Margin-Based Generalization for Quantum Machine Learning with Classical Data Encoding
Understanding generalization is essential for assessing the practical viability of quantum machine learning. In many applications, quantum models operate on classical data that must first be encoded into quantum states, making the encoding part of the effective hypothesis class. We develop a margin-based generalization framework for this setting, covering both fixed and trainable quantum data encodings. For fixed encodings, the existing margin bound transfers unchanged. For trainable encodings, we derive a bound using a two-scale covering argument that combines the original margin-dependent term with an additive encoding-capacity term of order $\tilde O(\sqrt{L/m})$, where $L$ is the numb...
| Authors | Ju-Young Ryu, Tak Hur, Daniel K. Park |
|---|---|
| Primary category | quant-ph |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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