A research paper introducing the 'class margin' metric for binary classification tasks in quantum machine learning (QML). The metric merges average randomness and classification margin to analytically connect data-induced randomness with classification accuracy for a given data-embedding map. The authors benchmark various data-embedding strategies using class margin, demonstrating its ability to identify randomness that hinders classification performance, offering a new evaluation approach for QML models.
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