Exploring the application of quantum technologies to industrial and real-world use cases

Eneko Osaba; Esther Villar-Rodriguez; Izaskun Oregi

Exploring the application of quantum technologies to industrial and real-world use cases

T0 review · 0 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Five applied-quantum papers collectively argue that quantum computing is becoming a practical catalyst in machine learning and optimization.

desk verdict A competent special-issue editorial with no research content; useful only as a pointer to the five accepted papers, and slightly too eager in its 'quantum revolution' framing. read the letter →

arxiv 2505.03302 v1 pith:S37EP7O4 submitted 2025-05-06 quant-ph cs.ET

Eneko Osaba , Esther Villar-Rodriguez , Izaskun Oregi This is my paper · ORCID

classification quant-phcs.ET

keywords QuantumComputingMachineLearningOptimizationAnnealingHybridQuantum-ClassicalAlgorithmsNISQSpecialIssueEditorial

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 editorial argues that quantum computing has moved into a phase of practical exploration, anchored by five accepted papers in a special issue on real-world applications. The papers span quantum machine learning for slope stability and movie recommendations, hybrid variational simulation of metal solidification, quantum-inspired intrusion detection, and a review of hardware progress. Taken together, the editors claim, these contributions show quantum computing becoming a significant research catalyst in machine learning and optimization, even amid noisy intermediate-scale devices. The value of the claim, if true, is that near-term quantum and hybrid methods can already improve accuracy or training characteristics on narrowly scoped industrial tasks, not that general-purpose quantum advantage has arrived.

What carries the argument

The load-bearing mechanism is the family of hybrid quantum-classical schemes, including variational quantum algorithms for simulation and classification, quantum kernel methods such as the QSVM, and quantum-inspired evolutionary metaheuristics. The editorial identifies three enablers: larger and better-connected quantum devices; specialized programming frameworks that lower the entry barrier; and ready-to-use hybrid solver services. Together these components let domain experts apply quantum methods without deep hardware expertise, which is what makes the special-issue results possible.

What would settle it

Re-running the movie-recommendation experiment with the same dataset and a properly tuned classical SVM, using cross-validation and multiple seeds, and finding that the QSVM's 96% accuracy does not exceed the classical baseline, or that the gap disappears when noise is added, would undercut the strongest evidence for near-term QML utility.

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Extended reading notes

Core claim

The central claim is that five peer-selected studies provide converging evidence that quantum and hybrid quantum-classical methods are applicable to real-world industrial problems now. Concretely: a quantum support-vector machine reaches 96% accuracy (F1 0.9693) on movie recommendation data, beating a classical SVM; quantum machine learning classifiers outperform classical models on rock-block stability prediction; a variational quantum algorithm serves as a viable surrogate for phase-field simulation of dendritic metal solidification; a quantum-inspired particle-swarm extreme-learning machine speeds intrusion detection; and the hardware trajectory has crossed the 1,000-qubit mark while remaining in the NISQ era. The editors read these results as indicating that quantum computing is set to become a significant research catalyst in machine learning and optimization.

Load-bearing premise

The editorial's case depends on the experimental results reported in the five papers being accurate and representative, especially the claim that the quantum support-vector machine beats the classical one on movie data.

Editorial extensions

If this is right

If the reported results hold, near-term quantum machine learning can beat classical baselines on narrow industrial datasets, making QML a candidate for early adoption in quality control, risk assessment, and recommendation systems.
Hybrid quantum-classical surrogates become a practical alternative for expensive physics simulations, such as metal solidification in additive manufacturing, where full simulations are computationally costly.
Quantum-inspired metaheuristics, run on classical hardware, offer an immediate path to speedups in feature selection and intrusion detection without waiting for fault-tolerant machines.
The hardware trajectory, crossing 1,000 qubits and heading toward larger annealers and error-corrected processors, suggests the bottleneck shifts from qubit count to noise, error correction, and problem encoding.
The three enablers identified (devices, frameworks, hybrid schemes) imply that progress in software and hybrid integration may matter as much as raw hardware for real-world adoption.

Reading between the lines

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

A natural test the editorial leaves implicit is whether the reported QSVM 96% accuracy survives a properly cross-validated classical SVM on the same held-out data; if the advantage shrinks or vanishes under noise or re-tuning, the evidence for practical QML utility weakens.
The special issue's selection of positive results means the collection demonstrates feasibility in favorable cases, not typical performance; a reader should treat the reported margins as optimistic upper bounds.
The editorial's framing suggests that quantum-inspired methods and hybrid schemes may reach industrial deployment before fault-tolerant quantum computing, since they run on classical hardware and only borrow quantum principles.
One could extend the thesis by benchmarking the same five problem classes (classification, recommendation, simulation, intrusion detection) on both quantum hardware and strong classical baselines with identical data splits, converting anecdotal gains into a generalizable estimate.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague,

This is an editorial for a special issue, not a research paper. It contains no new equations, data, or analyses, and it does not try to. Its function is to summarize five accepted contributions and argue that they show quantum computing becoming a practical tool in machine learning and optimization. Judged as an editorial, it is fine: the summaries are accurate in substance, the three enabling factors (bigger devices, frameworks, hybrid schemes) are reasonable, and the references point to real work. The authors also acknowledge NISQ limitations early on, which is more measured than many pieces in this genre.

Where it gets softer is in the height of the conclusion relative to the evidence it cites. The showcase number is the QSVM result from Shahid et al.: 96% accuracy vs classical SVM's 95.33%. The stress-test note is right to flag that this is reported with zero statistical context, no dataset size, number of runs, variance, or significance test. A 0.67 point gap could easily be noise, and the editorial presents it as evidence of quantum advantage. The same pattern repeats in the other summaries: claims of "outperform" are taken at face value without critical assessment. That is normal for an editorial, but it means the "quantum revolution" language in Section 3 is not supported by anything the editorial itself checks. The phrase "it is indisputable" is too strong for any claim in this field, and the duplicated paragraph in Section 3 should have been caught in editing.

None of this is fatal to the genre. The editorial is not proposing a research result; it is curating other people's results. If the underlying papers hold up, the editorial is a serviceable overview. If they do not, the editorial inherits their fragility, but that is a limitation of the format rather than an internal contradiction. The stress-test concern is fair, but I would not call it load-bearing: the editorial's central claim is a forward-looking perspective, not a quantitative theorem, and it would not collapse if the QSVM gap shrank.

My take: this is not a research preprint and should not be treated as one. It is a reasonable editorial for a special issue, useful to someone looking for a quick map of the five papers. I would not cite it in my own work, and I would not bring it to a reading group for substantive discussion. If it were submitted as a regular research article, I would desk reject it. As an editorial, it does not need external peer review beyond the editors' own judgment.

Recommendation: no peer review; accept or reject on editorial merits.

Referee Report

0 major / 5 minor

Summary. This manuscript is an editorial for a special issue on quantum technologies in industrial and real-world applications. It opens with a brief overview of the current NISQ landscape, listing three factors (larger devices, specialized frameworks, and ready-to-use hybrid methods) that have driven recent interest. The bulk of the paper summarizes the five accepted contributions: QML for rockfall prediction (Cisneros et al.), a QSVM-based movie recommender (Shahid et al.), a VQA surrogate model for phase-field simulation (Garate-Perez et al.), a quantum-inspired particle swarm optimization with extreme learning machines for intrusion detection (Qi et al.), and a review of IBM's quantum hardware roadmap (AbuGhanem). The editorial concludes that these contributions collectively demonstrate the potential of quantum computing to become a significant research catalyst in machine learning and optimization.

Significance. The paper makes no original quantitative or technical claims; its value lies in curating and contextualizing five peer-reviewed contributions for a broad audience. The structure is clear, and the introductory caveats about NISQ limitations are welcome. The main weaknesses are rhetorical: some conclusions in Section 3 go beyond what the summarized evidence supports, and the quantitative summaries in Section 2 lack statistical context. These issues are local and easily fixed. Because the paper is an editorial rather than a research article, the absence of independently reproducible code or derivations is not a defect, but the authors should attribute summarized results explicitly to the original papers and avoid definitive language such as 'indisputable' in the conclusion.

minor comments (5)

[Section 2, Shahid et al. paragraph] The reported QSVM accuracy advantage (96% vs 95.33%) and F1 difference (0.9693 vs 0.9641) are presented without sample size, variance, or significance testing; since the editorial is summarizing, please attribute these numbers explicitly to the original paper and add a caution that the original authors' results have not been independently verified.
[Section 3] The phrase "it is indisputable that the opportunities and benefits arising from the evolution of quantum computing will soon surpass the boundaries of our imagination" overstates the evidence; the introduction's own caveat that NISQ devices 'cannot process medium-scale complex problems efficiently' argues for a more measured formulation.
[Section 3] The first two paragraphs of Section 3 are nearly identical and appear to be a duplication; please remove one of them.
[Section 1 and Abstract] There are minor typographical issues: 'fosterred' should be 'fostered' in the bullet list on frameworks, and 'e ra' in the abstract should be 'era'.
[Section 1] The sentence about IBM's Heron processor (156 superconducting qubits) lacks a citation; please add a reference or clearly mark it as general knowledge from the vendor's public materials.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the editorial merely summarizes five independently reviewed contributions; no prediction or derivation reduces to its inputs.

full rationale

This manuscript is a special-issue editorial, not a derivation. Its central claim is that the five accepted papers collectively demonstrate quantum computing's potential as a research catalyst in machine learning and optimization. That claim is supported by the editorial's summaries of each paper's reported results (e.g., Shahid et al.'s QSVM accuracy) and by citations to external surveys and roadmaps. No equation, fitted parameter, or algorithm is introduced by the editorial itself, so there is no step in which an input is renamed as a prediction or a conclusion is equivalent to its premise. The only self-citation, [7] (Villar-Rodriguez et al.), supports the general point that hybrid schemes and frameworks make quantum computing accessible; that point is also supported by independent citations and is not load-bearing for the editorial's conclusion. The skeptical concern that the summarized results, especially the 96% QSVM accuracy, lack statistical context is a reproducibility or evidence-quality concern about the underlying papers, not a circularity in this editorial's argument. Accordingly, no circular step is present.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The editorial's outlook depends on the accuracy of external hardware claims and the reliability of the five summarized studies. No free parameters or invented entities are introduced.

assumptions (2)

domain assumption Quantum hardware development milestones cited in [4][6] and the D-Wave roadmap are accurate and achievable.
The editorial's optimistic framing relies on these external hardware projections.
domain assumption The five accepted papers in the special issue report their experimental results accurately and without significant flaws.
The editorial's central demonstration that quantum computing has practical potential is based entirely on these summaries.

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Cite this review

Pith. "Pith review of Exploring the application of quantum technologies to industrial and real-world use cases." pith.science (2026). https://pith.science/paper/S37EP7O4

@misc{pith2026250503302,
  author       = {Pith},
  title        = {Pith review of: Exploring the application of quantum technologies to industrial and real-world use cases},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S37EP7O4}},
  note         = {Machine review of arXiv:2505.03302}
}

read the original abstract

Recent advancements in quantum computing are leading to an era of practical utility, enabling the tackling of increasingly complex problems. The goal of this era is to leverage quantum computing to solve real-world problems in fields such as machine learning, optimization, and material simulation, using revolutionary quantum methods and machines. All this progress has been achieved even while being immersed in the noisy intermediate-scale quantum era, characterized by the current devices' inability to process medium-scale complex problems efficiently. Consequently, there has been a surge of interest in quantum algorithms in various fields. Multiple factors have played a role in this extraordinary development, with three being particularly noteworthy: (i) the development of larger devices with enhanced interconnections between their constituent qubits, (ii) the development of specialized frameworks, and (iii) the existence of well-known or ready-to-use hybrid schemes that simplify the method development process. In this context, this manuscript presents and overviews some recent contributions within this paradigm, showcasing the potential of quantum computing to emerge as a significant research catalyst in the fields of machine learning and optimization in the coming years.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

12 extracted references · 11 canonical work pages

[1]

Quantum Reports 6(4), 627–663 (2024)

Memon, Q.A., Al Ahmad, M., Pecht, M.: Quantum computing: navigat ing the future of computation, challenges, and technological breakthro ughs. Quantum Reports 6(4), 627–663 (2024)

work page 2024
[2]

Frontiers in Physics 10, 940293 (2022)

Lubinski, T., Granade, C., Anderson, A., Geller, A., Roetteler, M., P etrenko, A., Heim, B.: Advancing hybrid quantum–classical computation with re al-time execution. Frontiers in Physics 10, 940293 (2022)

work page 2022
[3]

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Preskill, J.: Quantum computing in the nisq era and beyond. Quantu m 2, 79 (2018)

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[4]

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Boothby, K., Bunyk, P., Raymond, J., Roy, A.: Next-generation t opology of d- wave quantum processors. arXiv preprint arXiv:2003.00133 (2020 )

arXiv 2003
[5]

Journal of Heuristics 30(5), 325–358 (2024)

Tasseﬀ, B., Albash, T., Morrell, Z., Vuﬀray, M., Lokhov, A.Y., Misra, S., Coﬀrin, C.: On the emerging potential of quantum annealing hardware for co mbinatorial optimization. Journal of Heuristics 30(5), 325–358 (2024)

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[6]

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McGeoch, C., Farr´ e, P.: Milestones on the quantum utility highway . In: 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC), pp. 393–399 (2 022). IEEE

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[8]

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[9]

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Quantum Reports 6(4), 627–663 (2024)

Memon, Q.A., Al Ahmad, M., Pecht, M.: Quantum computing: navigat ing the future of computation, challenges, and technological breakthro ughs. Quantum Reports 6(4), 627–663 (2024)

work page 2024

[2] [2]

Frontiers in Physics 10, 940293 (2022)

Lubinski, T., Granade, C., Anderson, A., Geller, A., Roetteler, M., P etrenko, A., Heim, B.: Advancing hybrid quantum–classical computation with re al-time execution. Frontiers in Physics 10, 940293 (2022)

work page 2022

[3] [3]

Quantu m 2, 79 (2018)

Preskill, J.: Quantum computing in the nisq era and beyond. Quantu m 2, 79 (2018)

work page 2018

[4] [4]

arXiv preprint arXiv:2003.00133 (2020 )

Boothby, K., Bunyk, P., Raymond, J., Roy, A.: Next-generation t opology of d- wave quantum processors. arXiv preprint arXiv:2003.00133 (2020 )

arXiv 2003

[5] [5]

Journal of Heuristics 30(5), 325–358 (2024)

Tasseﬀ, B., Albash, T., Morrell, Z., Vuﬀray, M., Lokhov, A.Y., Misra, S., Coﬀrin, C.: On the emerging potential of quantum annealing hardware for co mbinatorial optimization. Journal of Heuristics 30(5), 325–358 (2024)

work page 2024

[6] [6]

In: 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC), pp

McGeoch, C., Farr´ e, P.: Milestones on the quantum utility highway . In: 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC), pp. 393–399 (2 022). IEEE

work page 2022

[7] [7]

Villar-Rodriguez, E., Gomez-Tejedor, A., Osaba, E.: Hybrid classic al-quantum computing: are we forgetting the classical part in the binomial? In: 2 023 IEEE International Conference on Quantum Computing and Engineering (QCE), vol. 2, pp. 264–265 (2023). IEEE

work page 2023

[8] [8]

: Variational quantum algorithms

Cerezo, M., Arrasmith, A., Babbush, R., Benjamin, S.C., Endo, S., F ujii, K., McClean, J.R., Mitarai, K., Yuan, X., Cincio, L., et al. : Variational quantum algorithms. Nature Reviews Physics 3(9), 625–644 (2021)

work page 2021