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

TL;DR

This paper implements and evaluates various quantum machine learning algorithms in Qiskit for classifying genomic data, demonstrating their potential and performance in this domain.

Contribution

It provides an independent implementation of multiple quantum machine learning algorithms in Qiskit tailored for genomic data classification.

Findings

Quantum algorithms show promise in genomic data classification.

Implementation details improve reproducibility of quantum ML in genomics.

Preliminary results indicate competitive performance with classical methods.

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.