Quantum Machine Learning Algorithms for Genome Disease Diagnosis

Quantum Machine Learning Algorithms for Genome Disease Diagnosis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 30 September 2025 V1 Latest version Share on Quantum Machine Learning Algorithms for Genome Disease Diagnosis Authors : Al Khan 0000-0002-6403-6091 [email protected] and Elnura Usupova Authors Info & Affiliations https://doi.org/10.22541/au.175924949.93262371/v1 366 views 133 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract 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. Supplementary Material File (preprint submission.pdf) Download 809.54 KB Information & Authors Information Version history V1 Version 1 30 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords artificial intelligence, machine learning, deep learning computation time optimization genetic disease diagnosis hybrid quantum-classical framework quantum machine learning (qml) Authors Affiliations Al Khan 0000-0002-6403-6091 [email protected] Kyrgyz-German University View all articles by this author Elnura Usupova AlaToo International University View all articles by this author Metrics & Citations Metrics Article Usage 366 views 133 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Al Khan, Elnura Usupova. Quantum Machine Learning Algorithms for Genome Disease Diagnosis. Authorea . 30 September 2025. DOI: https://doi.org/10.22541/au.175924949.93262371/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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