Component-level SHAP and ALE analysis via state-fidelity pseudo-models reveals differing feature importance across feature maps, ansatze, quantum kernels, and decision functions in a QML classifier.
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Component Based Quantum Machine Learning Explainability
Component-level SHAP and ALE analysis via state-fidelity pseudo-models reveals differing feature importance across feature maps, ansatze, quantum kernels, and decision functions in a QML classifier.