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International Conference on Networks and Cryptology (NETCRYPT)

Application of Quantum Machine Learning in Genomic Data Analysis Using Quantum Support Vector Machines (QSVM)

Anoop Kumar, Wakeel Ahmed, Muzaffar Matchanov, Sindor Sapaev, Thomas Leonid T, Vaibhav C. Gandhi

Published May 29, 2025• 2 citations
Publisher

Abstract

Quantum computing has reintroduced itself as a breakthrough technology for machine learning, especially in the analysis of massive datasets, especially the genomic ones. The present research focuses on using QML, namely QSVM, to parse sequences, discover features relating to multiple genetic alterations, biomarkers, and gene expression patterns. The data type under consideration, namely genomic data, is high-dimensional and complex, which presents some problems to the computational methods originating from traditional mathematics. QSVM is designed to work with the mathematical foundations of quantum mechanics including superposition and entanglement and for this reason, it solves problems associated with large scale genomic data more efficiently and accurately. The application of QSVM in MEPs confirms its superiority over classical counterparts in classifying gene mutations, clustering of expression profiles or prediction of disease susceptibility. In addition, through feature mapping, QSVM offers better insights into biological information contained on genomic data, as compared to linear correlations. Furthermore, the ability of QML frameworks to expand to fit the increasing amount of data produced by next-generation sequencing technologies is assessed. Leveraging on this research QSVM has been recognised to redefine precision medicine by rapidly diagnosing diseases and providing unique therapeutic options. The results re-echo the potential of QML in transforming CG and promoting future innovations in biomedicine and health.

DOI
10.1109/netcrypt65877.2025.11102778

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Quantum computing has reintroduced itself as a breakthrough technology for machine learning, especially in the analysis of massive datasets, especially the genomic ones. The present research focuses on using QML, namely QSVM, to parse sequences, discover features relating to multiple genetic alterations, biomarkers, and gene expression patterns. The data type under consideration, namely genomic data, is high-dimensional and complex, which presents some problems to the computational methods originating from traditional mathematics. QSVM is designed to work with the mathematical foundations of quantum mechanics including superposition and entanglement and for this reason, it solves problems associated with large scale genomic data more efficiently and accurately. The application of QSVM in MEPs confirms its superiority over classical counterparts in classifying gene mutations, clustering of expression profiles or prediction of disease susceptibility. In addition, through feature mapping, QSVM offers better insights into biological information contained on genomic data, as compared to linear correlations. Furthermore, the ability of QML frameworks to expand to fit the increasing amount of data produced by next-generation sequencing technologies is assessed. Leveraging on this research QSVM has been recognised to redefine precision medicine by rapidly diagnosing diseases and providing unique therapeutic options. The results re-echo the potential of QML in transforming CG and promoting future innovations in biomedicine and health.
The rapid advancements in genomics and the availability of large-scale genetic datasets have revolutionized our understanding of genetic diseases. However, the complexity and high dimensionality of genomic data pose significant computational challenges for classical machine learning (ML) algorithms. Quantum machine learning (QML), an emerging interdisciplinary field that combines quantum computing with ML techniques, offers a promising solution to address these challenges. This paper explores the application of QML algorithms for diagnosing genetic diseases by leveraging the unique properties of quantum computing, such as superposition, entanglement, and quantum parallelism. A novel hybrid quantum-classical approach is proposed to enhance the accuracy and efficiency of disease diagnosis using genomic datasets. The methodology involves encoding genetic data into quantum states, applying quantum-enhanced feature selection and classification algorithms, and validating the results on publicly available datasets, such as the UK Biobank and the Cancer Genome Atlas (TCGA). Experimental results demonstrate that the proposed QML framework achieves higher classification accuracy and faster computation times compared to classical counterparts. Equations, tables, and charts are used to illustrate the effectiveness of the approach. This research highlights the transformative potential of QML in precision medicine and lays the groundwork for future investigations into quantum-enabled healthcare solutions.
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Quantum Machine Learning can be considered one of the transformational technologies in precision medicine and drug discovery because quantum computing combines immense processing power with high capability, predictive machine learning. This chapter will discuss the potential for QML to revolutionize complex biological data analysis to rapidly identify disease biomarkers and highly personalized treatment approaches. The chapter describes the two superior performances that can be achieved by quantum algorithms in simulating molecular interactions, and thereby drastically reducing time and cost in drug development. It also discusses key applications that include quantum-enhanced neural networks and support vector machines to diagnose diseases and predict outcomes of treatments. Challenges regarding scalability, noise reduction, and hardware limitations are discussed together with some very promising future directions. When quantum technology has reached full maturity, the opportunities it will give to machine learning will provide unparalleled breakthroughs in medical research.
In this research, we offer an unprecedented opportunity of combination of quantum computing and genomic data analysis with the aim of though improvements in disease susceptibility prediction. Due to the limitations of traditional computational approaches when processing the data volume and complexity in dealing with genomic data, the given model benefits from the use of quantum feature encoding techniques, including angle and amplitude encoding, and Variational Quantum Classifier (VQC) element to present superior classification results. The raw data was normalized into genomic data, then the data was dimensionally reduced with PCA and encoded to a quantum circuit, enabling the VQC to perform more accurately, sensitively and efficiently than classical model of machine learning. The findings showed that the quantum model had better predictive power in addition to the fact that it took shorter time to train through its parallel nature of quantum operations. The multi-stage integration is an important step towards scalable, real-time and personalized healthcare support in the framework of genomic-based diagnostics.