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arxiv: 2607.01197 · v2 · pith:I2MYYOEInew · submitted 2026-07-01 · 💻 cs.LG

Quantum vs. Classical Machine Learning: A Unified Empirical Comparison

Chuanming Yu , Jiaming Liu , Zihao Ge , Xiongfei Wu , Lulu Zhu , Pengzhan Zhao , Jianjun Zhao This is my paper

Pith reviewed 2026-07-03 21:35 UTC · model grok-4.3

classification 💻 cs.LG
keywords quantum machine learningclassical machine learningempirical comparisonsupervised learningreinforcement learningperformance evaluationnoise filtering
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The pith

Quantum machine learning models do not yet surpass classical baselines in prediction performance, policy stability, or training time.

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

This paper runs a direct empirical comparison of seven quantum-classical model pairs across supervised learning and reinforcement learning tasks. It shows the quantum versions fall short on standard measures of accuracy, stability during learning, and computation time. A reader would care because quantum approaches are frequently described as offering future speed or capability gains in machine learning, and this study supplies concrete side-by-side numbers on current hardware. The work also flags a possible niche for quantum models in noise reduction and false-positive control while listing open problems in hardware limits, training speed, and stable convergence.

Core claim

The evaluated quantum machine learning models do not yet surpass the classical baselines in overall prediction performance, policy stability, or training time. Nevertheless, QML remains a promising approach for filtering noise and controlling false positives. The findings summarize challenges facing quantum machine learning across hardware environments, training efficiency, and convergence stability.

What carries the argument

The unified set of seven paired quantum and classical models run on shared datasets and environments to enable direct performance measurement.

If this is right

  • Current quantum models face documented limits in hardware compatibility, training efficiency, and convergence stability.
  • Future QML work should prioritize robustness improvements and better parameter tuning.
  • Quantum approaches may still find use in tasks where noise filtering or false-positive reduction is the primary goal.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Repeating the same paired comparisons on larger or fault-tolerant quantum devices could reveal performance shifts not visible on today's hardware.
  • The same benchmark structure could be applied to test other non-classical computing approaches against standard machine learning baselines.
  • Expanding the datasets beyond the ones used here would test whether the observed patterns hold in more varied real-world settings.

Load-bearing premise

The seven chosen model pairs and the specific datasets or environments used are representative enough of the broader space of quantum versus classical machine learning.

What would settle it

A controlled experiment in which any of the tested quantum models, or a close variant, exceeds the corresponding classical model on prediction performance, policy stability, or training time under the same conditions.

Figures

Figures reproduced from arXiv: 2607.01197 by Chuanming Yu, Jiaming Liu, Jianjun Zhao, Lulu Zhu, Pengzhan Zhao, Xiongfei Wu, Zihao Ge.

Figure 1
Figure 1. Figure 1: Cross-Paradigm Unified Framework for QML vs. CML Gaussian noise (parameterized by the standard deviation σ) is added to the im￾ages before normalization. Furthermore, for specific quantum models constrained by the number of available qubits (e.g., QSVM), dimensionality reduction tech￾niques such as Principal Component Analysis (PCA) can be applied to project features into a lower-dimensional space. This co… view at source ↗
read the original abstract

Quantum computing has emerged as a promising computational paradigm for machine learning (ML), with the potential to offer computational advantages over classical approaches. At this stage, the evidence supporting the performance and advantages of quantum machine learning (QML) models relative to classical models is insufficient. To address this gap, this paper presents an empirical study on the performance of QML models and their classical counterparts. We compare seven model pairs spanning supervised learning and reinforcement learning. Our results indicate that the evaluated quantum machine learning models do not yet surpass the classical baselines in overall prediction performance, policy stability, or training time. Nevertheless, QML remains a promising approach for filtering noise and controlling false positives. Our research findings summarize the challenges facing quantum machine learning across hardware environments, training efficiency, and convergence stability, providing a foundation for research into the robustness and parameter optimization of QML. This work is publicly available at https://github.com/Z-537-437/QML.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper presents an empirical comparison of seven pairs of quantum machine learning models against classical counterparts across supervised learning and reinforcement learning tasks. It reports that the QML models do not surpass the classical baselines in prediction performance, policy stability, or training time, while suggesting promise for noise filtering and false-positive control, and summarizes challenges in hardware, efficiency, and stability.

Significance. If the empirical findings are robust, the work supplies a unified benchmark that documents current limitations of QML relative to classical methods and identifies directions for improving robustness and optimization; the public GitHub repository further strengthens the contribution by supporting reproducibility.

major comments (2)
  1. [Abstract] Abstract: the central claim that QML models 'do not yet surpass the classical baselines in overall prediction performance, policy stability, or training time' is stated without any enumeration of the seven model pairs, description of quantum circuit ansatze or embedding methods, datasets, statistical tests, error bars, or hyperparameter controls, rendering the support for the claim unevaluable from the provided information.
  2. [Abstract] Abstract and implied methods: the conclusion that the evaluated QML models do not surpass classical baselines is presented as a general finding, yet no justification is given that the seven chosen pairs and the specific datasets/environments adequately sample the space of tasks where quantum advantage might appear; without such sampling argument the non-surpassing result cannot be distinguished from an artifact of benchmark selection.
minor comments (1)
  1. [Abstract] Abstract: the statement that 'QML remains a promising approach for filtering noise and controlling false positives' is asserted without any accompanying quantification, table, or figure reference.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We address each major comment below and will revise the abstract and add discussion to improve clarity and support for our claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that QML models 'do not yet surpass the classical baselines in overall prediction performance, policy stability, or training time' is stated without any enumeration of the seven model pairs, description of quantum circuit ansatze or embedding methods, datasets, statistical tests, error bars, or hyperparameter controls, rendering the support for the claim unevaluable from the provided information.

    Authors: We agree the abstract is concise and omits these specifics. The main text enumerates the seven pairs (QSVM vs SVM, QNN vs NN, etc.), describes ansatze (hardware-efficient) and embeddings (amplitude/angle), lists datasets/environments, and reports statistical tests with error bars and hyperparameter details in Sections 3-5. We will revise the abstract to briefly list the model pairs and direct readers to the experimental setup in the body. revision: yes

  2. Referee: [Abstract] Abstract and implied methods: the conclusion that the evaluated QML models do not surpass classical baselines is presented as a general finding, yet no justification is given that the seven chosen pairs and the specific datasets/environments adequately sample the space of tasks where quantum advantage might appear; without such sampling argument the non-surpassing result cannot be distinguished from an artifact of benchmark selection.

    Authors: The pairs were chosen as representative QML approaches from the literature spanning supervised and RL tasks, using standard benchmarks (UCI datasets, Gym environments) to enable direct comparison under NISQ constraints. We will add a methods paragraph explaining this selection rationale, the focus on common tasks, and the inherent limits on generalizing the no-advantage finding beyond these benchmarks. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical comparison with no derivations or self-referential fits

full rationale

The paper conducts an empirical study by running seven model pairs on chosen datasets/environments and reports observed performance metrics. No equations, parameter fittings, uniqueness theorems, or ansatze are invoked in the provided text. The central claim is a direct summary of experimental outcomes rather than a derivation that reduces to its inputs by construction. Self-citations, if present, are not load-bearing for any mathematical result. This matches the default expectation for non-circular empirical work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is an empirical comparison study and introduces no new theoretical axioms, free parameters, or invented entities beyond standard machine-learning experimental assumptions.

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Works this paper leans on

20 extracted references · 20 canonical work pages · 1 internal anchor

  1. [1]

    UMYU Scientifica2(4), 65–75 (2023)

    Adebayo, P., Basaky, F., Osaghae, E.: Variational quantum-classical algorithms: A review of theory, applications, and opportunities. UMYU Scientifica2(4), 65–75 (2023)

    work page 2023

  2. [2]

    Authorea Preprints (2025)

    Adnan, M.: Competitive performance of quantum algorithms leveraging qml, pen- nylane, and fujitsu qulacs against classic algorithms. Authorea Preprints (2025)

    work page 2025

  3. [3]

    classical ma- chine learning: A benchmark study for financial prediction

    Ahmad, R., Kashif, M., Innan, N., Shafique, M.: Quantum vs. classical ma- chine learning: A benchmark study for financial prediction. arXiv preprint arXiv:2601.03802 (2026) Quantum vs. Classical Machine Learning: A Unified Empirical Comparison 13

    work page arXiv 2026

  4. [4]

    Journal of Multiscale Materials Informatics1(2), 1–8 (2024)

    Akrom, M.: Quantum support vector machine for classification task: A review. Journal of Multiscale Materials Informatics1(2), 1–8 (2024)

    work page 2024

  5. [5]

    PennyLane: Automatic differentiation of hybrid quantum-classical computations

    Bergholm, V., Izaac, J., Schuld, M., Gogolin, C., Ahmed, S., Ajith, V., Alam, M.S., Alonso-Linaje, G., AkashNarayanan, B., Asadi, A., et al.: Pennylane: Au- tomatic differentiation of hybrid quantum-classical computations. arXiv preprint arXiv:1811.04968 (2018)

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  6. [6]

    Quantum machine learning,

    Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., Lloyd, S.: Quantum machine learning. Nature549(7671), 195–202 (sep 2017). https://doi.org/10.1038/nature23474, https://doi.org/10.1038%2Fnature23474

    work page doi:10.1038/nature23474 2017

  7. [7]

    and Arrasmith, Andrew and Babbush, Ryan and Benjamin, Simon C

    Cerezo, M., Arrasmith, A., Babbush, R., Benjamin, S.C., Endo, S., Fujii, K., McClean, J.R., Mitarai, K., Yuan, X., Cincio, L., Coles, P.J.: Varia- tional quantum algorithms. Nature Reviews Physics3(9), 625–644 (sep 2021). https://doi.org/10.1038/s42254-021-00348-9, https://doi.org/10.1038/s42254-021- 00348-9

    work page doi:10.1038/s42254-021-00348-9 2021

  8. [8]

    Chakraborty, S.: Classical machine learning vs quantum machine learning in com- putational chemistry: Hype, hope, and horizons (2025)

    work page 2025

  9. [9]

    In: 2025 International Wireless Communications and Mobile Computing (IWCMC)

    Chen, K.C., Chen, S.Y.C., Liu, C.Y., Leung, K.K.: Toward large-scale distributed quantum long short-term memory with modular quantum computers. In: 2025 International Wireless Communications and Mobile Computing (IWCMC). pp. 337–342. IEEE (2025)

    work page 2025

  10. [10]

    Nature Physics15(12), 1273–1278 (2019)

    Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nature Physics15(12), 1273–1278 (2019)

    work page 2019

  11. [11]

    Meth- odsX14, 103318 (2025)

    Devadas, R.M., Sowmya, T.: Quantum machine learning: A comprehensive review of integrating ai with quantum computing for computational advancements. Meth- odsX14, 103318 (2025)

    work page 2025

  12. [12]

    Supervised learning with quantum-enhanced feature spaces,

    Havlíček, V., Córcoles, A.D., Temme, K., Harrow, A.W., Kandala, A., Chow, J.M., Gambetta, J.M.: Supervised learning with quantum-enhanced feature spaces. Na- ture567(7747), 209–212 (2019). https://doi.org/10.1038/s41586-019-0980-2

    work page doi:10.1038/s41586-019-0980-2 2019

  13. [13]

    arXiv preprint arXiv:2502.04909 (2025)

    Kruse, G., Coelho, R., Rosskopf, A., Wille, R., Lorenz, J.M.: Benchmarking quan- tum reinforcement learning. arXiv preprint arXiv:2502.04909 (2025)

    work page arXiv 2025

  14. [14]

    IEEE Access (2025)

    Lamichhane, P., Rawat, D.B.: Quantum machine learning: Recent advances, chal- lenges and perspectives. IEEE Access (2025)

    work page 2025

  15. [15]

    In: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE)

    Meyer, N., Ufrecht, C., Periyasamy, M., Plinge, A., Mutschler, C., Scherer, D.D., Maier, A.: Qiskit-torch-module: Fast prototyping of quantum neural networks. In: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). vol. 1, pp. 817–823. IEEE (2024)

    work page 2024

  16. [16]

    ACM Computing Surveys58(4), 1–35 (2025)

    Rodríguez-Díaz, F., Gutiérrez-Avilés, D., Troncoso, A., Martínez-Álvarez, F.: A survey of quantum machine learning: Foundations, algorithms, frameworks, data and applications. ACM Computing Surveys58(4), 1–35 (2025)

    work page 2025

  17. [17]

    In: Quantum Computing, Communication, and Sim- ulation IV

    Sharna, N.A., Islam, E.: Quantum machine learning approach for classification: case studies and implications. In: Quantum Computing, Communication, and Sim- ulation IV. vol. 12911, pp. 181–194. SPIE (2024)

    work page 2024

  18. [18]

    In: 2024 IEEE In- ternational Conference on Big Data (BigData)

    Tasnim, T., Rahman, M., Wu, F.: Comparison of cnn and qcnn performance in binary classification of breast cancer histopathological images. In: 2024 IEEE In- ternational Conference on Big Data (BigData). pp. 3780–3787. IEEE (2024)

    work page 2024

  19. [19]

    In: 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC)

    Tasnim, T., Saha, A., Rahman, M., Wu, F.: Quantum vs classical: Performance benchmarking of cnn and qcnn in binary image classification. In: 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC). pp. 00203–00208. IEEE (2025)

    work page 2025

  20. [20]

    npj Quantum Information (2019)

    Zoufal, C., Lucchi, A., Woerner, S.: Quantum generative adversarial networks for learning and loading random distributions. npj Quantum Information (2019)

    work page 2019