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.
An Independent Implementation of Quantum Machine Learning Algorithms in Qiskit for Genomic Data
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abstract
In this paper, we explore the power of Quantum Machine Learning as we extend, implement and evaluate algorithms like Quantum Support Vector Classifier (QSVC), Pegasos-QSVC, Variational Quantum Circuits (VQC), and Quantum Neural Networks (QNN) in Qiskit with diverse feature mapping techniques for genomic sequence classification.
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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.