Fractal dimension predicts quantum kernel collapse in angle-encoded data
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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.
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References · 13
- 2018Supervised learning with quantum enhanced feature spacescited by 654
- 2020The power of quantum neural networkscited by 377
- 2020Power of data in quantum machine learningcited by 336
- 2018Estimating the intrinsic dimension of datasets by a minimal neighborhood informationcited by 246
- 2021The Inductive Bias of Quantum Kernelscited by 90
- 2022Exponential concentration in quantum kernel methodscited by 89
- 2023Understanding quantum machine learning also requires rethinking generalizationcited by 48
- 2022Bandwidth Enables Generalization in Quantum Kernel Modelscited by 32
- 2021Importance of Kernel Bandwidth in Quantum Machine Learningcited by 31
- 2024Quantum Kernel Methods under Scrutiny: A Benchmarking Studycited by 22
- 2024Neural auto-designer for enhanced quantum kernelscited by 7
- 2024Mitigating exponential concentration in covariant quantum kernels for subspace and real-world datacited by 7
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- 2025Learning Reduced Representations for Quantum Classifierscited by 2
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