Pith. sign in

REVIEW 5 major objections 5 minor 42 references

From Pattern Detection to Composition Analysis in Quantum Software

T0 review · 5 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Quantum-pattern counts mix developer calls with framework internals; the expanded detector reaches micro-F1 0.712 on a held-out framework.

desk verdict Composition graphs and the directness ratio are the real contribution; the Qrisp F1 claim is shakier than it looks but the paper is honest and reproducible. read the letter →

arxiv 2608.10882 v1 pith:F7IXUS7H submitted 2026-08-11 cs.SE quant-ph

Neilson Carlos Leite Ramalho , Erico Augusto da Silva , Anthony Accioly , Higor Amario de Souza , Marcos Lordello Chaim This is my paper · ORCID
classification cs.SEquant-ph
keywords quantumsoftwarepatternspatternminingcompositiongraphsvocabularyexpansionknowledgebaseLLMensembleQrispevaluationengineering
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

Quantum software patterns are meant to be reusable building blocks, but before this paper there was little measurement of whether detection counts reflected what developers actually chose to call, and no accuracy check of the detector at all. The paper tries to establish two things: that a pattern-mining pipeline can generalize to a framework outside its knowledge base when it first learns that framework's vocabulary from its own source code, and that call graphs among framework components let an analyst separate deliberate developer calls from patterns that enter a program through framework-internal composition. On Qrisp, a framework deliberately excluded from the knowledge base, the expanded pipeline reaches micro-F1 0.712 (precision 0.913, recall 0.583) against 0.449 without vocabulary expansion. Applied to 80 open-source projects, the pipeline detects all 23 represented patterns and shows that counts concentrate in utility and variational patterns, while the composition graphs show that frameworks expose the same pattern at different granularities. A sympathetic reader would care because the result turns pattern adoption from a counting exercise into a structural analysis of how quantum frameworks are built.

What carries the argument

Two mechanisms carry the argument. The first is two-phase vocabulary expansion: before scanning notebooks, qpa extracts non-private functions and classes from each project's source, associates them with patterns either by documentation similarity at a threshold of $\geq 0.70$ to a seed component or by name matching against pattern names, and merges the results into a project-specific dynamic KB that is combined with the seed KB. This is what lets a detector built on Qiskit, PennyLane, and Classiq vocabulary recognize Qrisp's algorithm-centric names. The second is the projected composition graph, stored in a graph database, in which usage files point to KB components through CALLS edges and reach further components through USES edges; the directness ratio $\rho(c)=\mathrm{direct}(c)/\mathrm{reach}(c)$ then quantifies whether a component is usually written by hand or pulled in internally.

What would settle it

Re-run the Qrisp evaluation after an independent source-code review of all 286 component-to-pattern assignments; if any corrected labels move components between patterns, the reported micro-F1 of 0.712 and every directness ratio change. A cheaper check is to run the same two-phase pipeline on a second held-out framework with a different naming convention and see whether the vocabulary-expansion step again raises recall from about 0.3 to above 0.5.

Watch

Extended reading notes

Core claim

The paper's central claim, on its own terms: the qpa pipeline, extended with two additional matching channels and a two-phase vocabulary expansion, is the first version of the tool whose output is measured against a manual ground truth, and it scores micro-F1 0.712 on Qrisp tutorial notebooks, with the expansion step alone raising micro-F1 from 0.449 by lifting recall from 0.306 to 0.583. The companion conceptual claim is that composition graphs built from CALLS and USES edges among the 286 knowledge-base components show that a detection count is not a neutral measure: Qiskit's most common pattern is driven by synthesis routines no developer writes by hand, while PennyLane's counts mostly reflect direct developer choices, and the same pattern (Grover is the worked example) appears as a single block in some frameworks and as a chain of named primitives in others.

Load-bearing premise

The load-bearing premise is that each of the 286 framework components was correctly assigned to exactly one of the 23 patterns, a manual judgment the paper itself shows to be fallible when its LLM audit found four components mislabeled as Schmidt Decomposition.

Editorial extensions

If this is right

  • If the Qrisp result generalizes, pattern detection on a new quantum framework should start by building a dynamic vocabulary from that framework's own source, rather than relying only on a static knowledge base.
  • Raw pattern counts should be reported together with directness ratios; otherwise Qiskit-style frameworks, where low-level synthesis routines dominate counts without any developer call, will look artificially pattern-heavy.
  • The composition graphs make co-occurrence interpretable: a pair of patterns can co-occur because one is built from the other, because both share a lower-level routine, or because a developer combined them deliberately, so future adoption studies should distinguish these three cases.
  • The LLM ensemble's audit corrected four mislabeled Schmidt Decomposition components, so the released knowledge base and all results derived from it reflect the corrected 23-pattern set rather than the earlier 24-pattern set.
  • The release of the pipeline, knowledge base, and graphs means the Qrisp evaluation and the directness-ratio tables can be reproduced or checked without re-running the paid LLM service.

Reading between the lines

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

  • A natural next test, not run in the paper, is to hold out a second framework such as Cirq or TensorFlow Quantum and check whether the expansion step produces a comparable recall gain; if the gain is specific to Qrisp, the transfer claim is weaker.
  • The directness ratio could be adopted outside quantum software as a metric for framework transparency, since it measures how much of a library's apparent pattern usage is actually hidden plumbing.
  • Because the LLM ensemble disagreed with the manual knowledge base on roughly a quarter of components, an independent full re-labeling of the 286 components would likely shift some counts and ratios even if the headline evaluation stands.
  • The recurring QRAM/QROM disagreement noted in the paper suggests the 61-pattern catalog may need a dedicated addressable-quantum-memory pattern, and adding one would change the knowledge base and the detection results.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. This paper extends the qpa quantum-pattern mining pipeline and uses it to study pattern adoption and composition in open-source quantum software. It expands the knowledge base from 217 components across three frameworks to 286 components across five, adds two matching channels and a two-phase vocabulary-expansion step, and evaluates the extended detector on Qrisp, a framework excluded from the KB, reporting micro-F1 0.712 with expansion versus 0.449 without. It then builds projected call graphs for the five frameworks, defines a directness ratio to separate direct developer calls from framework-internal reach, and uses graph structure to explain pattern co-occurrence and compare the granularity at which frameworks expose patterns. The paper finds all 23 KB-represented patterns in 80 open-source projects and releases the pipeline, KB, datasets, and graph database.

Significance. If the evaluation held, the paper would be a useful step in empirical quantum software engineering: it provides the first quantitative detector evaluation on an out-of-distribution framework, introduces a composition analysis that distinguishes direct from indirect pattern use, and ships reproducible artifacts. Strengths include pinned framework versions, an open-source pipeline with Docker Compose setup, released ground-truth and usage datasets, and an LLM ensemble with cached outputs used to audit KB labels. The directness ratio and projected call graphs are simple, transferable concepts. However, the headline F1 result and several composition conclusions rest on measurement choices that need strengthening before the claims are fully supported.

major comments (5)
  1. [Section 3.6.1, Table 2] The central RQ1 result is not robustly supported by the reported ground truth. The 36-pair gold set has Light's kappa of 0.316 and exact-set agreement of only 4/22 notebooks, and the disagreements listed (Block Encoding as LCU versus Data/Matrix Encoding, Domain Specific Application versus specific sub-patterns in Shor/Sudoku/TSP, and the pattern set of HHL.py) directly determine the true-positive counts for Grover, QPE, LCU, Amplitude Amplification, and Hamiltonian Simulation. Because the metric is micro-averaged over only 36 pairs and four patterns have zero true positives, a plausible alternative consensus label set could change aggregate recall by several points, potentially bringing the 0.712 micro-F1 close to or below the 0.449 baseline. The paper should report per-rater performance, confidence intervals, and a sensitivity analysis over alternative labelings, or rebuild the ground truth with more raters and stricter adjudication.
  2. [Section 3.3 and Section 3.6.1] The evaluation protocol is transductive rather than a held-out generalization test. The "two-phase pipeline" constructs a dynamic KB from Qrisp's own source code before scanning Qrisp notebooks, so the improvement from 0.449 to 0.712 measures how much target-source vocabulary helps, not how well vocabulary expansion generalizes to an unseen framework's naming. The paper should state this explicitly and, for RQ1, either evaluate the expansion on a framework whose source was not used for the dynamic KB, or frame the claim only as adaptation to the target. This distinction matters because the abstract and RQ1 answer describe the result as a gain from "vocabulary expansion" on a framework "not present in the knowledge base."
  3. [Section 4.3.2, Table 8, Figure 7] The RQ3 cross-framework comparison is partially an artifact of graph scope. The paper states that Qiskit Algorithms and Qiskit Machine Learning appear flat because their calls to core components are recorded in a separate graph, and that Qiskit Algorithms has no hubs by construction. Comparing depth, hub share, and Grover decomposition across frameworks under these different scopes is therefore not apples-to-apples: the "same pattern at different granularity" claim in the RQ3 answer conflates implementation choices with package-boundary decisions. The analysis should either follow calls across package boundaries (listed as future work) or restrict the granularity comparison to frameworks with comparable graph scopes.
  4. [Section 3.1.2 and Section 3.4] All downstream counts and composition measures inherit the manual single-label assignment of 286 components to patterns, and the paper itself demonstrates that these labels are fallible: the LLM re-check of the original 217-component KB found four Schmidt Decomposition entries that should have been assigned to three other patterns, and the authors then reran every analysis. This shows that label errors can propagate into all reported results. Because no independent audit is reported for the remaining entries, the paper should add a robustness analysis (for example, recomputing Table 1, Table 2, and the directness ratios under per-rater label sets or under a conservative perturbation of the KB) or provide a concrete argument for why such errors would not change the qualitative conclusions.
  5. [Section 3.5.1 and Section 3.5.3] The adoption claim that "all 23 patterns occur in practice" is based solely on qpa's detections on the 80-project corpus; the only manual validation is the 22-notebook Qrisp evaluation, which is not drawn from that corpus. Moreover, 440 of 611 detections (72%) come from dynamic-KB entries extracted from the selected projects themselves, so the scan can confirm patterns by matching a project's own vocabulary to itself. The paper should either sample and manually validate detections in the 80-project corpus or explicitly label the adoption counts as unvalidated estimates, and it should quantify how many of the 23 patterns are detected only through dynamic-KB entries.
minor comments (5)
  1. [Table 2 caption] The caption should explain that "–" means the metric is undefined because the pattern has no true positives (or no positive predictions), and it should state how the macro-average treats undefined per-pattern scores.
  2. [Section 3.4] The sentence "The ensemble labeled 214 components and reproduced the existing label for 160 of them" should clarify the denominator: 160 of 214 classified components, not 160 of the original 217, and whether the four Schmidt Decomposition components were among the 214.
  3. [Section 3.6.2] The statement that "stronger models would likely raise these numbers" is unsupported speculation; it should be removed or explicitly marked as a conjecture.
  4. [Section 4.1 and Table 7] The units differ between the adoption analysis (notebooks/files in the 80-project corpus) and the composition analysis (usage files, including tests and framework-provided examples); Table 7 should state explicitly which file population is being counted.
  5. [Throughout] There are several typographical issues, including "T able 1", "V ocabulary expansion", and "SW AP Test" with a spurious space; these should be fixed in the final version.

Circularity Check

1 steps flagged · score 3.0 of 10

Partial circularity in the Qrisp F1 claim: the dynamic KB is built from Qrisp's own library source, so the reported micro-F1 improvement is an adapted-pipeline result; the composition analysis is independent.

  1. fitted input called prediction [Section 3.3 (Vocabulary expansion) and Section 3.6.1 (Evaluation on Qrisp), RQ1 answer]
    "We report results for the full two-phase pipeline (seed KB plus the Qrisp dynamic KB built from the library source code as described in Section 3.3)... Vocabulary expansion raises recall from 0.306 to 0.583 because it learns the target framework's names from its own library before the scan."

    The headline micro-F1 gain is produced by a pipeline whose dynamic KB is generated from Qrisp's own library source before the Qrisp notebooks are scanned. Section 3.3 defines the expansion as extracting functions and classes from the selected project's Python source and assigning them patterns by documentation similarity or by whether the name contains a pattern's words; the Qrisp notebooks then call the same library functions, so the added detections are largely the names the expansion itself contributed. The 0.712 versus 0.449 comparison therefore shows that adding target-framework vocabulary to the KB improves matching on that target framework, which is built into the method as designed, rather than demonstrating held-out detection accuracy on a truly unseen framework.

full rationale

The composition analysis in Section 4 is self-contained and does not reduce to its inputs: the graphs are built from pinned framework source code and KB-classified components, and the RQ2 and RQ3 conclusions follow from graph reachability, the CALLS versus USES distinction, and the directness ratio definition. No composition result is equivalent to a fitted parameter or to a self-citation. The study does reuse the catalog and KB from prior work [12] by overlapping authors, but that reuse is not load-bearing circularity: the KB was audited with an LLM ensemble and source-level review, and the new evaluation and graph analyses are carried out in this paper. The one partially circular step is the Qrisp evaluation: the dynamic KB is built from Qrisp's own library source before scanning Qrisp notebooks, so the reported F1 reflects a transductive, adapted pipeline rather than a blind held-out test. This is disclosed in the paper, and the exact score depends on the low-agreement manual ground truth (Light's kappa 0.316, 18.2% exact-set match), which is a measurement-validity concern rather than definitional circularity. Overall, the central compositional claims are independent, and only the headline F1 comparison has a constructed, partially self-referential character; hence a score of 3 rather than higher.

Assumptions & free parameters 4 free parameters · 5 assumptions · 3 invented entities

The numerical results depend on several manually chosen thresholds and on the single-label KB assignment, while the composition analysis depends on the projected-graph construction and the noisy Qrisp ground truth. The paper discloses most of these choices in the methodology and threats sections.

free parameters (4)
  • Matching channel thresholds = 0.88 (name), 0.78 (summary), 0.76 (title), 0.80 (pattern description)
    Section 3.3: manually chosen cosine-similarity cutoffs that determine which matches are recorded as detections, affecting precision, recall, and all downstream counts.
  • Vocabulary expansion similarity threshold = 0.70
    Section 3.3: a project function or class is added to the dynamic KB when its docstring similarity to a seed component is at least 0.70; this threshold directly drives the Qrisp recall improvement from 0.306 to 0.583.
  • Keyword veto parameters = top 30 tokens, min frequency 2, max 2 patterns per token
    Section 3.3: text-based candidate matches are kept only if a file contains a discriminating keyword from the pattern's list; these heuristics control false positives in the summary, title, and pattern-description channels.
  • Directness ratio denominator cutoff = reach(c) >= 2
    Section 4.3.1: components reached by fewer than two files are excluded from rho, an analytic choice that shapes Tables 5 and 6 but does not affect the main conclusions.
assumptions (5)
  • domain assumption Each framework component is assigned to exactly one pattern.
    Section 3.1.2: used throughout detection and composition despite acknowledged ambiguities such as the QRAM family split across Function Table, Data Encoding, and Initialization.
  • domain assumption The 61-pattern catalog from the prior study is a valid taxonomy for quantum software.
    Section 3.1: inherited from prior work and not independently validated here; the paper itself suggests the catalog may lack a dedicated addressable-memory pattern.
  • domain assumption Jupyter notebooks converted to Python scripts are a valid proxy for practitioner quantum code.
    Section 3.2 and Section 5 (external validity): the adoption analysis is based on notebooks from selected GitHub projects, which are not production-scale applications.
  • domain assumption AST-extracted calls in framework source accurately represent how components are composed.
    Section 4.1: the projected graph links KB components across non-KB functions, so some edges represent paths of calls rather than literal direct calls, and the graph is not a subgraph of the framework's full call graph.
  • domain assumption The manual Qrisp ground truth, despite low inter-rater agreement, is treated as the reference for F1.
    Section 3.6.1: Light's kappa is 0.316 and exact set match is only 18.2% (4/22); disagreements were resolved by discussion to produce the 36-pair ground truth.
invented entities (3)
  • Projected call graph independent evidence
    purpose: Represents calls among KB-classified framework components, with edges added for paths through non-KB helpers, to analyze how patterns are assembled.
    Defined in Section 4.1; constructed from pinned framework source and released in the replication package, so it can be independently rebuilt.
  • Directness ratio rho(c) independent evidence
    purpose: Quantifies whether a component is usually written by hand (rho near 1) or pulled in transitively (rho near 0).
    Defined in Equation 1 (Section 4.3.1); computed from the released graphs and usage files, providing a falsifiable handle on the direct versus indirect distinction.
  • Dynamic knowledge base independent evidence
    purpose: Per-project vocabulary of functions and classes with pattern labels, used to expand matching beyond the seed KB.
    Section 3.3; the extraction procedure is described and reproducible, and its outputs are included in the released dataset.

how reviews work

0 comments
Cite this review

Pith. "Pith review of From Pattern Detection to Composition Analysis in Quantum Software." pith.science (2026). https://pith.science/paper/F7IXUS7H

@misc{pith2026260810882,
  author       = {Pith},
  title        = {Pith review of: From Pattern Detection to Composition Analysis in Quantum Software},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/F7IXUS7H}},
  note         = {Machine review of arXiv:2608.10882}
}
read the original abstract

Quantum software patterns provide high-level abstractions for building quantum programs, but there is still little empirical evidence on how they are adopted in practice. In prior work, we extended an existing quantum-pattern atlas into a 61-pattern catalog, created a knowledge base that links framework components to those patterns, and built a tool that mines pattern implementations from open-source code. We applied this tool on 80 projects and find that all 23 patterns occur in practice. In this work, we extend the tool with two additional matching channels and a vocabulary expansion step, and execute a quantitative evaluation of its accuracy on Qrisp, a framework not present in the knowledge base, reaching a micro-F1 of 0.712 against 0.449 without the expansion step. We then construct composition graphs that record calls among the high-level framework components associated with patterns and store them in a graph database. We use these graphs to examine how pattern implementations are assembled inside each framework, why patterns co-occur, and how much of a pattern's detection count comes from components called directly by developers rather than introduced through internal framework calls. We release qpa, an open-source mining pipeline, together with the knowledge base, which maps 286 framework components across five sources to the pattern catalog, maintained with the support of an LLM ensemble that classifies newly extracted components, and the resulting pattern usage dataset, to support reproducible studies on the adoption and evolution of quantum patterns.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

42 extracted references · 29 canonical work pages

  1. [1]

    Springer, Cham (2019)

    Hidary, J.D.: Quantum Computing: An Applied Approach. Springer, Cham (2019)

    work page 2019

  2. [2]

    Ali, S., Yue, T., Abreu, R.: When software engineering meets quantum computing. Commun. ACM65(4), 84–88 (2022) https://doi.org/10.1145/3512340

    doi:10.1145/3512340 2022

  3. [3]

    Addison-Wesley Longman Publishing Co., Inc., USA (1994)

    Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns: Elements of Reusable Object-oriented Software. Addison-Wesley Longman Publishing Co., Inc., USA (1994)

    work page 1994

  4. [4]

    Murillo, J.M., Garcia-Alonso, J., Moguel, E., Barzen, J., Leymann, F., Ali, S., Yue, T., Arcaini, P., P´ erez-Castillo, R., Guzm´ an, I., Piattini, M., Ruiz-Cort´ es, A., Brogi, A., Zhao, J., Miranskyy, A., Wimmer, M.: Quantum software engineering: Roadmap and challenges ahead34(5) (2025) https://doi.org/10.1145/3712002

    doi:10.1145/3712002 2025

  5. [5]

    In: Quan- tum Technology and Optimization Problems

    Leymann, F.: Towards a pattern language for quantum algorithms. In: Quan- tum Technology and Optimization Problems. Lecture Notes in Computer Science (LNCS), vol. 11413, pp. 218–230. Springer, Cham (2019). https://doi.org/10. 1007/978-3-030-14082-3 19

    work page 2019

  6. [6]

    In: Proceedings of the 30th Conference on Pattern Languages of Programs

    Bechtold, M., Barzen, J., Beisel, M., Leymann, F., Weder, B.: Patterns for quan- tum circuit cutting. In: Proceedings of the 30th Conference on Pattern Languages of Programs. PLoP ’23. The Hillside Group, USA (2023)

    work page 2023

  7. [7]

    In: Proceedings of the 2025 14th International Conference on Software and Com- puter Applications

    Vietz, D., Barzen, J., Beisel, M., Leymann, F., Stiliadou, L., Weder, B.: Har- nessing patterns to support the development of hybrid quantum applications. In: Proceedings of the 2025 14th International Conference on Software and Com- puter Applications. ICSCA ’25, pp. 40–46. Association for Computing Machinery, New York, NY, USA (2025). https://doi.org/10...

    work page arXiv 2025

  8. [8]

    In: Proceedings of the 14th International Conference 39 on Pervasive Patterns and Applications (PATTERNS 2022), pp

    Beisel, M., Barzen, J., Leymann, F., Truger, F., Weder, B., Yussupov, V.: Patterns for Quantum Error Handling. In: Proceedings of the 14th International Conference 39 on Pervasive Patterns and Applications (PATTERNS 2022), pp. 22–30. Xpert Publishing Services (XPS), Barcelona, Spain (2022)

    work page 2022

Show all 42 references
  1. [9]

    In: Proceedings of the 18th International Conference on Software Technologies - ICSOFT, pp

    Georg, D., Barzen, J., Beisel, M., Leymann, F., Obst, J., Vietz, D., Weder, B., Yussupov, V.: Execution patterns for quantum applications. In: Proceedings of the 18th International Conference on Software Technologies - ICSOFT, pp. 258–

  2. [10]

    In: Proceedings of the 15th International Conference on Pervasive Patterns and Applications (PATTERNS 2023), pp

    B¨ uhler, F., Barzen, J., Beisel, M., Georg, D., Leymann, F., Wild, K.: Patterns for Quantum Software Development. In: Proceedings of the 15th International Conference on Pervasive Patterns and Applications (PATTERNS 2023), pp. 30–

    2023

  3. [11]

    In: Proceedings of the 17 th International Conference on Pervasive Patterns and Applications (PATTERNS 2025), pp

    Stiliadou, L., Barzen, J., Beisel, M., Leymann, F., Weder, B.: Patterns for Quan- tum Machine Learning. In: Proceedings of the 17 th International Conference on Pervasive Patterns and Applications (PATTERNS 2025), pp. 7–14. Xpert Publishing Services (XPS), Valencia, Spain (2025)

    2025

  4. [12]

    In: Proceedings of the 23rd International Mining Software Repositories Conference

    Ramalho, N.C.L., Silva, E.A., Souza, H.A., Chaim, M.L.: Mining quantum soft- ware patterns in open-source projects. In: Proceedings of the 23rd International Mining Software Repositories Conference. MSR ’26. ACM, New York, NY, USA (2026). https://arxiv.org/abs/2601.06281

    2026

  5. [13]

    Original site offline; pattern list republished at https://qpa-quantum-patterns.web.app/

    PlanQK: Quantum Computing Patterns. Original site offline; pattern list republished at https://qpa-quantum-patterns.web.app/. https://patternatlas. planqk.de/pattern-languages/af7780d5-1f97-4536-8da7-4194b093ab1d Accessed 2025-09-28

    2025

  6. [14]

    https://github.com/ qiskit-community

    Community, Q.: Qiskit Community GitHub Repository. https://github.com/ qiskit-community. Accessed: June 2025

    2025

  7. [15]

    https://arxiv.org/abs/1811.04968 40

    Bergholm, V., Izaac, J., Schuld, M., Gogolin, C., Ahmed, S., Ajith, V., Alam, M.S., Alonso-Linaje, G., AkashNarayanan, B., Asadi, A., Arrazola, J.M., Azad, U., Banning, S., Blank, C., Bromley, T.R., Cordier, B.A., Ceroni, J., Delgado, A., Matteo, O.D., Dusko, A., Garg, T., Gua...

    2022 arXiv

  8. [16]

    https://doi.org/10

    Goldfriend, T., Reichental, I., Naveh, A., Gazit, L., Yoran, N., Alon, R., Ur, S., Lahav, S., Cornfeld, E., Elazari, A., Emanuel, P., Harpaz, D., Michaeli, T., Erez, N., Preminger, L., Shapira, R., Garcell, E.M., Samimi, O., Kisch, S., Hallel, G., Kishony, G., Wingerden, V.v.,...

    2024 doi arXiv

  9. [17]

    https://doi.org/10.48550/arXiv

    Javadi-Abhari, A., Treinish, M., Krsulich, K., Wood, C.J., Lishman, J., Gacon, J., Martiel, S., Nation, P.D., Bishop, L.S., Cross, A.W., Johnson, B.R., Gambetta, J.M.: Quantum computing with Qiskit (2024). https://doi.org/10.48550/arXiv. 2405.08810

    2024 doi arXiv

  10. [18]

    arXiv e-prints, 2505–17756 (2025) arXiv:2505.17756 [quant- ph]

    Sahin, M.E., Altamura, E., Wallis, O., Wood, S.P., Dekusar, A., Millar, D.A., Imamichi, T., Matsuo, A., Mensa, S.: Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators. arXiv e-prints, 2505–177...

    2025 arXiv

  11. [19]

    https://arxiv.org/abs/2406.14792

    Seidel, R., Bock, S., Zander, R., Petriˇ c, M., Steinmann, N., Tcholtchev, N., Hauswirth, M.: Qrisp: A Framework for Compilable High-Level Programming of Gate-Based Quantum Computers (2024). https://arxiv.org/abs/2406.14792

    2024 arXiv

  12. [20]

    Online documentation

    Docker: Docker Compose. Online documentation. Accessed July 29, 2026 (2026). https://docs.docker.com/compose/

    2026

  13. [21]

    https://arxiv.org/abs/2411.10487

    Klymenko, M., Hoang, T., Xu, X., Xing, Z., Usman, M., Lu, Q., Zhu, L.: Archi- tectural Patterns for Designing Quantum Artificial Intelligence Systems (2024). https://arxiv.org/abs/2411.10487

    2024 arXiv

  14. [22]

    https://arxiv.org/ abs/2507.11671

    Aktar, M.S., Liang, P., Waseem, M., Tahir, A., Shahin, M., Akbar, M.A., Khan, A.A., Ahmad, A., Dieu, M.J., Li, R.: Decision Models for Selecting Architecture Patterns and Strategies in Quantum Software Systems (2025). https://arxiv.org/ abs/2507.11671

    2025 arXiv

  15. [23]

    Computing 107, 111 (2025) https://doi.org/10.1007/s00607-025-01467-2

    Fern´ andez-Osuna, M., P´ erez-Castillo, R., Cruz-Lemus, J.A., Baczyk, M., Piattini, M.: Exploring design patterns in quantum software: a case study. Computing 107, 111 (2025) https://doi.org/10.1007/s00607-025-01467-2

    2025 doi

  16. [24]

    In: Proceedings of the 5th ACM/IEEE International Workshop on Quantum Soft- ware Engineering

    Perez-Castillo, R., Fernandez-Osuna, M., Cruz-Lemus, J.A., Piattini, M.: A preliminary study of the usage of design patterns in quantum software. In: Proceedings of the 5th ACM/IEEE International Workshop on Quantum Soft- ware Engineering. Q-SE 2024, pp. 41–48. Association for...

    2024

  17. [25]

    https://arxiv.org/abs/2501

    Shen, J., Ammermann, J., K¨ onig, C., Schaefer, I.: Quantum Pattern Detection: Accurate State- and Circuit-based Analyses (2025). https://arxiv.org/abs/2501. 15895

    2025

  18. [26]

    Quantum7, 1062 (2023) https://doi.org/10.22331/q-2023-07-20-1062

    Quetschlich, N., Burgholzer, L., Wille, R.: Mqt bench: Benchmarking software and design automation tools for quantum computing. Quantum7, 1062 (2023) https://doi.org/10.22331/q-2023-07-20-1062

    2023 doi

  19. [27]

    https://github.com/ PatternAtlas

    The Pattern Atlas Team: The Pattern Atlas Initiative. https://github.com/ PatternAtlas. Accessed: 2025-11-02 (2021)

    2021

  20. [28]

    Zenodo (2025)

    Cirq Developers: Cirq. Zenodo (2025). https://doi.org/10.5281/zenodo.4062499 . https://doi.org/10.5281/zenodo.4062499

    2025 doi

  21. [29]

    https://arxiv.org/abs/2003.02989

    Broughton, M., Verdon, G., McCourt, T., Martinez, A.J., Yoo, J.H., Isakov, S.V., Massey, P., Halavati, R., Niu, M.Y., Zlokapa, A., Peters, E., Lockwood, O., Skolik, A., Jerbi, S., Dunjko, V., Leib, M., Streif, M., Dollen, D.V., Chen, H., Cao, S., Wiersema, R., Huang, H.-Y., Mc...

    2021 arXiv

  22. [30]

    HuggingFace (2021)

    Transformers, S.: all-mpnet-base-v2. HuggingFace (2021). https://huggingface. co/sentence-transformers/all-mpnet-base-v2

    2021

  23. [31]

    org/10.1145/3703155

    Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., Liu, T.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions43(2) (2025) https://doi. org/10.1145/3703155

    2025 doi

  24. [32]

    https://www.together.ai/

    Together AI: Together AI Platform. https://www.together.ai/. Accessed: 28-06- 2026 (2026)

    2026

  25. [33]

    Deutsches Institut f¨ ur Normung e. V. (DIN): DIN – Deutsches Institut f¨ ur Normung. https://www.din.de/en. Accessed: 2026-05-04 (2026)

    2026

  26. [34]

    https://www.cencenelec

    CEN-CENELEC: CEN-CENELEC Official Website. https://www.cencenelec. eu/. Accessed: 2026-05-04 (2026)

    2026

  27. [35]

    In: Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering (MODELSW ARD) (2025)

    Bock, S., Seidel, R., Petriˇ c, M., Tcholtchev, N., Hoffmann, A., Porges, N.: Designing a meta-model for the Eclipse Qrisp eDSL for high-level quantum pro- gramming. In: Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering (MODELSW A...

    2025 doi

  28. [36]

    Python Software Foundation

    Ast — Abstract Syntax Trees. Python Software Foundation. https://docs.python. org/3/library/ast.html Accessed 2026-08-05 42

    2026

  29. [37]

    https://neo4j.com/ (2026)

    Neo4j, Inc.: Neo4j Graph Database. https://neo4j.com/ (2026)

    2026

  30. [38]

    American Mathematical Society (2002)

    Brassard, G., Høyer, P., Mosca, M., Tapp, A.: Quantum amplitude amplification and estimation. American Mathematical Society (2002). https://doi.org/10.1090/ conm/305/05215 . http://dx.doi.org/10.1090/conm/305/05215

    2002 doi

  31. [39]

    International Academy, Research, and Industry Association (IARIA), Nice, France (2023)

    2023

  32. [40]

    http://quantumalgorithmzoo.org

    Jordan, S.: The Quantum Algorithm Zoo. http://quantumalgorithmzoo.org. Accessed: June 2025 (2024) 43

    2024

  33. [41]

    Physical Review A76(3) (2007) https://doi.org/10.1103/ physreva.76.030306

    Dobˇ s ´ ıˇ cek, M., Johansson, G., Shumeiko, V., Wendin, G.: Arbitrary accuracy iterative quantum phase estimation algorithm using a single ancillary qubit: A two-qubit benchmark. Physical Review A76(3) (2007) https://doi.org/10.1103/ physreva.76.030306

    2007

  34. [268]

    https://doi.org/10.5220/0012057700003538

    SciTePress, Rome, Italy (2023). https://doi.org/10.5220/0012057700003538 . https://www.scitepress.org/Link.aspx?doi=10.5220%2f0012057700003538

    2023 doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.