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

Aug 4, 2026

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Quantum machine learning enables genomic sequence classification by mapping classical biological data into quantum states and evaluating multiple learning paradigms in Qiskit. The study extends and implements Quantum Support Vector Classifier (QSVC), PegasosQSVC, Variational Quantum Circuits (VQC), and Quantum Neural Networks (QNN) using ZFeatureMap, ZZFeatureMap, and PauliFeatureMap. It analyzes how algorithmic choices and feature-mapping parameters affect quantum kernel construction, optimization, and classification performance on genomic sequences.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages3

Reading Time8 min

AccessFree to download

Abstract

Introduction

Background and Implementation

Feature maps and quantum state encoding

QSVC implementation and quantum kernel optimization

Pegasos-QSVC and training loop

Variational Quantum Classifier (VQC)

Quantum Neural Network (QNN) operation

An Independent Implementation of Quantum Machine Learning Algorithms in Qiskit for Genomic Data Navneet Singh and Shiva Raj Pokhrel arXiv :2405 .09781v1 [ cs .LG] 16 May 2024 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), PegasosQSVC, Variational Quantum Circuits (VQC), and Quantum Neural Networks (QNN) in Qiskit with diverse feature mapping techniques for genomic sequence classification.1 KEYWORDS Quantum Machine Learning, Quantum Support Vector Classifier (QSVC), Pegasos-QSVC, Variational Quantum Circuits (VQC), Feature Map, Genomic Sequence Classification and Quantum Neural Networks (QNN) 1 INTRODUCTION The growing evolution of the Quantum Machine Learning (QML) models has shown leapfrog in medical imaging analysis, however, there remains a notable gap in genomic sequence classification, prompting our research to evaluate, rethink and extend the baseline performance of Quantum Support Vector Classifier (QSVM), Pegasos-QSVM [2], Variational Quantum Classifier (VQC), and Quantum Neural Network (QNN) [1] over genomic data [5] . In this preliminary research, we employ feature mapping techniques: ZFeatureMap, ZZFeatureMap, and PauliFeatureMap [4], extend, implement and evaluate QML algorithms over Qiskit for genomic data, to analyze the impact of algorithmic and feature mapping parameters for better understanding and application in genomic sequence classification. 2 BACKGROUND AND IMPLEMENTATION Feature maps facilitate QML models by translating classical data (􀁇 􀀸, 􀁾 􀀸) into operational quantum states 􀁫 , enabling algorithms to process and analyze information efficiently. The ZZFeatureMap uses the ZZ gate to entangle pairs of qubits, introducing phase factors based on encoded classical data and structuring interactions between qubit pairs according to the data distribution. In contrast, the ZFeatureMap employs the Z gate to introduce phase shifts to individual qubit states 1Authors are from IoT & SE Lab, School of IT, Deakin University, Australia; [shiva.pokhrel@deakin.edu.au](shiva.pokhrel@deakin.edu.au). SIGCOMM’24, August 4-8, Sydney, Australia 2024. based on classical data, rotating qubit states accordingly. The PauliFeatureMap uses combinations of Pauli gates (X, Y, and Z) to encode classical data. Our developed QML algorithms and Qiskit implementations are open source. In our QSVC (Algorithm 1), we begin by encoding data points 􀁇 􀀸 into quantum states 􀁫 using a tai lored quantum circuit C , nd a feature map with parametrs 􀁜 ,􀀪feature (􀁇®􀀸, 􀁜 ) . A quantum kernel matrix 􀀠 is computed, representing the inner products between ⟨􀁫(􀁇®􀀸), 􀁫 (􀁇®􀀹)⟩, which facilitates the optimization of the SVM. Training entails solving a dual quadratic programming problem (step 4, Algo. 1) using classical to find the optimal separation hyperplane for labels 􀁾 􀀸 in the quantum-enhanced feature space, while predictions involve preparing a 􀁫 for new data points and evaluating the decision function, 􀀵 (􀁇®) incorporating support vectors, kernel evaluations, and a bias term. | Algorithm 1 Quantum Support Vector Classifier (QSVC) | | --- | | 1: Encode data 􀁇 􀀸 into quantum states 􀁫 ← 􀀪feature (􀁇®􀀸, 􀁜 ) .
2: Construct C Compute kernel matrix K.
3: Measure inner product, 􀀠􀀸 􀀹 ← ⟨􀁫(􀁇®􀀸), 􀁫 (􀁇®􀀹)⟩ .
4: Optimize support vectors and coefficients 􀁕 􀀸 and solve QP problem:
􀁕® ← solve max ∑︁􀀸1 􀁕 􀀸 − 12 􀀸∑︁,􀀹􀀽= 1 􀁾 􀀸􀁾 􀀹􀁕 􀀸􀁕􀀹􀀠􀀸 􀀹 !! subject to: 0 ≤ 􀁕 􀀸 ≤ 􀀘 andÍ1 􀁕 􀀸􀁾 􀀸 = 0.
5: Prepare 􀁫 ← 􀀪feature (􀁇®􀀸, 􀁜 ) .
6: return Decision function 􀀵 (􀁇®) for prediction 􀀽
􀀵 (􀁇®) = sign ∑︁ 􀁕 􀀸􀁾 􀀸􀀠(􀁇®, 􀁇®􀀸 ) − 􀀱! | 􀀸 = 1 The Pegasos-QSVC,(Algorithm 2), in our implementation, initializes the qubits and 􀁜 , including learning rate 􀁛 and regularization factor 􀁟, encoding data in 􀁫 for the computation of the quantum kernel 􀀠 . In the training loop 􀀩 , iterative updates occur with subset evaluations, upda

FAQs

Which quantum machine learning algorithms are implemented for genomic sequence classification in Qiskit?

The document implements QSVC, PegasosQSVC, Variational Quantum Circuits/Variational Quantum Classifier (VQC/VQC), and Quantum Neural Networks (QNN).

What feature mapping techniques are used to encode genomic data?

It uses ZFeatureMap, ZZFeatureMap, and PauliFeatureMap to translate classical genomic inputs into quantum states for downstream learning.

How does the QSVC approach build and use a quantum kernel matrix?

Encoded quantum states are used to compute inner products between data-dependent states, forming a quantum kernel matrix that supports SVM optimization and prediction via the decision function.

Aug 4, 2026

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