An Independent Implementation of Quantum Machine Learning Algorithms in Qiskit for Genomic Data

Navneet Singh; Shiva Raj Pokhrel

An Independent Implementation of Quantum Machine Learning Algorithms in Qiskit for Genomic Data

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arxiv 2405.09781 v1 pith:MHJ2HJKF submitted 2024-05-16 cs.LG cs.AI

Navneet Singh , Shiva Raj Pokhrel This is my paper · ORCID

classification cs.LGcs.AI

keywords quantumalgorithmsgenomiclearningmachineqiskitcircuitsclassification

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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
quant-ph 2025-06 conditional novelty 4.0 of 10

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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