Author

A. P. Appel

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Preprint Sep 2026

Quantum Encoding Agents: A Natural Language Interface for Data Embedding Strategy Selection in Quantum Machine Learning

Selecting a data encoding is a central and poorly tooled decision in quantum machine learning. The feature map fixes the geometry of the Hilbert space, the expressibility of quantum kernels, and whether the circuit can run on near-term hardware. This paper presents Quantum Encoding Agents, an open-source system that tu...

A. P. Appel · 0 citations

#machine learning Preprint Aug 2026

Fractal dimension predicts quantum kernel collapse in angle-encoded data

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 sta...

A. P. Appel · 0 citations