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REVIEW 3 major objections 4 minor 72 references

Decision Models for Selecting Architecture Patterns and Strategies in Quantum Software Systems

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper proposes six decision models for choosing architecture patterns and strategies in quantum software systems, grounded in mined practitioner data and a systematic literature review, and reports practitioner ratings suggesting the…

desk verdict A systematic, useful catalog of quantum architecture patterns—worth refereeing, but the 'can aid practitioners' claim rests on thin subjective evidence and needs revision. read the letter →

arxiv 2507.11671 v2 pith:2OKFWKMO submitted 2025-07-15 cs.SE

Mst Shamima Aktar , Peng Liang , Muhammad Waseem , Amjed Tahir , Mojtaba Shahin , Muhammad Azeem Akbar , Arif Ali Khan , Aakash Ahmad
show 2 more authors
Musengamana Jean de Dieu Ruiyin Li
This is my paper · ORCID
classification cs.SE
keywords quantumsoftwarearchitecturepatternsstrategiesdecisionmodelsqualityattributesquantum-classicalhybridsystemssystematicliteraturereviewempiricalengineering
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that choosing architecture patterns and strategies for quantum software does not have to be an ad hoc, expert-only activity: structured decision models can guide the choice. The authors build six such models, one for each of six design areas—Communication, Decomposition, Data Processing, Fault Tolerance, Integration and Optimization, and Algorithm Implementation—from mined open-source project discussions, developer Q&A posts, and a systematic literature review. Each model connects quality attributes such as security, scalability, and maintainability to concrete patterns and strategies, showing which attributes each pattern improves and which it degrades. Sixteen quantum software practitioners rated the models as largely familiar, understandable, and sufficient for making architecture decisions, and the paper takes this as evidence that the models can aid real-world selection.

What carries the argument

The central artifact is the decision model itself, rendered as a BPMN-style flow that maps a design area's required quality attributes (the problem space) to candidate architecture patterns and strategies (the solution space). Inclusive, exclusive, and parallel gateways represent decision flows; patterns appear as rounded rectangles labeled with the quality attributes they positively and negatively affect, and constraints appear as octagons attached by dashed arrows. For example, the Communication model routes a practitioner needing a unified quantum-classical interface to Quantum API Gateway, showing its positive effects on flexibility, modularity, interoperability, security, and scalability alongside its negative effects on availability, performance, and cost. The models were built from patterns identified in mined project data and developer Q&A posts plus patterns from a systematic literature review of 92 studies, then de-duplicated into 63 unique patterns and strategies across the six design areas.

What would settle it

A controlled experiment where matched teams design the same quantum software system with and without the decision models, judged blind on quality-attribute satisfaction by independent experts; if the model-using teams show no measurable advantage over a simple pattern list, the central claim fails.

Watch

Extended reading notes

Core claim

The central claim is that six proposed decision models can aid practitioners in selecting suitable architecture patterns and strategies for quantum software systems. The models organize a consolidated set of 63 unique patterns and strategies into six design areas, connect each pattern to the quality attributes it improves and degrades, and use BPMN-style gateways to route decisions based on conditions such as whether communication is point-to-point or collective, whether fault detection or correction is needed, or whether data should be encoded with basis, amplitude, or angle encoding. Evaluation by 16 practitioners produced high ratings for familiarity, understandability, and sufficiency across all six models, with Communication and Fault Tolerance rated most familiar and all six models judged sufficient by most participants. The paper concludes that the models offer a structured way to select reusable patterns and strategies in the complexity of hybrid quantum-classical environments.

Load-bearing premise

The claim rests on the assumption that 16 practitioners' self-reported familiarity, understandability, and completeness ratings are a reliable proxy for actual improvement in architecture decisions; no baseline or behavioural test shows the models outperform unaided judgment.

Editorial extensions

If this is right

  • Using the Communication model, a practitioner who needs a unified entry point to quantum backends is routed to Quantum API Gateway, with its positive and negative quality-attribute impacts shown side by side.
  • Using the Decomposition model, a team building a hybrid system can decide between Quantum-Classic Split and Quantum Microservices by checking which quality attributes matter more for their hardware and integration constraints.
  • Using the Fault Tolerance model, an architect can separate fault detection (Comparison, Voting, Sparing) from fault correction (Error Correction, Readout Error Mitigation, Gate Error Mitigation) rather than choosing one pattern in isolation.
  • Because the models and their underlying dataset are public, the mapping from quality attributes to patterns can be inspected, reproduced, and extended with new patterns as quantum technology evolves.
  • Following the paper's own plans, the models could be embedded in tool support that recommends patterns during architecture design.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The most decisive next test would be a controlled comparison: teams with and without the decision models designing the same system, with independent experts scoring the resulting architectures; the interview ratings alone cannot show that the models beat unaided judgment or a plain list of patterns.
  • Because the models encode today's pattern vocabulary, they will need ongoing updates as quantum hardware and hybrid integration practices change; the paper's static models do not yet include such a maintenance mechanism.
  • Tying the models to measurable quality-attribute outcomes, such as decoherence rates, gate counts, or integration latency, would let future users validate a pattern choice numerically instead of by familiarity.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes six decision models that map architectural patterns and strategies to quality attributes in six design areas of quantum software systems: Communication, Decomposition, Data Processing, Fault Tolerance, Integration and Optimization, and Algorithm Implementation. The models are constructed from a mining study of GitHub issues/pull requests and Stack Exchange posts, combined with a systematic literature review, then condensed and de-duplicated into 63 unique patterns/strategies organized as BPMN-style decision flows. The evaluation consists of semi-structured interviews with 16 quantum software practitioners, who rated their familiarity with the patterns, the models' understandability, and the models' completeness/sufficiency for architecture decision-making. The paper concludes that the decision models can aid practitioners in selecting suitable architecture patterns and strategies.

Significance. If the central claim were supported by the evidence, this would be a useful contribution: the quantum software engineering community currently lacks structured, quality-attribute-driven selection guidance for architecture patterns and strategies. The paper has clear strengths: the construction process is transparent, combining a mining study and an SLR; the decision model notation is carefully documented; the interview protocol and materials are included; and a replication package is provided. The study also explicitly acknowledges several validity threats and reports the demographic diversity of its small participant sample. However, the significance is currently limited by the evaluation design: the only evidence for the load-bearing claim that the models 'can aid practitioners in selecting' is self-reported familiarity, understandability, and completeness, with no behavioral test, baseline comparison, or objective measure of decision quality. The paper is therefore a solid model-proposal study whose central claim needs either additional validation or a more modest wording.

major comments (3)
  1. [Section 5.2 and Figure 10] The central claim in the Abstract and Section 8 is that the decision models 'can aid practitioners in selecting suitable patterns and strategies.' The evaluation in Section 5.2, however, measures only self-reported familiarity with patterns, perceived ease of understanding, and perceived completeness/sufficiency. None of these metrics operationalizes 'aid in selecting': a practitioner can find a model understandable and complete and still make the same architecture decision as without it. The interview questions quoted in Section 5.2 ask about ease of understanding and sufficiency, but not about whether the models changed or improved a real selection decision. Section 7.3 explicitly concedes that 'the reliance on subjective evaluations introduces potential bias' and that further validation across real-world scenarios is needed, so the Section 8 conclusion that the models 'can effectively support' practitioners goes beyond the reported evidence. This issue is load-bearing for the paper's main claim; it should be addressed either by adding a behavioral evaluation (e.g., a decision task with and without the models, scored by independent experts) or by substantially tempering the conclusion to claim perceived usefulness rather than demonstrated selection support.
  2. [Section 3.1.3 and Section 3.1.1, Phase 4] The reported pattern counts are internally inconsistent. Section 3.1.1, Phase 4 states that 164 patterns/strategies were identified, with 145 from 121 GitHub issues and 19 from 13 Stack Exchange posts. Section 3.1.3 states that the mining study yielded 162 patterns/strategies, comprising 145 from GitHub and 17 from Stack Exchange, and then lists 210 patterns from the SLR in a way that appears to make the 'comprising' clause sum to 145+17+210=372. The totals 164 versus 162, and 19 versus 17, are contradictory. The per-site breakdown is also inconsistent: Table 2 lists 13 related Stack Exchange posts (2+0+0+5+3+2+0+1+0 = 13), but Phase 4 reports 19 patterns from '5 from Stack Overflow, 13 from Quantum Computing Stack Exchange, and 1 from Computer Science Stack Exchange,' which does not match the related-post counts in Table 2. These inconsistencies weaken confidence in the traceability of the model construction and should be corrected and explained before the paper can be accepted.
  3. [Section 4.4 and Figure 6/Figure 7] The Fault Tolerance decision model section refers to 'Figure 7,' but the displayed figure for that model is numbered Fig. 6, while Fig. 7 is the Integration and Optimization model. Similarly, Section 4.1 states that 'Table 3 lists the patterns and strategies covered by the Communication Decision Model,' but the table actually appears as Table 9. These cross-reference errors, while not technical, make the paper difficult to verify and suggest the final manuscript needs a careful pass over figure and table numbering.
minor comments (4)
  1. [Section 7.1] A paragraph beginning 'To mitigate bias that may arise from relying on a single data source or method...' is duplicated verbatim within Section 7.1; the duplicate should be removed.
  2. [Section 7.3] The sentence 'Despite the modest number of participants, their qualitative feedback played a crucial role in refining the decision models...' appears twice in the same paragraph; one occurrence should be deleted.
  3. [Section 3.1.3] The sentence 'Specifically, from the mining study, we identified 162 patterns/strategies, comprising 145 from 121 GitHub issues & PRs, 17 from 13 Stack Exchange posts, and 210 patterns from 92 reviewed studies' is grammatically ambiguous about whether the 210 SLR patterns are part of the mining-study total or the overall total; the intended grouping should be restated clearly.
  4. [Section 3.1.1, Phase 4] The text reports '87.8% of participants' as familiar with most or all patterns in the Algorithm Implementation model, but Figure 10 shows 56.3% + 31.3% = 87.6%, and the individual response counts (9 and 5 of 16) give 87.5%; the percentage should be corrected to match the data.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the decision models are induced from external mining and SLR data, and the interview evaluation, while subjective, is not definitionally tied to the model content.

full rationale

The derivation chain is: collect patterns, strategies, and quality attributes from GitHub, Stack Exchange, and a systematic literature review (Sections 3.1.1 and 3.1.2); integrate and deduplicate them into six decision models (Section 3.1.3 and Section 4); and evaluate the models through semi-structured interviews with 16 practitioners (Section 5). No step defines a model output in terms of the evaluation outcome, and no fitted parameter is renamed as a prediction. The six design areas are said to originate from recurring architectural challenges reported in the literature [10, 30], and the authors' prior empirical study [7] is used only to motivate the focus rather than to force the model contents; the models themselves are populated from the current mining study and SLR. The evaluation asks practitioners for familiarity, understandability, completeness, and usefulness, which are self-reports about the presented artifacts rather than a behavioral test of decision quality, and Section 7.3 concedes that 'the reliance on subjective evaluations introduces potential bias.' That is a validity limitation, not a circularity: the claim that the models can aid practitioners is not equivalent by construction to the input data, and there are no equations or fitted parameters into which the conclusion collapses. Numerical inconsistencies (e.g., 164 versus 162 patterns, and 19 versus 17 Stack Exchange patterns) bear on data quality but do not establish circularity. Accordingly, no circular step is identified.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No numeric free parameters are present; the models are qualitative artifacts. The main unproved premises are the representativeness of the mined data, the completeness of the six design areas, the correctness of the qualitative QA-impact attributions, and the validity of practitioner self-report as evidence of usefulness. No new physical or conceptual entities beyond the decision models themselves are introduced.

assumptions (4)
  • domain assumption The six design areas (Communication, Decomposition, Data Processing, Fault Tolerance, Integration and Optimization, Algorithm Implementation) are the critical architectural concerns for quantum software systems.
    Adopted from recurring challenges reported in the literature (Sections 1 and 3), but no empirical demonstration is given that these six are exhaustive or the most important for all quantum software projects.
  • domain assumption GitHub issues and pull requests, Stack Exchange posts, and SLR primary studies are representative of real quantum software architecture decisions.
    The paper's own Section 7.1 acknowledges these sources 'may not reflect the full spectrum of quantum software development practices', so representativeness is assumed rather than established.
  • domain assumption The authors' thematic coding and naming unification correctly identify, consolidate, and attribute quality attributes to patterns and strategies.
    Data extraction and coding were performed primarily by the first author with discussion among co-authors (Sections 3.1.1 and 3.1.2), and no inter-rater reliability statistic is reported.
  • domain assumption Practitioners' self-reported familiarity, understandability, and sufficiency are valid evidence that the models aid selection.
    The interview instrument (Section 3.3) measures perceptions rather than observed decision quality; Section 7.3 explicitly notes reliance on subjective evaluations.

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Pith. "Pith review of Decision Models for Selecting Architecture Patterns and Strategies in Quantum Software Systems." pith.science (2026). https://pith.science/paper/2OKFWKMO

@misc{pith2026250711671,
  author       = {Pith},
  title        = {Pith review of: Decision Models for Selecting Architecture Patterns and Strategies in Quantum Software Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2OKFWKMO}},
  note         = {Machine review of arXiv:2507.11671}
}
read the original abstract

Quantum software represents disruptive technologies in terms of quantum-specific software systems, services, and applications - leverage the principles of quantum mechanics via programmable quantum bits (Qubits) that manipulate quantum gates (QuGates) - to achieve quantum supremacy in computing. Quantum software architecture enables quantum software developers to abstract away implementation-specific details (i.e., mapping of Qubits and QuGates to high-level architectural components and connectors). Architectural patterns and strategies can provide reusable knowledge and best practices to engineer quantum software systems effectively and efficiently. However, quantum software practitioners face significant challenges in selecting and implementing appropriate patterns and strategies due to the complexity of quantum software systems and the lack of guidelines. To address these challenges, this study proposes decision models for selecting patterns and strategies in six critical design areas in quantum software systems: Communication, Decomposition, Data Processing, Fault Tolerance, Integration and Optimization, and Algorithm Implementation. These decision models are constructed based on data collected from both a mining study (i.e., GitHub and Stack Exchange) and a Systematic Literature Review, which were used to identify relevant patterns and strategies with their involved Quality Attributes (QAs). We then conducted semi-structured interviews with 16 quantum software practitioners to evaluate the familiarity, understandability, completeness, and usefulness of the proposed decision models. The results show that the proposed decision models can aid practitioners in selecting suitable patterns and strategies to address the challenges related to the architecture design of quantum software systems. The dataset is available at [6], allowing the community to reproduce and build upon our findings.

Figures

Figures reproduced from arXiv: 2507.11671 by the authors.

Figure 1
Figure 1. Overview of the research process identifying relevant quantum-related projects on GitHub. After evaluating different options, we selected “quantum” and “quantum computing” as our primary keywords. The search was performed across multiple project components, including names, topics, descriptions, and README files. The keyword “quantum” returned a total of 74,432 projects, whereas “quantum computing” yielded significa… view at source ↗
Figure 2
Figure 2. Notations used in the decision models from established questionnaires used in prior research (e.g., [57], [36], [66], [56]) to align with the objectives of this study. Our interview questionnaire was conducted using Google Docs and is publicly available online12. Additionally, a detailed document describing the architectural patterns and strategies with practical examples was shared with participants prior to the in… view at source ↗
Figure 3
Figure 3. Decision model for selecting architecture patterns and strategies for communication [PITH_FULL_IMAGE:figures/full_fig_p022_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Decision model for selecting architecture patterns and strategies for decomposition [PITH_FULL_IMAGE:figures/full_fig_p024_4.png]
Figure 5
Figure 5. Figure 5: Decision model for selecting architecture patterns and strategies for data processing [PITH_FULL_IMAGE:figures/full_fig_p027_5.png]
Figure 6
Figure 6. Figure 6: Decision model for selecting architecture patterns and strategies for fault tolerance [PITH_FULL_IMAGE:figures/full_fig_p028_6.png]
Figure 7
Figure 7. Figure 7: Decision model for selecting architecture patterns and strategies for integration and optimization [PITH_FULL_IMAGE:figures/full_fig_p031_7.png]
Figure 8
Figure 8. Figure 8: Decision model for selecting architecture patterns and strategies for algorithm implementation [PITH_FULL_IMAGE:figures/full_fig_p033_8.png]
Figure 9
Figure 9. Figure 9: Demographic details of the interview participants [PITH_FULL_IMAGE:figures/full_fig_p036_9.png]
Figure 10
Figure 10. Figure 10: Overview of practitioners’ responses for familiarity, understandability, and completeness of the [PITH_FULL_IMAGE:figures/full_fig_p037_10.png]

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.