Pith. sign in

REVIEW 2 major objections 5 minor 69 references

An ontology, SHACL checks, and SPARQL queries can turn VQA runs into complete, machine-checkable execution records.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · grok-4.5

2026-07-11 22:32 UTC pith:6AJG23DN

load-bearing objection Solid QSE tooling paper: a real OWL/SHACL/SPARQL VQA execution ontology with a working demo and public package; impact is bounded by schema choice and missing exporters, not by a broken claim. the 2 major comments →

arxiv 2607.03982 v1 pith:6AJG23DN submitted 2026-07-04 quant-ph

A Semantic Framework for Reproducible Variational Quantum Algorithm Execution Records

Silvie Ill\'esov\'a , Martin Beseda This is my paper
classification quant-ph
keywords quantum software engineeringvariational quantum algorithmsontologyVQEreproducibilitySHACLSPARQLexecution metadata
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Variational quantum algorithms produce results that depend on a thick stack of choices—ansatz, Hamiltonian, optimizer, backend, shots, noise, mitigation, seed, software versions—usually scattered across code, logs, configs, and papers. This paper argues that those executions should be treated as first-class software-engineering artifacts: structured, machine-readable records. It supplies an OWL ontology that models the main entities in a VQA run, SHACL constraints that flag incomplete or inconsistent metadata, and SPARQL competency queries that retrieve what is needed to reproduce or compare a run. On VQE examples the valid record conforms and answers the queries; an intentionally broken record yields twenty violations. A sympathetic reader cares because without such records, reported energies cannot be reliably reused, debugged, or compared across tools and papers.

Core claim

An OWL ontology plus SHACL validation and SPARQL queries can represent complete VQA execution contexts, automatically detect missing or malformed metadata, and retrieve the information needed for reproducible quantum software experimentation, as shown on valid and intentionally invalid VQE records.

What carries the argument

The VQA execution ontology (71 OWL classes, 35 object properties, 33 datatype properties) together with SHACL shapes and SPARQL competency questions; it makes the hybrid quantum–classical execution context—algorithm, circuit, backend, noise, software environment, result—explicit and checkable as an RDF artifact.

Load-bearing premise

That the authors’ chosen completeness checklist—classes, shapes, and six competency questions—is a good enough proxy for what real-world VQA reproducibility actually requires across groups and tools.

What would settle it

Take a set of real VQE or QAOA runs exported from common quantum frameworks; if the ontology cannot capture the fields practitioners need, or if valid-looking records still fail to support independent reproduction while the SHACL/SPARQL layer reports no defects, the central claim fails.

Watch this falsifier — get emailed when new claim-graph text bears on it.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 5 minor

Summary. The paper proposes an OWL ontology plus SHACL validation and SPARQL competency queries for representing VQA execution records as structured, machine-readable software-engineering artifacts. It models algorithms, ansatzes, Hamiltonians, optimizers, backends, noise models, mitigation methods, software environments, execution steps, and results, and demonstrates the stack on controlled VQE records: a valid record that conforms and an intentionally invalid record that yields 20 violations (Table 3), with six SPARQL queries (Table 2) retrieving reproducibility-relevant metadata. A public Zenodo package provides the ontology, shapes, queries, examples, and scripts.

Significance. If the demonstrated capability holds, the work is a useful contribution to quantum software engineering: it makes VQA execution context first-class, checkable, and queryable rather than scattered across code, logs, and papers. Strengths include a concrete OWL model (71 classes, 35 object / 33 datatype properties), explicit SHACL rules, competency questions, a clear valid/invalid contrast (0 vs 20 violations), and a replication package. The evaluation is a controlled feasibility study rather than a scalability or multi-framework adoption proof; the authors acknowledge this in §5.4. Within that scope the claim is well supported and relevant to reproducibility practice.

major comments (2)
  1. §5.2 and Table 3: the central capability claim is demonstrated only on two hand-crafted VQE records. That is sufficient for a feasibility claim, but the manuscript should state more sharply what is and is not established—e.g., that SHACL detects the injected defect classes, not that the chosen 14 shapes / 33 constraints are complete for real multi-group campaigns. A short explicit scope sentence in the abstract and §5 would prevent over-reading.
  2. §5.4 and §3: practical impact hinges on automatic export from Qiskit/PennyLane/Cirq and on ontology versioning as frameworks evolve. These are correctly listed as limitations, but they are load-bearing for the reproducibility motivation. The revision should either sketch a minimal exporter interface (or mapping from existing result objects) or demote adoption language so the paper’s claim stays aligned with the controlled demo.
minor comments (5)
  1. §5.2: typo “he goal of this evaluation” → “The goal”.
  2. Figure 1 / Figure 2: ensure labels remain legible in print; the hybrid-loop diagram is useful but dense.
  3. Table 1 and §3: a brief note on how AdaptiveAnsatz / SAOOVQE relate to the VQE case study would help readers map the full class list to the demo.
  4. Data Availability: cite the Zenodo DOI consistently in the main text when first mentioning the replication package.
  5. Related work: a short comparison to existing quantum provenance / workflow metadata efforts (beyond general frameworks) would situate the ontology more clearly.

Circularity Check

0 steps flagged

No significant circularity: a feasibility demo of an author-designed OWL+SHACL+SPARQL stack on intentionally valid/invalid VQE records, with self-consistency expected for ontology engineering rather than a derivation that collapses into its inputs.

full rationale

This paper does not claim a first-principles physical derivation, uniqueness theorem, or fitted-parameter prediction. Its central claim is a scoped software-engineering capability claim: that an OWL ontology plus SHACL shapes and SPARQL competency queries can represent VQA execution metadata, detect missing/malformed fields, and retrieve reproducibility-relevant information, as shown on one valid and one intentionally invalid VQE record (0 vs 20 violations; Table 3; CQ1–CQ6). The SHACL shapes validate records written against the same ontology the authors designed, and the invalid example is constructed to fail—this is ordinary self-consistency for ontology/schema papers, not a circular reduction of a scientific prediction to a fitted constant or a load-bearing self-citation chain. The authors openly treat the evaluation as a controlled feasibility demonstration (§5.2, §5.4), not a scalability or multi-framework adoption proof, and they flag that other groups may need different metadata detail and that practical use requires exporters from existing tools. No equation equates a claimed prediction to its own fit; no uniqueness result is imported from prior author work to force the schema; no known empirical law is merely renamed. Score 1 reflects only the mild, expected self-consistency of validating author-authored records against author-authored shapes, which is not load-bearing circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 4 axioms · 2 invented entities

This is a design-and-demonstration paper, not a fitted physical model. Load-bearing content is mostly domain modeling choices (what counts as required VQA metadata) and standard Semantic Web formalisms, plus the invented ontology as the delivered artifact. There are essentially no numerical free parameters fitted to experimental data; risk sits in schema completeness and adoption assumptions rather than curve-fitting.

axioms (4)
  • domain assumption Reproducible VQA experimentation requires explicit, machine-checkable linkage among algorithm, ansatz, Hamiltonian, optimizer, backend, noise/mitigation, software environment, seeds/shots, and results.
    Stated throughout §1–§2 and operationalized as ontology modules and SHACL structural checks; if false for some workflows, the schema over- or under-specifies.
  • domain assumption OWL class hierarchies plus SHACL shapes with RDF inference are an adequate formalism for expressing and checking VQA execution-record completeness and consistency.
    Framework choice in §3–§4; alternative provenance models (e.g., PROV extensions, workflow DBs) are not comparatively validated.
  • ad hoc to paper The controlled vocabulary and numerical ranges encoded in SHACL (optimizer names, Pauli strings, non-negative shots/iterations, admissible error rates) correctly capture well-formed VQA metadata.
    Validation rules in §4.1–§4.2; other groups may need different vocabularies or calibration detail (§5.4).
  • standard math Standard Semantic Web stack semantics (OWL, SHACL, SPARQL) behave as specified in the cited literature.
    Background tooling assumptions for validation and querying (§4).
invented entities (2)
  • VQA Execution Ontology (71 OWL classes, related properties, execution-centric modules) independent evidence
    purpose: Provide a shared vocabulary linking algorithm configuration, hybrid execution context, and recorded outputs for VQA runs.
    Core contribution of §3; not a physical particle but a new schema entity whose value is design usefulness rather than independent empirical discovery.
  • SHACL validation layer and six SPARQL competency questions for VQA records independent evidence
    purpose: Automatically detect incomplete/inconsistent records and retrieve reproducibility-oriented metadata.
    §4 and Table 2; evidence is the public shapes/queries and the 0-vs-20 violation demo, not external physics measurements.

pith-pipeline@v1.1.0-grok45 · 16858 in / 3055 out tokens · 35996 ms · 2026-07-11T22:32:37.346034+00:00 · methodology

0 comments
read the original abstract

Variational quantum algorithms are hybrid quantum-classical workflows whose results depend on many interacting choices, including the ansatz, Hamiltonian, optimizer, backend, shot count, noise model, mitigation method, random seed, stopping criteria, and software versions. In current practice, this information is often scattered across code, configuration files, logs, backend metadata, and paper descriptions, making executions difficult to reproduce, compare, debug, and reuse. This paper proposes an ontology-supported framework for representing Variational Quantum Algorithm (VQA) execution records as structured and machine-readable software engineering artifacts. The framework defines a Web Ontology Language (OWL) ontology for modeling the main entities involved in VQA experimentation, including algorithms, circuits, ansatzes, Hamiltonians, optimizers, backends, noise models, mitigation techniques, execution steps, software environments, measurement outcomes, and results. It further combines the ontology with Shapes Constraint Language (SHACL) constraints for validating completeness and consistency, and SPARQL Protocol and RDF Query Language (SPARQL) competency queries for retrieving reproducibility-relevant information. We demonstrate the approach using Variational Quantum Eigensolver (VQE) execution records, including a valid record and intentionally incomplete or inconsistent examples. The results show that the framework can represent complete VQA execution contexts, detect missing or malformed metadata, and support query-based inspection of information needed for reproducible quantum software experimentation.

Figures

Figures reproduced from arXiv: 2607.03982 by Martin Beseda, Silvie Ill\'esov\'a.

Figure 1
Figure 1. Figure 1: General workflow of a variational quantum algo [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Compact overview of the VQA ontology as an execution record. The ontology connects algorithm configuration, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

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

69 extracted references · 1 canonical work pages

  1. [1]

    David Amaro, Carlo Modica, Matthias Rosenkranz, Mattia Fiorentini, Marcello Benedetti, and Michael Lubasch. 2022. Filtering variational quantum algorithms for combinatorial optimization.Quantum Science & Technology7, 1 (2022), 015021

    2022

  2. [2]

    George S Barron and Christopher J Wood. 2020. Measurement error mitigation for variational quantum algorithms.arXiv preprint arXiv:2010.08520(2020)

    Pith/arXiv arXiv 2020

  3. [3]

    Bela Bauer, Sergey Bravyi, Mario Motta, and Garnet Kin-Lic Chan. 2020. Quantum algorithms for quantum chemistry and quantum materials science.Chemical reviews120, 22 (2020), 12685–12717

    2020

  4. [4]

    Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, Shahnawaz Ahmed, Vishnu Ajith, M Sohaib Alam, Guillermo Alonso-Linaje, Bharath Akash- Narayanan, Ali Asadi, et al. 2018. Pennylane: Automatic differentiation of hybrid quantum-classical computations.arXiv preprint arXiv:1811.04968(2018)

    Pith/arXiv arXiv 2018

  5. [5]

    Martin Beseda, Silvie Illésová, Saad Yalouz, and Bruno Senjean. 2024. State- Averaged Orbital-Optimized VQE: A quantum algorithm for the democratic description of ground and excited electronic states.Journal of Open Source Software9, 101 (2024), 6036

    2024

  6. [6]

    Tomáš Bezděk, Haomu Yuan, Vojtěch Novák, Silvie Illésová, and Martin Beseda

  7. [7]

    doi:10.48550/arXiv.2511.09314

    Classical Optimization Strategies for Variational Quantum Algorithms: A Systematic Study of Noise Effects and Parameter Efficiency.arXiv preprint arXiv:2511.09314(2025). doi:10.48550/arXiv.2511.09314

    doi:10.48550/arxiv.2511.09314 2025

  8. [8]

    Bart Bogaerts, Maxime Jakubowski, and Jan Van den Bussche. 2022. SHACL: A description logic in disguise. InInternational Conference on Logic Programming and Nonmonotonic Reasoning. Springer, 75–88

    2022

  9. [9]

    Ludmila Botelho, Adam Glos, Akash Kundu, Jarosław Adam Miszczak, Özlem Salehi, and Zoltán Zimborás. 2022. Error mitigation for variational quantum algorithms through mid-circuit measurements.Physical Review A105, 2 (2022), 022441

    2022

  10. [10]

    Giuseppe Buonaiuto, Francesco Gargiulo, Giuseppe De Pietro, Massimo Esposito, and Marco Pota. 2024. The effects of quantum hardware properties on the performances of variational quantum learning algorithms.Quantum Machine Intelligence6, 1 (2024), 9

    2024

  11. [11]

    Marco Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, et al. 2021. Variational quantum algorithms.Nature Reviews Physics3, 9 (2021), 625–644

    2021

  12. [12]

    Samuel Yen-Chi Chen, Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, and Hsi-Sheng Goan. 2020. Variational quantum circuits for deep reinforcement learning.IEEE access8 (2020), 141007–141024

    2020

  13. [13]

    Alexandre Choquette, Agustin Di Paolo, Panagiotis Kl Barkoutsos, David Sénéchal, Ivano Tavernelli, and Alexandre Blais. 2021. Quantum-optimal-control- inspired ansatz for variational quantum algorithms.Physical Review Research3, 2 (2021), 023092

    2021

  14. [14]

    Ciaramelletti, Martin Beseda, M

    C. Ciaramelletti, Martin Beseda, M. Consiglio, L. Lepori, and T. J. G. Apollaro

  15. [15]

    doi:10.1103/PhysRevA.111.022437

    Detecting Quasidegenerate Ground States in Topological Models via 6 A Semantic Framework for Reproducible Variational Quantum Algorithm Execution Records the Variational Quantum Eigensolver.Physical Review A111, 2 (2025), 022437. doi:10.1103/PhysRevA.111.022437

    doi:10.1103/physreva.111.022437 2025

  16. [16]

    Alain Delgado, Juan Miguel Arrazola, Soran Jahangiri, Zeyue Niu, Josh Izaac, Chase Roberts, and Nathan Killoran. 2021. Variational quantum algorithm for molecular geometry optimization.Physical Review A104, 5 (2021), 052402

    2021

  17. [17]

    Cirq Developers. 2025. Cirq. doi:10.5281/ZENODO.4062499

    doi:10.5281/zenodo.4062499 2025

  18. [18]

    Pranav Gokhale, Yongshan Ding, Thomas Propson, Christopher Winkler, Nelson Leung, Yunong Shi, David I Schuster, Henry Hoffmann, and Frederic T Chong

  19. [19]

    InProceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture

    Partial compilation of variational algorithms for noisy intermediate-scale quantum machines. InProceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture. 266–278

  20. [20]

    Gian Giacomo Guerreschi and Anne Y Matsuura. 2019. QAOA for Max-Cut requires hundreds of qubits for quantum speed-up.Scientific reports9, 1 (2019), 6903

    2019

  21. [21]

    Gupta, Martin Beseda, and Piotr Gawron

    Manish K. Gupta, Martin Beseda, and Piotr Gawron. 2022. How Quantum Computing-Friendly Multispectral Data can be?. InIGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 4153–4156. doi:10.1109/IGARSS46834.2022.9883676

    doi:10.1109/igarss46834.2022.9883676 2022

  22. [22]

    Dylan Herman, Cody Googin, Xiaoyuan Liu, Yue Sun, Alexey Galda, Ilya Safro, Marco Pistoia, and Yuri Alexeev. 2023. Quantum computing for finance.Nature Reviews Physics5, 8 (2023), 450–465

    2023

  23. [23]

    2026.VQA Execution Ontology: RDF/OWL Vocabulary, SHACL Shapes, and SPARQL Competency Queries

    Silvie Illésová and Martin Beseda. 2026.VQA Execution Ontology: RDF/OWL Vocabulary, SHACL Shapes, and SPARQL Competency Queries. doi:10.5281/zenodo. 21168931

    doi:10.5281/zenodo 2026

  24. [24]

    Silvie Illésová, Martin Beseda, Saad Yalouz, Benjamin Lasorne, and Bruno Senjean

  25. [25]

    Transformation-free generation of a quasi-diabatic representation from the state-average orbital-optimized variational quantum eigensolver.Journal of Chemical Theory and Computation21, 11 (2025), 5457–5480

    2025

  26. [26]

    Silvie Illésová, Tomáš Bezděk, Vojtěch Novák, Bruno Senjean, and Martin Beseda

  27. [27]

    Statistical Benchmarking of Optimization Methods for Variational Quantum Eigensolver under Quantum Noise.arXiv preprint arXiv:2510.08727(2025). doi:10. 48550/arXiv.2510.08727

    arXiv 2025

  28. [28]

    Silvie Illésová, Tomáš Bezděk, Vojtěch Novák, Ivan Zelinka, Stefano Cacciatore, and Martin Beseda. 2025. From Classical to Hybrid: A Practical Framework for Quantum-Enhanced Learning.arXiv preprint arXiv:2511.08205(2025). doi:10. 48550/arXiv.2511.08205

    arXiv 2025

  29. [29]

    Silvie Illésová, Emmanuel Obeng, Tomáš Bezděk, Vojtěch Novák, and Martin Beseda. 2025. On the Complementarity of Classical Convolution and Quantum Neural Networks in Image Classification.Preprints(2025). Preprint. doi:10.20944/ preprints202512.1348.v1

    arXiv 2025

  30. [30]

    Silvie Illésová, Tomasz Rybotycki, and Martin Beseda. 2025. QMetric: Bench- marking Quantum Neural Networks Across Circuits, Features, and Training Dimensions. InQualITA 2025: The Fourth Conference on System and Service Quality (CEUR Workshop Proceedings, Vol. 4080). https://ceur-ws.org/Vol- 4080/paper3.pdf

    2025

  31. [31]

    Silvie Illésová, Tomasz Rybotycki, Piotr Gawron, and Martin Beseda. 2025. On the Importance of Fundamental Properties in Quantum-Classical Machine Learning Models.arXiv preprint arXiv:2507.10161(2025). doi:10.48550/arXiv.2507.10161

    work page doi:10.48550/arxiv.2507.10161 2025

  32. [32]

    Ali Javadi-Abhari, Matthew Treinish, Kevin Krsulich, Christopher J Wood, Jake Lishman, Julien Gacon, Simon Martiel, Paul D Nation, Lev S Bishop, An- drew W Cross, et al . 2024. Quantum computing with Qiskit.arXiv preprint arXiv:2405.08810(2024)

    Pith/arXiv arXiv 2024

  33. [33]

    Abhinav Kandala, Antonio Mezzacapo, Kristan Temme, Maika Takita, Markus Brink, Jerry M Chow, and Jay M Gambetta. 2017. Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets.nature549, 7671 (2017), 242–246

    2017

  34. [34]

    Hui-Min Li, Yuan-Liang Han, Zhi-Xi Wang, and Shao-Ming Fei. 2026. Varia- tional quantum algorithm for constrained combinatorial optimization problems. Physical Review A113, 3 (2026), 032406

    2026

  35. [35]

    Jin-Guo Liu, Yi-Hong Zhang, Yuan Wan, and Lei Wang. 2019. Variational quan- tum eigensolver with fewer qubits.Physical Review Research1, 2 (2019), 023025

    2019

  36. [36]

    Tessa Livingston and Suresh Muknahallipatna. 2025. Application of Variational Quantum Algorithms in Quantum Machine Learning.IEEE Nanotechnology Magazine(2025)

    2025

  37. [37]

    Ashis Kumar Mandal, Md Nadim, Chanchal K Roy, Banani Roy, and Kevin A Schneider. 2025. Quantum software engineering and potential of quantum computing in software engineering research: a review.Automated Software Engineering32, 1 (2025), 27

    2025

  38. [38]

    Maximilian Moll and Leonhard Kunczik. 2025. A case study for cyber-attack detection using quantum variational circuits.Quantum Machine Intelligence7, 1 (2025), 53

    2025

  39. [39]

    Kouhei Nakaji, Suguru Endo, Yuichiro Matsuzaki, and Hideaki Hakoshima

  40. [40]

    Measurement optimization of variational quantum simulation by classical shadow and derandomization.Quantum7 (2023), 995

    2023

  41. [41]

    Tochukwu Kennedy Njoku. 2025. Quantum software engineering: algorithm design, error mitigation, and compiler optimization for fault-tolerant quantum computing.Int J Comput Appl Technol Res14, 4 (2025), 30–42

    2025

  42. [42]

    Vojtěch Novák, Silvie Illésová, Tomáš Bezděk, Ivan Zelinka, and Martin Beseda

  43. [43]

    arXiv preprint arXiv:2511.08289(2025)

    Reliable Optimization Under Noise in Quantum Variational Algorithms. arXiv preprint arXiv:2511.08289(2025). doi:10.48550/arXiv.2511.08289

    doi:10.48550/arxiv.2511.08289 2025

  44. [44]

    Vojtěch Novák, Ivan Zelinka, Lenka Přibylová, Lubomír Martínek, and Vladimír Benčurik. 2026. Quantum machine learning for predicting anastomotic leak: a clinical study.Sci. Rep.16, 1 (May 2026)

    2026

  45. [45]

    Vojtěch Novák, Ivan Zelinka, Lenka Přibylová, Lubomír Martínek, Vladimír Benčurík, and Martin Beseda. 2026. Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors.arXiv preprint arXiv:2604.13951(2026)

    Pith/arXiv arXiv 2026

  46. [46]

    Vojtěch Novák, Ivan Zelinka, and Václav Snášel. 2025. Optimization Strate- gies for Variational Quantum Algorithms in Noisy Landscapes.arXiv preprint arXiv:2506.01715(2025). doi:10.48550/arXiv.2506.01715

    doi:10.48550/arxiv.2506.01715 2025

  47. [47]

    Paolo Pareti and George Konstantinidis. 2021. A review of SHACL: from data validation to schema reasoning for RDF graphs.Reasoning Web International Summer School(2021), 115–144

    2021

  48. [48]

    Jorge Pérez, Marcelo Arenas, and Claudio Gutierrez. 2009. Semantics and com- plexity of SPARQL.ACM Transactions on Database Systems (TODS)34, 3 (2009), 1–45

    2009

  49. [49]

    Han Qi, Sihui Xiao, Zhuo Liu, Changqing Gong, and Abdullah Gani. 2024. Varia- tional quantum algorithms: fundamental concepts, applications and challenges: H. Qi et al.Quantum Information Processing23, 6 (2024), 224

    2024

  50. [50]

    Junhan Qin. 2023. Review of ansatz designing techniques for variational quantum algorithms. InJournal of Physics: Conference Series, Vol. 2634. IOP Publishing, 012043

    2023

  51. [51]

    Behnaz Rahgozar, Shirin Shoghli, Mehdi Yadollahi, and Morteza SaberiKamar- poshti. 2025. Quantum Machine Learning in Cybersecurity: A Review of Quantum-Safe and Quantum-Enhanced Approaches. In2025 IEEE 13th Con- ference on Systems, Process & Control (ICSPC). IEEE, 210–215

    2025

  52. [52]

    Md Abdur Rahman, Mst Shapna Akter, Emily Miller, Bogdan Timofti, Hossain Shahriar, Mohammad Masum, and Fan Wu. 2024. Fine-tuned variational quantum classifiers for cyber attacks detection based on parameterized quantum circuits and optimizers. In2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 1067–1072

    2024

  53. [53]

    Akilan Rajamani, Martin Beseda, Benjamin Lasorne, and Bruno Senjean. 2026. How an Equi-Ensemble Description Systematically Outperforms the Weighted Ensemble Variational Quantum Eigensolver.The Journal of Physical Chemistry A(2026)

    2026

  54. [54]

    Gokul Subramanian Ravi, Kaitlin N Smith, Pranav Gokhale, Andrea Mari, Nathan Earnest, Ali Javadi-Abhari, and Frederic T Chong. 2022. Vaqem: A variational approach to quantum error mitigation. In2022 IEEE international symposium on high-performance computer architecture (HPCA). IEEE, 288–303

    2022

  55. [55]

    Max Scheerer, Jonas Klamroth, Simon Garhofer, Florian Knäble, and Oliver Denninger. 2023. Experiences in quantum software engineering. In2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 552–559

    2023

  56. [56]

    Priyabrata Senapati, Zhepeng Wang, Weiwen Jiang, Travis S Humble, Bo Fang, Shuai Xu, and Qiang Guan. 2023. Towards redefining the reproducibility in quantum computing: A data analysis approach on nisq devices. In2023 IEEE International Conference on Quantum Computing and Engineering (QCE), Vol. 1. IEEE, 468–474

    2023

  57. [57]

    Harshdeep Singh, Sonjoy Majumder, and Sabyashachi Mishra. 2023. Benchmark- ing of different optimizers in the variational quantum algorithms for applications in quantum chemistry.The Journal of Chemical Physics159, 4 (2023)

    2023

  58. [58]

    Francesco Tacchino, Stefano Mangini, Panagiotis Kl Barkoutsos, Chiara Mac- chiavello, Dario Gerace, Ivano Tavernelli, and Daniele Bajoni. 2021. Variational learning for quantum artificial neural networks.IEEE Transactions on Quantum Engineering2 (2021), 1–10

    2021

  59. [59]

    Hiep L Thi and Thanh Nguyen. 2024. Variational Quantum Algorithms in Anomaly Detection, Fraud Indicator Identification, Credit Scoring, and Stock Price Prediction. InInternational Congress on Information and Communication Technology. Springer, 483–492

    2024

  60. [60]

    Jules Tilly, Hongxiang Chen, Shuxiang Cao, Dario Picozzi, Kanav Setia, Ying Li, Edward Grant, Leonard Wossnig, Ivan Rungger, George H Booth, et al. 2022. The variational quantum eigensolver: a review of methods and best practices. Physics Reports986 (2022), 1–128

    2022

  61. [61]

    Antonio Trovato, Martin Beseda, and Dario Di Nucci. 2025. A preliminary investigation on the usage of quantum approximate optimization algorithms for test case selection. InProceedings of the 2025 29th International Conference on Evaluation and Assessment in Software Engineering Companion. 56–60

    2025

  62. [62]

    Shengbin Wang, Peng Wang, Guihui Li, Shubin Zhao, Dongyi Zhao, Jing Wang, Yuan Fang, Menghan Dou, Yongjian Gu, Yu-Chun Wu, et al. 2025. Variational quantum eigensolver with linear depth problem-inspired ansatz for solving portfolio optimization in finance.Science China Information Sciences68, 8 (2025), 180504

    2025

  63. [63]

    Benjamin Weder, Johanna Barzen, Martin Beisel, and Frank Leymann. 2023. Provenance-Preserving Analysis and Rewrite of Quantum Workflows for Hybrid Quantum Algorithms: Benjamin Weder et al.SN Computer Science4, 3 (2023), 7 Silvie Illésová and Martin Beseda 233

    2023

  64. [64]

    Anbang Wu, Gushu Li, Yuke Wang, Boyuan Feng, Yufei Ding, and Yuan Xie

  65. [65]

    arXiv preprint arXiv:2111.13730(2021)

    Towards efficient ansatz architecture for variational quantum algorithms. arXiv preprint arXiv:2111.13730(2021)

    Pith/arXiv arXiv 2021

  66. [66]

    Sheng-Yao Wu, Yan-Qi Song, Run-Ze Li, Su-Juan Qin, Qiao-Yan Wen, and Fei Gao. 2026. Resource-Efficient Adaptive Variational Quantum Algorithm for Combinatorial Optimization Problems.Advanced Quantum Technologies9, 2 (2026), 2400484

    2026

  67. [67]

    Zeyu Xu, Wenbin Yu, Chengjun Zhang, and Yadang Chen. 2024. Quantum convolutional long short-term memory based on variational quantum algorithms in the era of NISQ.Information15, 4 (2024), 175

    2024

  68. [68]

    Leo Zhou, Sheng-Tao Wang, Soonwon Choi, Hannes Pichler, and Mikhail D Lukin

  69. [69]

    Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices.Physical Review X10, 2 (2020), 021067. 8

    2020