mlsanityData to 7 Oct 2026

Fractal dimension predicts quantum kernel collapse in angle-encoded data

arXiv 2609.00475 ↗First posted 31 Aug 2026quant-ph cs.LGcited by 0

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Abstract (arXiv)

Angle-encoded quantum kernels on tabular data collapse when the feature map is wider than the intrinsic dimension of the data. We propose the correlation fractal dimension D2 as an a priori qubit budget: encode D2 coordinates chosen by FD-ASE instead of the PCA-95% width or all E attributes. On nine data sets and a statevector simulator (n= 32), a one-layer ZZ fidelity kernel at q=D2 stays geometrically alive while the same kernel at the PCA-95% width has already collapsed. The budget is map-dependent: product-state and IQP maps overshoot it; a second ZZ layer undershoots it. Packed dense-angle and re-uploading encodings still live at the fractal q, but not when PCA-95% features are stacked onto those qubits. Shrinking the angle bandwidth moves the ZZ knee later; stretching it kills the kernel earlier. On IBM Quantum (ibm_fez, 256 shots, n=8) the one-layer ZZ kernel at the fractal width matches the exact kernel (MAE 0.021); past that width both hardware and simulator have collapsed. The ceiling is a property of the map-data pair at a stated bandwidth, not of the classical table alone.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 35 references that are in the map. First posted on arXiv 31 Aug 2026.

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References · 13

  1. 2018Supervised learning with quantum enhanced feature spacescited by 654
  2. 2020The power of quantum neural networkscited by 377
  3. 2020Power of data in quantum machine learningcited by 336
  4. 2018Estimating the intrinsic dimension of datasets by a minimal neighborhood informationcited by 246
  5. 2021The Inductive Bias of Quantum Kernelscited by 90
  6. 2022Exponential concentration in quantum kernel methodscited by 89
  7. 2023Understanding quantum machine learning also requires rethinking generalizationcited by 48
  8. 2022Bandwidth Enables Generalization in Quantum Kernel Modelscited by 32
  9. 2021Importance of Kernel Bandwidth in Quantum Machine Learningcited by 31
  10. 2024Quantum Kernel Methods under Scrutiny: A Benchmarking Studycited by 22
  11. 2024Neural auto-designer for enhanced quantum kernelscited by 7
  12. 2024Mitigating exponential concentration in covariant quantum kernels for subspace and real-world datacited by 7
1 more
  1. 2025Learning Reduced Representations for Quantum Classifierscited by 2

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