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Fractal dimension predicts quantum kernel collapse in angle-encoded data

By PulseAugur Editorial · [1 sources] ·

A new research paper proposes using the correlation fractal dimension (D2) as a method to determine the optimal qubit budget for angle-encoded quantum kernels. This approach aims to prevent kernel collapse, a phenomenon where the feature map becomes wider than the data's intrinsic dimension. The study demonstrates that encoding D2 coordinates, rather than using broader methods like PCA-95%, keeps the kernel geometrically 'alive' and matches exact kernel results on simulators and IBM Quantum hardware. AI

IMPACT This research could improve the efficiency and accuracy of quantum machine learning models by optimizing qubit usage.

RANK_REASON Academic paper on a novel method for quantum kernel analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Fractal dimension predicts quantum kernel collapse in angle-encoded data

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Academic paper on a novel method for quantum kernel analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 Italiano(IT) · Ana Paula Appel ·

    Fractal dimension predicts quantum kernel collapse in angle-encoded data

    arXiv:2609.00475v1 Announce Type: cross Abstract: 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 chos…

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