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

arXiv: https://arxiv.org/abs/2405.09781 · HTML: https://mlsanity.com/paper/2405.09781/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 5 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 2 of 2)

- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Quantum Federated Learning Experiments in the Cloud with Data Encoding](https://mlsanity.com/paper/2405.00909/) (2024)

## Built on by (most cited first, 1 of 1)

- [Component Based Quantum Machine Learning Explainability](https://mlsanity.com/paper/2506.12378/) (2025)

## Abstract (arXiv)

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.