Pith. sign in

REVIEW 3 major objections 4 minor 40 references

Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read Ten-year roadmap puts quantum ML in a niche, not the AI mainstream

desk verdict A competent, accurate review of supervised QML whose speculative 10-year roadmap is under-supported and internally miscalibrated; the review portion stands, the roadmap needs work. read the letter →

arxiv 2505.24765 v5 pith:WICFUCTS submitted 2025-05-30 quant-ph cs.AI

Srikanth Thudumu , Jason Fisher , Hung Du This is my paper
classification quant-phcs.AI
keywords quantummachinelearningsupervisedvariationalcircuitskernelmethodshybridquantum-classicalworkflowsNISQdevicesfault-tolerantcomputingroadmap
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

Supervised quantum machine learning, the paper argues, will not replace classical machine learning over the next decade; it will instead become a specialized tool for problems where quantum resources provide a clear edge. The review's main contribution is a 2025–2035 roadmap that links hardware phases to realistic use cases: hybrid quantum-classical workflows and error mitigation dominate the near term, while genuinely useful applications such as quantum-enhanced drug discovery depend on the arrival of early fault-tolerant machines with tens of logical qubits. The roadmap matters because it states testable conditions for adoption—error rates dropping by about an order of magnitude, 1,000-qubit machines with gate errors near $10^{-3}$, and community benchmarks for quantum-native data—and it openly warns that slower progress could bring a 'quantum winter.'

What carries the argument

The paper's central object is a projected roadmap spanning 2025–2035, segmented into Early NISQ, Advanced NISQ, and early fault-tolerant phases, each tied to specific algorithm families and deployment models. What carries the argument is the assumed coupling between hardware capability and algorithmic feasibility: error-mitigation techniques (zero-noise extrapolation, virtual distillation, learning-based mitigation) are expected to extend useful circuit depth and qubit count by a factor of 2–3; an order-of-magnitude drop in error rates would make shallow circuits of 50–100 qubits reliable enough to implement QML models; and only fault-tolerant hardware would unlock deep quantum analogues of neural networks. The roadmap functions as a conditional forecast: each software milestone is gated by a hardware threshold.

What would settle it

Track published two-qubit gate error rates and logical-qubit demonstrations year by year: if by 2030 the best available devices do not show at least a tenfold error reduction, or if no system performs a supervised learning task on tens of logical qubits by 2035, the roadmap's core timeline is falsified.

Watch

Extended reading notes

Core claim

The central claim is that the field will follow a hardware-determined trajectory. In the next five years, supervised QML will likely show advantage on one or two specialized problems, most plausibly where the data is itself quantum or the sample size is extremely small; around the same time, hardware should reach about 1,000 physical qubits with error rates at or below $10^{-3}$. In the following five years, early fault-tolerant machines with tens of logical qubits could allow deeper circuits, and if research 'stays on course,' that stage could produce genuinely useful applications at the enterprise level. The paper's overall position is that QML becomes a specialized instrument in the machine-learning toolbox, with broad replacement of classical ML unlikely.

Load-bearing premise

The roadmap assumes that quantum hardware noise will fall by about tenfold within the next few years and that early error-corrected machines with tens of reliable logical qubits will appear by 2035, but it offers no evidence, probability estimates, or alternative timelines for these hardware projections.

Editorial extensions

If this is right

  • Within five years, supervised QML will likely demonstrate advantage on one or two specialized problems, most plausibly involving quantum-native data or extremely low-data regimes.
  • Hardware progress to roughly 1,000 physical qubits with error rates near $10^{-3}$ is expected to enable integrated classical-quantum cloud platforms where QML deployments resemble classical ML pipelines.
  • By 2035, early fault-tolerant machines with tens of logical qubits could support deeper circuits and deliver concrete applications such as quantum-enhanced drug discovery or recommendation systems that catch correlations classical models miss.
  • Quantum machine learning will not broadly replace classical ML; it will be used only where quantum resources give a clear edge, with classical ML staying superior in many areas.
  • Community-driven benchmarks and datasets tailored to quantum data are a precondition for identifying real advantages, and their emergence is expected within the next few years.

Reading between the lines

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

  • A direct testable consequence of the roadmap is that the 2035 'useful applications' claim is the most fragile part: if fault tolerance slips, the deeper-circuit benefits disappear even if near-term hybrid results hold.
  • Quantum data generation may become commercially useful before supervised QML does, because generative models avoid the expensive classical-to-quantum encoding step that still bottlenecks supervised pipelines.
  • If the roadmap is right, enterprise adoption should concentrate in sectors with quantum-native or low-signal data—chemistry, materials, high-energy physics, and selective finance tasks—rather than spread across general IT workloads.
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

3 major / 4 minor

Summary. This paper surveys the state of supervised quantum machine learning (QML), covering variational quantum circuits, quantum kernels, encoding strategies, and hybrid quantum-classical workflows. It summarizes recent empirical demonstrations (e.g., Huang et al. on learning from experiments, IonQ's NLP experiment), theoretical generalization and trainability results (Caro et al., Anschuetz and Kiani), and current challenges such as noise, barren plateaus, scalability, and lack of benchmarks. The paper's stated main contribution is a ten-year outlook (2025–2035) with a roadmap linking hardware development to QML applications, arguing that QML will become a specialized tool rather than a broad replacement for classical ML.

Significance. If its roadmap were properly grounded, the paper could serve as a useful resource for practitioners, funders, and researchers planning QML investments. The factual summaries of key cited works (Huang et al. 2022, Caro et al. 2022, Anschuetz and Kiani 2022) are accurate at face value, and the paper's balanced treatment of current limitations is a strength. However, the roadmap is the central contribution, and its quantitative hardware projections are currently asserted without citation, derivation, or sensitivity analysis. The significance of the paper therefore hinges on whether these projections can be supported or appropriately hedged; in its present form, the roadmap lacks the evidentiary basis needed for a scholarly outlook.

major comments (3)
  1. [Section V-A] The claim that an order-of-magnitude drop in error rates would enable 'shallow-depth quantum circuits with meaningful size (50–100 qubits) reliably implementing QML models' is not checked against simple gate-count arithmetic. At a two-qubit gate error rate of 1e-4, a 100-qubit circuit of depth 100 contains roughly 10,000 two-qubit gates, giving an unmitigated success probability of approximately e^{-1} ≈ 0.37, which is not 'reliable.' If the intended scenario involves much lower error rates or substantially shallower circuits, the text should say so explicitly; otherwise the projection is internally miscalibrated.
  2. [Section V-F] The roadmap's timeline rests on unquantified hardware projections: 'quantum hardware reaching 1000 qubits with error rates around or below 10^{-3}' within five years, and 'tens of logical qubits' by 2035. These are presented as a 'realistic consensus' but are given without citations, probability estimates, or clearly stated assumptions. Since the paper's stated main contribution is this ten-year outlook, these unsupported thresholds are load-bearing; if hardware progress is slower than assumed, the entire 2025–2035 roadmap loses its anchor.
  3. [Section V (overall)] The paper acknowledges a possible 'quantum winter' in Section V-F but does not integrate this possibility into the roadmap as an alternative scenario. The reader is not told which conditions would falsify the optimistic timeline, nor how sensitive the milestones are to variations in error-rate improvement and qubit-count growth. Adding a sensitivity analysis or a scenario tree (e.g., slow, nominal, and fast hardware improvement) would make the roadmap a more rigorous contribution and would address the paper's own caveat about progress being slower than hoped.
minor comments (4)
  1. [Section V-A] The assertion that error mitigation techniques 'could extend the scale at which QML models operate usefully by, say, a factor of 2–3 in circuit depth or qubit count' is given without a citation or derivation. As a quantitative claim in the central outlook, it needs support or should be presented as an illustrative assumption.
  2. [Section III-A] The sentence 'In [3], the authors showed that many classically hard functions can be easily solved by classical neural networks given enough data' is ambiguous: it should clarify that this is a theoretical result with empirical validation in the same paper, not a claim about all classically hard functions.
  3. [Figure 1] Figure 1 is referenced in the text but not described in sufficient detail. The reader cannot interpret the roadmap without knowing what milestones, dependencies, and time intervals are shown; the caption or text should explain the figure's axes, phases, and key transitions.
  4. [References] Reference [20] is formatted as 'M. C. Caro and et al.' which mixes an author name with 'et al.' inconsistently; the reference should either list the full author list or use the standard 'Caro, M. C., et al.' format. Other references to arXiv preprints without journal identifiers should be flagged as such for the reader.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a review and outlook whose forward-looking statements are presented as perspective, not derived quantities.

full rationale

The paper contains no derivation chain that reduces a conclusion to its inputs. Its stated main contribution is a ten-year outlook and roadmap, which is explicitly opinionative and conditional ('we could start to see', 'the 10-year horizon might bring', 'a realistic consensus is'). Claims such as Section V-A's 'If error rates drop by an order of magnitude...' and Section V-F's hardware projections are unsourced and arguably unsupported, but unsupported assumptions are a correctness or evidence concern, not circularity, because they are not presented as predictions derived from fits or from prior results. The reference list contains no papers authored by Thudumu, Fisher, or Du, so there is no self-citation, and no uniqueness theorem is invoked to forbid alternatives. No quantity is fitted and then renamed a prediction, and no known empirical result is repackaged under new coordinates. The review sections summarize external literature; those summaries may be challenged for accuracy, but they do not reduce to their own inputs by construction. Under the hard rules, a non-finding is appropriate: set score 0 with empty steps because the central claim (an outlook) is not equivalent to any input assumption in the paper's own equations or definitions.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper's central claim is a forecast, so the ledger captures the unsupported assumptions about hardware scaling and consensus rather than fitted parameters or invented entities.

assumptions (3)
  • domain assumption The cited experimental and theoretical literature accurately describes the state of supervised QML.
    The entire review depends on the reliability of references [6], [20], [28], [29], etc., which are not independently verified in this paper.
  • domain assumption NISQ hardware will continue to improve along current trajectories (e.g., error rates dropping by an order of magnitude, 1000+ qubits within a few years).
    Section V-A and V-F assert these projections without evidence or citation; the roadmap's feasibility depends on them.
  • domain assumption Fault-tolerant quantum computers with tens of logical qubits will be available by about 2035.
    Section V-F states 'a realistic consensus' that fault tolerance is a decade or more away, but provides no source for this consensus.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications." pith.science (2026). https://pith.science/paper/WICFUCTS

@misc{pith2026250524765,
  author       = {Pith},
  title        = {Pith review of: Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WICFUCTS}},
  note         = {Machine review of arXiv:2505.24765}
}
read the original abstract

Supervised Quantum Machine Learning (QML) represents an intersection of quantum computing and classical machine learning, aiming to use quantum resources to support model training and inference. This paper reviews recent developments in supervised QML, focusing on methods such as variational quantum circuits, quantum neural networks, and quantum kernel methods, along with hybrid quantum-classical workflows. We examine recent experimental studies that show partial indications of quantum advantage and describe current limitations including noise, barren plateaus, scalability issues, and the lack of formal proofs of performance improvement over classical methods. The main contribution is a ten-year outlook (2025-2035) that outlines possible developments in supervised QML, including a roadmap describing conditions under which QML may be used in applied research and enterprise systems over the next decade.

Figures

Figures reproduced from arXiv: 2505.24765 by the authors.

Figure 1
Figure 1. Projected Roadmap for Supervised QML (2025–2035). [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

40 extracted references · 26 canonical work pages

  1. [1]

    Quantum machine learning: A review and case studies,

    A. Zeguendry, Z. Jarir, and M. Quafafou, “Quantum machine learning: A review and case studies,”Entropy, vol. 25, no. 2, p. 287, 2023

    work page 2023

  2. [2]

    Quantum machine learning: from physics to software engineering,

    A. Melnikov, M. Kordzanganeh, A. Alodjants, and R.-K. Lee, “Quantum machine learning: from physics to software engineering,”Advances in Physics: X, vol. 8, no. 1, p. 2165452, 2023

    work page 2023

  3. [3]

    Power of data in quantum machine learning,

    H.-Y . Huang, M. Broughton, M. Mohseni, R. Babbush, S. Boixo, H. Neven, and J. R. McClean, “Power of data in quantum machine learning,”Nature communications, vol. 12, no. 1, p. 2631, 2021

    2021

  4. [4]

    Overcoming the coherence time barrier in quantum machine learning on temporal data,

    F. Hu, S. A. Khan, N. T. Bronn, G. Angelatos, G. E. Rowlands, G. J. Ribeill, and H. E. T ¨ureci, “Overcoming the coherence time barrier in quantum machine learning on temporal data,”nature communications, vol. 15, no. 1, p. 7491, 2024

    work page 2024

  5. [5]

    Mitigating noise in digital and digital–analog quantum computation,

    P. Garc ´ıa-Molina, A. Martin, M. Garcia de Andoin, and M. Sanz, “Mitigating noise in digital and digital–analog quantum computation,” Communications Physics, vol. 7, no. 1, p. 321, 2024

    work page 2024

  6. [6]

    Quantum advantage in learning from experiments,

    H.-Y . Huang, M. Broughton, J. Cotler, S. Chen, J. Li, M. Mohseni, H. Neven, R. Babbush, R. Kueng, J. Preskillet al., “Quantum advantage in learning from experiments,”Science, vol. 376, no. 6598, pp. 1182– 1186, 2022

    work page 2022

  7. [7]

    Exponential quantum advantages in learning quantum observables from classical data,

    R. Molteni, C. Gyurik, and V . Dunjko, “Exponential quantum advantages in learning quantum observables from classical data,”arXiv preprint arXiv:2405.02027, 2024

    arXiv 2024

  8. [8]

    Trustworthy and reliable computing using untrusted and unreliable quantum hardware,

    S. Upadhyay and S. Ghosh, “Trustworthy and reliable computing using untrusted and unreliable quantum hardware,”Frontiers in Computer Science, vol. 6, p. 1431788, 2024

    work page 2024

Show all 40 references
  1. [9]

    When federated learning meets quantum computing: Survey and research opportunities,

    A. Mathur, A. Gupta, and S. K. Das, “When federated learning meets quantum computing: Survey and research opportunities,”arXiv preprint arXiv:2504.08814, 2025

    2025

  2. [10]

    A comparative analysis of hybrid-quantum classical neural networks,

    K. Zaman, T. Ahmed, M. A. Hanif, A. Marchisio, and M. Shafique, “A comparative analysis of hybrid-quantum classical neural networks,” in World Congress in Computer Science, Computer Engineering & Applied Computing. Springer, 2024, pp. 102–115

    2024

  3. [11]

    Machine learning on quantum experimental data toward solving quantum many-body problems,

    G. Cho and D. Kim, “Machine learning on quantum experimental data toward solving quantum many-body problems,”Nature Communica- tions, vol. 15, no. 1, p. 7552, 2024

    2024

  4. [12]

    Physics-informed bayesian optimization of variational quantum circuits,

    K. Nicoli, C. J. Anders, L. Funcke, T. Hartung, K. Jansen, S. K ¨uhn, K.- R. M ¨uller, P. Stornati, P. Kessel, and S. Nakajima, “Physics-informed bayesian optimization of variational quantum circuits,”Advances in Neural Information Processing Systems, vol. 36, pp. 18 341–18 376, 2023

    2023

  5. [13]

    A framework for demonstrating practical quantum advantage: comparing quantum against classical generative models,

    M. Hibat-Allah, M. Mauri, J. Carrasquilla, and A. Perdomo-Ortiz, “A framework for demonstrating practical quantum advantage: comparing quantum against classical generative models,”Communications Physics, vol. 7, no. 1, p. 68, 2024

    2024

  6. [14]

    Exponential con- centration in quantum kernel methods,

    S. Thanasilp, S. Wang, M. Cerezo, and Z. Holmes, “Exponential con- centration in quantum kernel methods,”Nature communications, vol. 15, no. 1, p. 5200, 2024

    2024

  7. [15]

    Evolutionary optimization for designing variational quantum circuits with high model capacity,

    S. Y .-C. Chen, “Evolutionary optimization for designing variational quantum circuits with high model capacity,”Quantum Physics, 2024, dOI:10.48550/arXiv.2412.12484

    2024 doi arXiv

  8. [16]

    Quantum kernel methods under scrutiny: a benchmarking study,

    J. Schnabel and M. Roth, “Quantum kernel methods under scrutiny: a benchmarking study,”Quantum Machine Intelligence, vol. 7, p. 58, 2025

    2025

  9. [17]

    Quantum data encoding: a comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy,

    M. Rath and H. Date, “Quantum data encoding: a comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy,”EPJ Quantum Technology, vol. 11, p. 72, 2024

    2024

  10. [18]

    Paving the way to hybrid quantum–classical scientific workflows,

    S. S. Cranganore, V . De Maio, I. Brandic, and E. Deelman, “Paving the way to hybrid quantum–classical scientific workflows,”Future Generation Computer Systems, vol. 152, pp. 1–12, 2024

    2024

  11. [19]

    Noisy intermediate-scale quantum algorithms,

    K. Bharti, A. Cervera-Lierta, T. H. Kyaw, T. Haug, S. Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, J. S. Kottmann, T. Menkeet al., “Noisy intermediate-scale quantum algorithms,”Reviews of Modern Physics, vol. 94, no. 1, p. 015004, 2022

    2022

  12. [20]

    Generalization in quantum machine learning from few training data,

    M. C. Caro and et al., “Generalization in quantum machine learning from few training data,”Nature Communications, vol. 13, no. 1, p. 4919, 2022

    2022

  13. [21]

    Regressions on quantum neural networks at maximal expres- sivity,

    I. Panadero, Y . Ban, H. Espin ´os, R. Puebla, J. Casanova, and E. Tor- rontegui, “Regressions on quantum neural networks at maximal expres- sivity,”Scientific Reports, vol. 14, no. 1, p. 31669, 2024

    2024

  14. [22]

    A study on optimization techniques for variational quantum circuits in reinforcement learning,

    M. K ¨olle, T. Witter, T. Rohe, G. Stenzel, P. Altmann, and T. Gabor, “A study on optimization techniques for variational quantum circuits in reinforcement learning,” in2024 IEEE International Conference on Quantum Software (QSW). IEEE, 2024, pp. 157–167

    2024

  15. [23]

    Quantum phases classification using quantum machine learning with shap-driven feature selection,

    G. S. Franco, F. Mahlow, P. M. Prado, G. E. Pexe, L. A. Rattighieri, and F. F. Fanchini, “Quantum phases classification using quantum machine learning with shap-driven feature selection,”arXiv preprint arXiv:2504.10673, 2025

    2025 arXiv

  16. [24]

    Data re-uploading in quantum machine learning for time series: appli- cation to traffic forecasting,

    N. Schetakis, P. Bonfini, N. Alisoltani, K. Blazakis, S. I. Tsintzos, A. Askitopoulos, D. Aghamalyan, P. Fafoutellis, and E. I. Vlahogianni, “Data re-uploading in quantum machine learning for time series: appli- cation to traffic forecasting,”arXiv preprint arXiv:2501.12776, 2025

    2025 arXiv

  17. [25]

    Regression and classification with single-qubit quantum neural networks,

    L. C. Souza, B. C. Guingo, G. Giraldi, and R. Portugal, “Regression and classification with single-qubit quantum neural networks,”arXiv preprint arXiv:2412.09486, 2024

    2024 arXiv

  18. [27]

    Hybrid quantum-classical machine learning: A survey,

    C. De Luca, “Hybrid quantum-classical machine learning: A survey,”Journal of Artificial Intelligence and Technology, vol. 1, no. 1, pp. 32–47, 2021. [Online]. Available: https://ojs.istp- press.com/jait/article/view/60

    2021

  19. [28]

    Quantum natural language processing,

    D. Widdows, W. Aboumrad, D. Kim, S. Ray, and J. Mei, “Quantum natural language processing,”arXiv preprint arXiv:2403.19758, 2024

    2024 arXiv

  20. [29]

    Beyond barren plateaus: Quantum variational algorithms are swamped with traps (2022),

    E. R. Anschuetz and B. T. Kiani, “Beyond barren plateaus: Quantum variational algorithms are swamped with traps (2022),”arXiv preprint arXiv:2205.05786, 2022

    2022 arXiv

  21. [30]

    Variational quantum algorithms,

    M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, and P. J. Coles, “Variational quantum algorithms,”Nature Reviews Physics, vol. 3, no. 9, pp. 625–644, 2021

    2021

  22. [31]

    Modelling and simulating the noisy behaviour of near-term quantum computers,

    K. Georgopoulos, C. Emary, and P. Zuliani, “Modelling and simulating the noisy behaviour of near-term quantum computers,”Physical Review A, vol. 104, no. 6, p. 062432, 2021

    2021

  23. [32]

    Barren plateaus in quantum neural network training landscapes,

    J. McClean, S. Boixo, V . Smelyanskiy, R. Babbush, and H. Neven, “Barren plateaus in quantum neural network training landscapes,”Nature Communications, vol. 9, no. 1, p. 4812, 2018

    2018

  24. [33]

    Towards understanding the power of quantum kernels in the nisq era,

    X. Wang, Y . Du, Y . Luo, and D. Tao, “Towards understanding the power of quantum kernels in the nisq era,”arXiv preprint arXiv:2103.16774, 2021

    2021 arXiv

  25. [34]

    Qdataset: Quantum datasets for machine learning,

    E. Perrier, A. Youssry, and C. Ferrie, “Qdataset: Quantum datasets for machine learning,”arXiv preprint arXiv:2108.06661, 2021

    2021 arXiv

  26. [35]

    Information-theoretic bounds on quantum advantage in machine learning,

    H.-Y . Huang, R. Kueng, and J. Preskill, “Information-theoretic bounds on quantum advantage in machine learning,”Physical Review Letters, vol. 126, no. 19, p. 190505, 2021

    2021

  27. [36]

    Quantum machine learning: a classical perspective,

    C. Ciliberto, M. Herbster, A. D. Ialongo, M. Pontil, A. Rocchetto, S. Severini, and L. Wossnig, “Quantum machine learning: a classical perspective,”Proceedings of the Royal Society A: Mathematical, Phys- ical and Engineering Sciences, vol. 474, no. 2209, p. 20170551, 2018

    2018

  28. [37]

    Quantum delegated and federated learning via quantum homomorphic encryption,

    W. Li and D.-L. Deng, “Quantum delegated and federated learning via quantum homomorphic encryption,”Research Directions: Quantum Technologies, vol. 3, p. e3, 2025

    2025

  29. [38]

    A quantum federated learning framework for classical clients,

    Y . Song, Y . Wu, S. Wu, D. Li, Q. Wen, S. Qin, and F. Gao, “A quantum federated learning framework for classical clients,”Science China Physics, Mechanics & Astronomy, vol. 67, no. 5, p. 250311, 2024

    2024

  30. [39]

    Federated learning with quantum secure aggregation,

    Y . Zhang, C. Zhang, C. Zhang, L. Fan, B. Zeng, and Q. Yang, “Federated learning with quantum secure aggregation,”arXiv preprint arXiv:2207.07444, 2022

    2022 arXiv

  31. [40]

    Quantum machine learning with differential privacy,

    W. M. Watkins, S. Y .-C. Chen, and S. Yoo, “Quantum machine learning with differential privacy,”Scientific Reports, vol. 13, no. 1, p. 2453, 2023

    2023

  32. [2024]

    Available: https://arxiv.org/abs/2401.11351

    [Online]. Available: https://arxiv.org/abs/2401.11351

    arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.