An Independent Implementation of Quantum Machine Learning Algorithms in Qiskit for Genomic Data
arXiv:2405.09781v15 citationsh-index: 26
Originality Synthesis-oriented
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This work provides an incremental application of existing quantum algorithms to genomic data, potentially aiding researchers in bioinformatics.
The authors implemented and evaluated several quantum machine learning algorithms, including QSVC and VQC, in Qiskit for genomic sequence classification, achieving competitive performance with classical methods in some cases.
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.