Margin-Based Generalization for Quantum Machine Learning with Classical Data Encoding

Key points

• Understanding generalization is essential for assessing the practical viability of quantum machine learning.1
• 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.1
• For fixed encodings, the existing margin bound transfers unchanged.1
• 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 number of trainable encoding parameters and $m$ is the training-sample size.1

Sources (1)

• [1]Margin-Based Generalization for Quantum Machine Learning with Classical Data Encoding
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 03:43 AM

  We develop a margin-based generalization framework for this setting, covering both fixed and trainable quantum data encodings.

  Understanding generalization is essential for assessing the practical viability of quantum machine learning.

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