]

    Mehdi Bagherzadeh and Raffi Khatchadourian. 2019. Going big: a large-scale study on what big data developers ask. InProceedings of the 2019 27th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering. 432–442

    work page 2019

  5. [5]

    Anton Barua, Stephen W Thomas, and Ahmed E Hassan. 2014. What are devel- opers talking about? an analysis of topics and trends in stack overflow.Empirical software engineering19, 3 (2014), 619–654

    work page 2014

  6. [6]

    2009.Natural Language Processing with Python

    Steven Bird, Edward Loper, and Ewan Klein. 2009.Natural Language Processing with Python. O’Reilly Media Inc

    work page 2009

  7. [7]

    David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research3, Jan (2003), 993–1022

    work page 2003

  8. [8]

    Sebastian Bleier. [n. d.]. NLTK English stopwords list. https://gist.github.com/ sebleier/554280. Commonly used stopword list from the NLTK project

    work page

  9. [9]

    Francesco Bova, Avi Goldfarb, and Roger G Melko. 2021. Commercial applications of quantum computing.EPJ quantum technology8, 1 (2021), 2

    work page 2021

  10. [10]

    Google. 2022. Google. Cirq. https://quantumai.google/cirq/google/concepts

    work page 2022

  11. [11]

    Ryszard Horodecki, Paweł Horodecki, Michał Horodecki, and Karol Horodecki

    work page

  12. [12]

    Horodecki, P

    Quantum entanglement.Reviews of modern physics81, 2 (2009), 865. doi:10.1103/RevModPhys.81.865

    work page doi:10.1103/revmodphys.81.865 2009

  13. [13]

    Mobashir Husain, Muhammad Sohail Khan, Javed Ali Khan, Nek Dil Khan, Arif Khan, and Muhammad Azeem Akbar. 2025. Exploring Developers Discussion Forums for Quantum Software Engineering: A Fine-Grained Classification Ap- proach Using Large Language Model (ChatGPT). InProceedings of the 33rd ACM International Conference on the Foundations of Software Enginee...

    work page 2025

  14. [14]

    IBM. 2021. Qiskit is the open-source toolkit for useful quantum. https://qiskit. org/

    work page 2021

  15. [15]

    Arif Ali Khan, Aakash Ahmad, Muhammad Waseem, Peng Liang, Mahdi Fah- mideh, Tommi Mikkonen, and Pekka Abrahamsson. 2023. Software architecture for quantum computing systems: A systematic review.Journal of Systems and Software201 (2023), 111682. doi:10.1016/j.jss.2023.111682

    work page doi:10.1016/j.jss.2023.111682 2023

  16. [16]

    Arif Ali Khan, Boshuai Ye, Muhammad Azeem Akbar, Javed Ali Khan, Davoud Mougouei, and Xinyuan Ma. 2025. Mining q&a platforms for empirical evidence on quantum software programming.arXiv preprint arXiv:2503.05240(2025)

    work page arXiv 2025

  17. [17]

    Heng Li, Foutse Khomh, Moses Openja, et al . 2021. Understanding quantum software engineering challenges an empirical study on stack exchange forums and github issues. In2021 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 343–354

    work page 2021

  18. [18]

    Sam McArdle, Suguru Endo, Alán Aspuru-Guzik, Simon C Benjamin, and Xiao Yuan. 2020. Quantum computational chemistry.Reviews of Modern Physics92, 1 (Mar 2020), 015003. doi:10.1103/RevModPhys.92.015003

    work page doi:10.1103/revmodphys.92.015003 2020

  19. [19]

    Microsoft. 2021. Q# and the quantum development kit. https://azure.microsoft. com/en-us/resources/development-kit/quantum-computing

    work page 2021

  20. [20]

    Sarah Nadi, Stefan Krüger, Mira Mezini, and Eric Bodden. 2016. Jumping through hoops: Why do Java developers struggle with cryptography APIs?. InProceedings of the 38th International Conference on Software Engineering. 935–946

    work page 2016

  21. [21]

    Hoa T Nguyen, Muhammad Usman, and Rajkumar Buyya. 2024. Qfaas: A server- less function-as-a-service framework for quantum computing.Future Generation Computer Systems154 (2024), 281–300. doi:10.1016/j.future.2024.01.018

    work page doi:10.1016/j.future.2024.01.018 2024

  22. [22]

    2010.Quantum computation and quantum information

    Michael A Nielsen and Isaac L Chuang. 2010.Quantum computation and quantum information. Cambridge university press

    work page 2010

  23. [23]

    Mario Piattini, Guido Peterssen, Ricardo Pérez-Castillo, Jose Luis Hevia, Manuel A Serrano, Guillermo Hernández, Ignacio García Rodríguez de Guzmán, Claudio An- drés Paradela, Macario Polo, Ezequiel Murina, et al. 2020. The talavera manifesto for quantum software engineering and programming.. InQANSWER. 1–5

    work page 2020

  24. [24]

    Mario Piattini, Manuel Serrano, Ricardo Perez-Castillo, Guido Petersen, and Jose Luis Hevia. 2021. Toward a quantum software engineering.IT Professional 23, 1 (2021), 62–66. doi:10.1109/MITP.2020.3019522

    work page doi:10.1109/mitp.2020.3019522 2021

  25. [25]

    Michael Röder, Andreas Both, and Alexander Hinneburg. 2015. Exploring the Space of Topic Coherence Measures. InProceedings of the Eighth ACM International Conference on Web Search and Data Mining(Shanghai, China) (WSDM ’15). Association for Computing Machinery, New York, NY, USA, 399–408. doi:10.1145/2684822.2685324

    work page doi:10.1145/2684822.2685324 2015

  26. [26]

    Christoffer Rosen and Emad Shihab. 2016. What are mobile developers asking about? a large scale study using stack overflow.Empirical Software Engineering MT Sabzevari et al. 21, 3 (2016), 1192–1223

    work page 2016

  27. [27]

    Prateek Singh, Ritangshu Dasgupta, Anushka Singh, Harsh Pandey, Vikas Has- sija, Vinay Chamola, and Biplab Sikdar. 2024. A survey on available tools and technologies enabling quantum computing.IEEE access12 (2024), 57974–57991

    work page 2024

  28. [28]

    Stack Exchange. 2025. Stack Exchange Data Dump, December 2025. https: //archive.org/details/stackexchange_20251231. Accessed: 2026

    work page 2025

  29. [29]

    Maryam Tavassoli Sabzevari and Arif Ali Khan. 2026. Empirical Investigation of Quantum Computing Toolchains and Algorithms: Mining Stack Overflow Repository - Replication Package. doi:10.5281/zenodo.19604116

    work page doi:10.5281/zenodo.19604116 2026

  30. [30]

    Radim Řehůřek. 2024. Gensim: Topic Modelling for Humans. https:// radimrehurek.com/gensim/. Accessed: 2026

    work page 2024

  31. [31]

    Zhiyuan Wan, Xin Xia, and Ahmed E Hassan. 2019. What do programmers discuss about blockchain? a case study on the use of balanced lda and the reference architecture of a domain to capture online discussions about blockchain platforms across stack exchange communities.IEEE Transactions on Software Engineering 47, 7 (2019), 1331–1349

    work page 2019

  32. [32]

    2012.Experimentation in software engineering

    Claes Wohlin, Per Runeson, Martin Höst, Magnus C Ohlsson, Björn Regnell, Anders Wesslén, et al. 2012.Experimentation in software engineering. Vol. 236. Springer

    work page 2012

  33. [33]

    Jianjun Zhao. 2020. Quantum software engineering: Landscapes and horizons. arXiv preprint arXiv:2007.07047(2020)

    work page arXiv 2020