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arxiv: 2605.19417 · v1 · pith:GBKX2CRTnew · submitted 2026-05-19 · 🪐 quant-ph

Towards Fair Benchmarking of Quantum Transfer Learning for Visual Classification

Nouhaila Innan , Saim Rehman , Muhammad Shafique This is my paper

Pith reviewed 2026-05-20 06:14 UTC · model grok-4.3

classification 🪐 quant-ph
keywords quantum transfer learningbenchmarkingvisual classificationhybrid quantum-classical modelsnear-term quantum devicesimage classificationcircuit designresource-aware evaluation
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The pith

A controlled benchmark of quantum transfer learning methods finds no single approach outperforms the others across visual classification tasks.

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

The paper introduces a standardized evaluation pipeline that applies identical preprocessing, frozen classical feature extractors, training rules, and metrics to five representative quantum transfer learning methods. It tests them on Fashion-MNIST and Hymenoptera datasets, with CIFAR-10 added for harder natural images, while also measuring circuit size, trainable parameters, training time, and sensitivity to qubit count and depth. The central result is that performance rankings shift depending on the dataset, encoding choice, circuit layout, and resource budget. A reader cares because near-term quantum hardware limits qubit number and circuit depth, so knowing which hybrid setup matches a given task and cost helps decide what to try first. The work therefore supplies practical selection guidance rather than declaring one best method.

Core claim

Under a shared transfer-learning pipeline with frozen backbones, the five compared QTL families (DQN-QTL, QPIE-QTL, AE-CQTL, PVCQTL, ED-QTL) produce accuracy and resource profiles that vary with dataset, encoding strategy, circuit design, and computational cost; consequently no single family is superior in every setting.

What carries the argument

The unified transfer-learning pipeline that fixes preprocessing rules, frozen-backbone settings, training conditions, and reporting metrics so that circuit size, parameter count, training time, and performance can be compared directly across methods.

If this is right

  • Accuracy rankings change when moving from grayscale datasets like Fashion-MNIST to color datasets like CIFAR-10.
  • Encoding strategy and circuit depth trade off against both predictive performance and wall-clock training time.
  • Methods must be evaluated on qubit scaling and parameter count, not accuracy alone, before deployment on near-term hardware.
  • Hybrid models require case-by-case selection once dataset complexity and resource limits are specified.

Where Pith is reading between the lines

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

  • Future QTL papers could adopt the same fixed pipeline as a minimal reporting standard to make results comparable.
  • The observed sensitivity to qubit count suggests testing whether certain encodings remain useful when qubit budgets drop below the values used here.
  • Extending the benchmark to other modalities such as time-series or graph data would test whether the same dependence on design choices holds outside images.

Load-bearing premise

The five chosen QTL methods together with Fashion-MNIST, Hymenoptera, and CIFAR-10 are representative enough of quantum transfer learning and visual tasks to yield general selection guidance.

What would settle it

Re-running the identical pipeline on a substantially different collection of image datasets or backbone architectures and finding one method consistently highest in accuracy at lowest cost across all of them would undermine the claim that no family dominates.

Figures

Figures reproduced from arXiv: 2605.19417 by Muhammad Shafique, Nouhaila Innan, Saim Rehman.

Figure 1
Figure 1. Figure 1: Hybrid quantum-classical learning architecture and its spe [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Controlled benchmark protocol for evaluating QTL methods. The pipeline standardizes data processing, frozen-backbone feature [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: F1 score comparison across QTL configurations on Fashion-MNIST and Ants & Bees. Each bar represents one model variant, and [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Performance-cost trade-off across QTL configurations on Fashion-MNIST and Ants & Bees. Each point represents one model [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qubit-scaling behavior of QTL configurations on the two main benchmark datasets. Increasing the number of qubits from [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
read the original abstract

Quantum Transfer Learning (QTL) offers a promising approach for visual quantum machine learning under near-term constraints, where limited qubit counts, shallow circuit depths, and costly hybrid optimization restrict end-to-end quantum training. In this setting, pretrained classical backbones can extract high-level visual features, while compact quantum modules operate as trainable classification heads. However, existing QTL results are difficult to compare because they often differ in datasets, preprocessing, backbone settings, qubit budgets, circuit designs, optimization choices, and reporting protocols. This work presents a controlled benchmarking methodology for evaluating representative QTL methods under a unified transfer-learning pipeline. The benchmark compares DQN-QTL, QPIE-QTL, AE-CQTL, PVCQTL, and ED-QTL under shared preprocessing rules, frozen-backbone settings, training conditions, and reporting metrics. The evaluation focuses on Fashion-MNIST and Hymenoptera Ants vs Bees as the two main datasets, while CIFAR-10 is used to provide additional configuration-level evidence on a harder natural-image task. Beyond predictive performance, the benchmark analyzes circuit size, trainable parameters, quantum parameters, training time, and architectural sensitivity to qubit count and circuit depth. The results show that no single QTL family dominates across all settings: performance depends on the dataset, encoding strategy, circuit design, and computational cost. These findings highlight the need for resource-aware QTL evaluation and provide guidance for selecting hybrid quantum-classical transfer models under near-term resource constraints.

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 manuscript proposes a controlled benchmarking methodology for Quantum Transfer Learning (QTL) methods in visual classification under near-term constraints. It evaluates five QTL approaches (DQN-QTL, QPIE-QTL, AE-CQTL, PVCQTL, ED-QTL) within a unified pipeline using shared preprocessing, frozen classical backbones, and consistent metrics, primarily on Fashion-MNIST and Hymenoptera datasets with supplementary tests on CIFAR-10. The central finding is that no single QTL family dominates across settings, with performance depending on dataset, encoding strategy, circuit design, and computational cost.

Significance. If the empirical comparisons hold under the unified conditions, the work is significant for establishing reproducible standards in quantum machine learning, where prior QTL studies have been difficult to compare due to inconsistent setups. By incorporating resource metrics such as circuit size, trainable parameters, and training time alongside accuracy, it promotes resource-aware evaluation of hybrid models, which could inform practical method selection for NISQ-era applications.

major comments (2)
  1. [Abstract] Abstract: The claim that 'no single QTL family dominates across all settings' and that 'performance depends on the dataset, encoding strategy, circuit design, and computational cost' is load-bearing for the guidance on method selection, yet rests on only five selected methods (DQN-QTL, QPIE-QTL, AE-CQTL, PVCQTL, ED-QTL) and three datasets (Fashion-MNIST, Hymenoptera, CIFAR-10). The manuscript must justify why this sample adequately represents the broader space of QTL variants (e.g., alternative feature-extraction layers, deeper circuits, or other encodings) to ensure the lack of dominance is not an artifact of the narrow scope.
  2. [Results and evaluation sections] Results and evaluation sections: The reported performance differences and sensitivity analyses to qubit count and circuit depth lack statistical details such as error bars from multiple random seeds, standard deviations, or significance tests. Without these, the comparisons between methods cannot be rigorously substantiated, particularly for the conclusion that outcomes vary with the listed factors.
minor comments (1)
  1. [Abstract] Abstract: The phrase 'additional configuration-level evidence' on CIFAR-10 is vague; specifying the exact configurations tested and how they extend the main results would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help improve the clarity and rigor of our work. We address each major comment point by point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that 'no single QTL family dominates across all settings' and that 'performance depends on the dataset, encoding strategy, circuit design, and computational cost' is load-bearing for the guidance on method selection, yet rests on only five selected methods (DQN-QTL, QPIE-QTL, AE-CQTL, PVCQTL, ED-QTL) and three datasets (Fashion-MNIST, Hymenoptera, CIFAR-10). The manuscript must justify why this sample adequately represents the broader space of QTL variants (e.g., alternative feature-extraction layers, deeper circuits, or other encodings) to ensure the lack of dominance is not an artifact of the narrow scope.

    Authors: We thank the referee for this observation. The five methods were deliberately chosen to span distinct encoding and architectural families commonly studied in near-term QTL literature: DQN-QTL for dense re-uploading networks, QPIE-QTL for phase-estimation-style encoding, AE-CQTL for autoencoder-assisted compression, PVCQTL for PCA-preprocessed variational circuits, and ED-QTL for entanglement-focused designs. The two primary datasets (Fashion-MNIST, Hymenoptera) are standard transfer-learning benchmarks, with CIFAR-10 included for supplementary evidence on a harder task. In the revision we will add an explicit paragraph in the Methods section justifying this selection as representative of the dominant QTL paradigms under NISQ constraints, while acknowledging that exhaustive coverage of all possible variants (deeper circuits, alternative backbones) lies beyond the present scope. This addition will clarify that the observed lack of dominance is not an artifact of an arbitrarily narrow sample. revision: yes

  2. Referee: [Results and evaluation sections] Results and evaluation sections: The reported performance differences and sensitivity analyses to qubit count and circuit depth lack statistical details such as error bars from multiple random seeds, standard deviations, or significance tests. Without these, the comparisons between methods cannot be rigorously substantiated, particularly for the conclusion that outcomes vary with the listed factors.

    Authors: We agree that statistical details are necessary to substantiate the comparisons. The results in the current manuscript are based on single runs, reflecting the computational expense of quantum simulations. In the revised version we will repeat the main experiments and sensitivity analyses across at least five independent random seeds, report mean accuracies together with standard deviations, and add error bars to the relevant figures. Where appropriate we will also include simple significance tests (e.g., paired t-tests or Wilcoxon tests) between methods. These changes will be incorporated into the Results and Evaluation sections to strengthen the evidence that performance varies with dataset, encoding, circuit design, and cost. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmarking with external datasets and metrics

full rationale

The paper conducts a controlled empirical comparison of five QTL methods (DQN-QTL, QPIE-QTL, AE-CQTL, PVCQTL, ED-QTL) on standard external datasets (Fashion-MNIST, Hymenoptera, CIFAR-10) under a unified pipeline with fixed preprocessing, frozen backbones, and shared metrics. No mathematical derivations, first-principles predictions, or parameter fits are claimed; all results are direct experimental outcomes. The central finding that no single family dominates is an observation from these benchmarks rather than a reduction to self-defined inputs or self-citations. The study is self-contained against external benchmarks with no load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on domain assumptions about method representativeness and fairness of the unified pipeline rather than mathematical derivations or new physical entities.

axioms (2)
  • domain assumption The selected QTL methods (DQN-QTL, QPIE-QTL, AE-CQTL, PVCQTL, ED-QTL) represent the main families of quantum transfer learning approaches.
    The benchmark treats these five as representative for drawing general conclusions.
  • domain assumption Shared preprocessing rules, frozen-backbone settings, and training conditions produce fair comparisons across methods.
    The evaluation protocol assumes these controls eliminate confounding factors.

pith-pipeline@v0.9.0 · 5792 in / 1358 out tokens · 64688 ms · 2026-05-20T06:14:32.597933+00:00 · methodology

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Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages · 3 internal anchors

  1. [1]

    Quantum machine learning,

    J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,”Nature, vol. 549, no. 7671, pp. 195–202, 2017

    work page 2017

  2. [2]

    Lep-qnn: Loan eligibility prediction using quantum neural networks,

    N. Innan, A. Marchisio, M. Bennai, and M. Shafique, “Lep-qnn: Loan eligibility prediction using quantum neural networks,” in2025 IEEE International Conference on Quantum Computing and Engineering (QCE), vol. 1. IEEE, 2025, pp. 1864–1872

    work page 2025

  3. [3]

    HQNN-FSP: A hybrid classical-quantum neural network for regression-based financial stock market prediction,

    P. K. Choudhary, N. Innan, M. Shafique, and R. Singh, “HQNN-FSP: A hybrid classical-quantum neural network for regression-based financial stock market prediction,”Quantum Machine Intelligence, vol. 8, no. 1, p. 55, 2026

    work page 2026

  4. [4]

    Quantum bayesian networks for machine learning in oil-spill detection,

    O. I. Siddiqui, N. Innan, A. Marchisio, M. Bennai, and M. Shafique, “Quantum bayesian networks for machine learning in oil-spill detection,” in2025 International Joint Conference on Neural Networks (IJCNN). IEEE, 2025, pp. 1–8

    work page 2025

  5. [5]

    Qnn-vrcs: A quantum neural network for vehicle road cooperation systems,

    N. Innan, B. K. Behera, S. Al-Kuwari, and A. Farouk, “Qnn-vrcs: A quantum neural network for vehicle road cooperation systems,”IEEE Transactions on Intelligent Transportation Systems, 2025

    work page 2025

  6. [6]

    Quantum vs. classical machine learning: A benchmark study for financial prediction,

    R. Ahmad, M. Kashif, N. Innan, and M. Shafique, “Quantum vs. classical machine learning: A benchmark study for financial prediction,”arXiv preprint arXiv:2601.03802, 2026

    work page arXiv 2026

  7. [7]

    Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease

    M. Kashif, H. M. Siraj, N. Innan, A. Marchisio, and M. Shafique, “Design space exploration of hybrid quantum neural networks for chronic kidney disease,”arXiv preprint arXiv:2604.13608, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  8. [8]

    Systematic literature review: Quantum machine learning and its applications,

    D. Peral-Garc´ıa, J. Cruz-Benito, and F. J. Garc ´ıa-Pe˜nalvo, “Systematic literature review: Quantum machine learning and its applications,” Computer Science Review, vol. 51, p. 100619, 2024

    work page 2024

  9. [9]

    Next- generation quantum neural networks: Enhancing efficiency, security, and privacy,

    N. Innan, M. Kashif, A. Marchisio, M. Bennai, and M. Shafique, “Next- generation quantum neural networks: Enhancing efficiency, security, and privacy,” in2025 IEEE 31st International Symposium on On-Line Testing and Robust System Design (IOLTS). IEEE, 2025, pp. 1–4

    work page 2025

  10. [10]

    Scaling Laws for Hybrid Quantum Neural Networks: Depth, Width, and Quantum-Centric Diagnostics

    D. Vyskubov, K. Vyskubov, N. Innan, and M. Shafique, “Scaling laws for hybrid quantum neural networks: Depth, width, and quantum-centric diagnostics,”arXiv preprint arXiv:2604.06007, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  11. [11]

    Robustness Evaluation of Hybrid Quantum Neural Networks under Noise Models via System-Level Error Mitigation

    J. R. M. Njiki, N. Innan, A. Marchisio, M. Kashif, J.-M. Dricot, and M. Shafique, “Robustness evaluation of hybrid quantum neural networks under noise models via system-level error mitigation,”arXiv preprint arXiv:2604.17515, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  12. [12]

    Transfer learning in hybrid classical-quantum neural networks,

    A. Mari, T. R. Bromley, J. Izaac, M. Schuld, and N. Killoran, “Transfer learning in hybrid classical-quantum neural networks,”Quantum, vol. 4, p. 340, 2020

    work page 2020

  13. [13]

    Quantum transfer learning for breast cancer detection,

    V . Azevedo, C. Silva, and I. Dutra, “Quantum transfer learning for breast cancer detection,”Quantum Machine Intelligence, vol. 4, no. 1, p. 5, 2022

    work page 2022

  14. [14]

    Quantum transfer learning for acceptability judgements,

    G. Buonaiuto, R. Guarasci, A. Minutolo, G. De Pietro, and M. Esposito, “Quantum transfer learning for acceptability judgements,”Quantum Machine Intelligence, vol. 6, no. 1, p. 13, 2024

    work page 2024

  15. [15]

    Classical-to-quantum convolutional neural network transfer learning,

    J. Kim, J. Huh, and D. K. Park, “Classical-to-quantum convolutional neural network transfer learning,”Neurocomputing, vol. 555, p. 126643, 2023

    work page 2023

  16. [16]

    Classical– quantum transfer learning for image classification,

    H. Mogalapalli, M. Abburi, B. Nithya, and S. K. V . Bandreddi, “Classical– quantum transfer learning for image classification,”SN Computer Science, vol. 3, no. 1, p. 20, 2022

    work page 2022

  17. [17]

    Classical-to-quantum transfer learning for spoken command recognition based on quantum neural networks,

    J. Qi and J. Tejedor, “Classical-to-quantum transfer learning for spoken command recognition based on quantum neural networks,” inICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022, pp. 8627–8631

    work page 2022

  18. [18]

    Quantum transfer learning for real-world, small, and high-dimensional remotely sensed datasets,

    S. Otgonbaatar, G. Schwarz, M. Datcu, and D. Kranzlm ¨uller, “Quantum transfer learning for real-world, small, and high-dimensional remotely sensed datasets,”IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 9223–9230, 2023

    work page 2023

  19. [19]

    Transfer learning,

    L. Torrey and J. Shavlik, “Transfer learning,” inHandbook of research on machine learning applications and trends: algorithms, methods, and techniques. IGI Global Scientific Publishing, 2010, pp. 242–264

    work page 2010

  20. [20]

    A survey of transfer learning,

    K. Weiss, T. M. Khoshgoftaar, and D. Wang, “A survey of transfer learning,”Journal of Big data, vol. 3, no. 1, p. 9, 2016

    work page 2016

  21. [21]

    A comprehensive survey on transfer learning,

    F. Zhuang, Z. Qi, K. Duan, D. Xi, Y . Zhu, H. Zhu, H. Xiong, and Q. He, “A comprehensive survey on transfer learning,”Proceedings of the IEEE, vol. 109, no. 1, pp. 43–76, 2020

    work page 2020

  22. [22]

    Deep transfer learning for image classification: a survey,

    J. Plested, M. Phiri, and T. Gedeon, “Deep transfer learning for image classification: a survey,”Artificial Intelligence Review, 2026

    work page 2026

  23. [23]

    Quantum transfer learning to boost dementia detection,

    S. Bhowmik, T. Perciano, and H. Thapliyal, “Quantum transfer learning to boost dementia detection,” inProceedings of the Great Lakes Symposium on VLSI 2025, 2025, pp. 849–853

    work page 2025

  24. [24]

    Quantum parallel information exchange (qpie) hybrid network with transfer learning,

    Z. Guo, A. Khan, V . S. Sheng, S. Jabeen, and Z. Pan, “Quantum parallel information exchange (qpie) hybrid network with transfer learning,” Quantum Science and Technology, vol. 10, no. 3, p. 035054, 2025

    work page 2025

  25. [25]

    An amplitude-encoding-based classical-quantum transfer learning framework: Outperforming classical methods in image recognition,

    S. Hu, X. Li, B. Ruan, and Z. Liu, “An amplitude-encoding-based classical-quantum transfer learning framework: Outperforming classical methods in image recognition,”arXiv preprint arXiv:2502.20184, 2025

    work page arXiv 2025

  26. [26]

    Post- variational classical quantum transfer learning for binary classification,

    K. Yogaraj, B. Quanz, T. Vikas, A. Mondal, and S. Mondal, “Post- variational classical quantum transfer learning for binary classification,” Scientific Reports, vol. 15, no. 1, p. 23682, 2025

    work page 2025

  27. [27]

    Bridging classical and quantum machine learning: Knowledge transfer from classical to quantum neural networks using knowledge distillation,

    M. J. Hasan and M. Mahdy, “Bridging classical and quantum machine learning: Knowledge transfer from classical to quantum neural networks using knowledge distillation,”arXiv preprint arXiv:2311.13810, 2023

    work page arXiv 2023