mlsanityData to 14 Oct 2026

Is data-efficient learning feasible with quantum models?

arXiv 2508.19437 ↗First posted 26 Aug 2025quant-ph cs.LGcited by 0

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Abstract (arXiv)

The importance of analyzing nontrivial datasets when testing quantum machine learning (QML) models is becoming increasingly prominent in literature, yet a cohesive framework for understanding dataset characteristics remains elusive. In this work, we introduce a data-generation tool that allows to construct semi-artificial classical datasets tailored to quantum kernel methods (QKMs). Using this tool, we show that on fully classical datasets, QKMs can require fewer training examples than classical kernels to reach comparable error, providing clear empirical evidence that data-efficient learning with quantum models is possible on classical data. The main motivation behind this tool is to enable the community to perform controlled studies to figure out which dataset characteristics are particularly fitting for quantum models by tuning the data-generation procedure. Additionally, our study brings a spectral-bias-based generalization metric from classical kernel methods into the QML domain and show that the performance predicted by this metric aligns closely with empirical results, thereby closing an important gap between theory and practice in QML generalization. Our tool paves the way for a systematic exploration of dataset complexities. This could potentially contribute to a deeper understanding of the generalization benefits of QKM models (extendable to a broader family of QML models) and shifts the search for quantum advantage from ad hoc benchmark hunting to principled dataset design.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 46 references that are in the map. First posted on arXiv 26 Aug 2025.

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References · 30

  1. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,621
  2. 2018Barren plateaus in quantum neural network training landscapescited by 630
  3. 2020The power of quantum neural networkscited by 379
  4. 2020Power of data in quantum machine learningcited by 338
  5. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 333
  6. 2018Regularisation of Neural Networks by Enforcing Lipschitz Continuitycited by 314
  7. 2020A rigorous and robust quantum speed-up in supervised machine learningcited by 271
  8. 2021Generalization in quantum machine learning from few training datacited by 216
  9. 2021Supervised quantum machine learning models are kernel methodscited by 200
  10. 2018Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networkscited by 195
  11. 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
  12. 2020Spectral Bias and Task-Model Alignment Explain Generalization in Kernel Regression and Infinitely Wide Neural Networkscited by 166
18 more
  1. 2021Quantum machine learning beyond kernel methodscited by 92
  2. 2021The Inductive Bias of Quantum Kernelscited by 91
  3. 2022Exponential concentration in quantum kernel methodscited by 90
  4. 2022Learning Smooth Neural Functions via Lipschitz Regularizationcited by 86
  5. 2021Training Quantum Embedding Kernels on Near-Term Quantum Computerscited by 69
  6. 2022Exploiting symmetry in variational quantum machine learningcited by 69
  7. 2023Understanding quantum machine learning also requires rethinking generalizationcited by 49
  8. 2020A case for new neural network smoothness constraintscited by 43
  9. 2022Classical surrogates for quantum learning modelscited by 39
  10. 2024Quantum Convolutional Neural Networks are Effectively Classically Simulablecited by 37
  11. 2022Bandwidth Enables Generalization in Quantum Kernel Modelscited by 32
  12. 2021Importance of Kernel Bandwidth in Quantum Machine Learningcited by 31
  13. 2024Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Losscited by 14
  14. 2023A Hyperparameter Study for Quantum Kernel Methodscited by 9
  15. 2024Predominant Aspects on Security for Quantum Machine Learning: Literature Reviewcited by 9
  16. 2025On the similarity of bandwidth-tuned quantum kernels and classical kernelscited by 3
  17. 2025Generalization Bounds in Hybrid Quantum-Classical Machine Learning Modelscited by 3
  18. 2024Sobolev neural network with residual weighting as a surrogate in linear and non-linear mechanicscited by 2

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