Empirical comparison of angle and amplitude encoding in VQCs on Wine and Diabetes datasets shows rotational gate selection in the encoding layer changes accuracy by 10-41 percent and treats embedding as a tunable hyperparameter.
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Evaluating Angle and Amplitude Encoding Strategies for Variational Quantum Machine Learning: their impact on model's accuracy
Empirical comparison of angle and amplitude encoding in VQCs on Wine and Diabetes datasets shows rotational gate selection in the encoding layer changes accuracy by 10-41 percent and treats embedding as a tunable hyperparameter.