The classification of high-dimensional genomic data presents fundamental difficulties for classical machine learning techniques because of the intrinsic non-linearity and the enormous amount of biological feature space represented. In this report, we propose a novel Quantum Kernel Mapping Algorithm (QKMA), which utilizes quantum superposition and entanglement to place genomic data into exponentially large Hilbert spaces and discover non-linear separating hyperplanes that are not computable with classical kernel methods. Our approach to QKMA is to create a parameterized quantum circuit architecture for genomic feature encoding by utilizing principles of variational quantum eigensolvers and support vector machine optimization. We have applied QKMA to three canonical classification problems in genomics: RNA sequencing data for the classification of cancer subtypes (n = 2,847 samples), SNP-based prediction of phenotypes (n = 5,102 subjects), and classification of epigenetic methylation patterns (n = 1,634 samples). QKMA achieved classification accuracies of 91.4%, 88.7%, and 93.2%, respectively, exceeding the baseline classical RBF-SVM accuracies by 7.2%, 6.3%, and 8.1%, respectively. Furthermore, our hybrid quantum-classical optimization framework reduces the kernel computation overhead by 34% compared to naive quantum simulation methods. These results suggest that quantum kernel methods provide a principled, scalable pathway to precision genomics and have important implications for early disease detection, pharmacogenomics, and personalized medicine.
The classification of high-dimensional genomic data presents fundamental difficulties for classical machine learning techniques because of the intrinsic non-linearity and the enormous amount of biological feature space represented. In this report, we propose a novel Quantum Kernel Mapping Algorithm (QKMA), which utilizes quantum superposition and entanglement to place genomic data into exponentially large Hilbert spaces and discover non-linear separating hyperplanes that are not computable with classical kernel methods. Our approach to QKMA is to create a parameterized quantum circuit architecture for genomic feature encoding by utilizing principles of variational quantum eigensolvers and support vector machine optimization. We have applied QKMA to three canonical classification problems in genomics: RNA sequencing data for the classification of cancer subtypes (n = 2,847 samples), SNP-based prediction of phenotypes (n = 5,102 subjects), and classification of epigenetic methylation patterns (n = 1,634 samples). QKMA achieved classification accuracies of 91.4%, 88.7%, and 93.2%, respectively, exceeding the baseline classical RBF-SVM accuracies by 7.2%, 6.3%, and 8.1%, respectively. Furthermore, our hybrid quantum-classical optimization framework reduces the kernel computation overhead by 34% compared to naive quantum simulation methods. These results suggest that quantum kernel methods provide a principled, scalable pathway to precision genomics and have important implications for early disease detection, pharmacogenomics, and personalized medicine.
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.
The support vector machine algorithm with a quantum kernel estimator (QSVM-Kernel), as a leading example of a quantum machine learning technique, has undergone significant advancements. Nevertheless, its integration with classical data presents unique challenges. While quantum computers primarily interact with data in quantum states, embedding classical data into quantum states using feature mapping techniques is essential for leveraging quantum algorithms Despite the recognized importance of feature mapping, its specific impact on data classification outcomes remains largely unexplored. This study addresses this gap by comprehensively assessing the effects of various feature mapping methods on classification results, taking medical data analysis as a case study. In this study, the QSVM-Kernel method was applied to classification problems in two different and publicly available medical datasets, namely, the Wisconsin Breast Cancer (original) and The Cancer Genome Atlas (TCGA) Glioma datasets. In the QSVM-Kernel algorithm, quantum kernel matrices obtained from 9 different quantum feature maps were used. Thus, the effects of these quantum feature maps on the classification results of the QSVM-Kernel algorithm were examined in terms of both classifier performance and total execution time. As a result, in the Wisconsin Breast Cancer (original) and TCGA Glioma datasets, when Rx and Ry rotational gates were used, respectively, as feature maps in the QSVM-Kernel algorithm, the best classification performances were achieved both in terms of classification performance and total execution time. The contributions of this study are that (1) it highlights the significant impact of feature mapping techniques on medical data classification outcomes using the QSVM-Kernel algorithm, and (2) it also guides undertaking research for improved QSVM classification performance.
Multi-omics integration has become a cornerstone of personalized medicine, enabling researchers to connect genomic, transcriptomic, proteomic, metabolomic, and epigenomic data to reveal complex biological mechanisms and individualized disease signatures. However, the extreme dimensionality, heterogeneity, and nonlinear interactions across omics layers challenge even the most advanced classical machine learning methods. Quantum Machine Learning (QML) offers a powerful alternative by exploiting quantum superposition, entanglement, and high-dimensional feature embeddings to model multi-omics interactions more efficiently. This study proposes a hybrid quantum–classical framework that combines quantum kernel estimation, variational quantum classifiers, and quantum-enhanced feature fusion networks to generate integrative biological signatures for precision diagnostics. Simulation results using benchmark multi-omics datasets demonstrate improved classification accuracy, enhanced sensitivity to weak biological signals, and reduced computational overhead compared to classical baselines. These findings highlight the potential of QML to accelerate multi-omics insights and pave the way toward truly personalized, predictive healthcare.
Subtyping of lung cancer is crucial for individualized treatment and optimizing outcomes. Computational modeling allows data analysis over large scales, which helps in making precision classification in large datasets or high-dimensional datasets. Conventional machine learning methods perform well, but integration of multi-omic data (DNA, RNA, miRNA) is challenging in cancer subtyping because of high dimensionality and complex inter-omic relationships. To bypass this Quantum machine learning (QML) provides better ability to navigate high-dimensional biological data with many variables using quantum computational concepts that can identify fine patterns or small-scale patterns beyond the capability of classical approaches. Through the integration of quantum computing principles with the machine learning framework, the study highlights the improvement of computer technology. The framework shows how the system is optimized with the help of quantum principles, how it can improve accuracy, parallel processing and reducing latency. In this work, we have combined data on miRNA, RNA expression, and DNA methylation to create a complete workflow for classifying cancer. This method uses a wide variety of feature selection techniques, such as random forest-based selection, chi-square, mutual information(MI), and principal component analysis(PCA).In order to illustrate the notion of quantum advantage, we carefully compare the performance metrics of the Quantum Support Vector Classifier (QSVC) to the classical Support Vector Machine (SVM), using a ZZ feature map quantum kernel as our baseline for analysis. Using five-fold crossvalidation, we have also verified that the quantum has an advantage with consistently lower variance and higher accuracy.
High-dimensional data classification -- where feature dimensionality p exceeds or approaches sample size n -- is ubiquitous in genomics, medical imaging, spectroscopy, and financial risk modelling. Quantum machine learning (QML) offers potential advantages for high-dimensional classification through quantum feature maps that implicitly operate in exponentially large Hilbert spaces, potentially capturing feature correlations inaccessible to polynomial-complexity classical kernel methods. However, identifying when and why QML provides genuine classification advantage over classical methods for high-dimensional data remains an open question, complicated by the QCCB finding (Popescu and Petrov, 2024) that quantum kernel advantages are dataset-specific. This paper proposes the Quantum High-Dimensional Classification (QHDC) framework, investigating QML advantage for five high-dimensional classification problems across three quantum model families: quantum kernel SVM (QKSVM) using parameterised ZZFeatureMap and IQPFeatureMap; quantum neural network classifier (QNNC) using LSAD-structured ansatz (Silva et al., 2024); and quantum-classical transfer learning (QCTL) using a quantum feature extractor with classical fine-tuning head. QHDC is evaluated on five datasets: genomic variant classification (p=8,442, n=284), hyperspectral remote sensing (p=200, n=10,249), drug-protein binding prediction (p=1,024, n=4,284), financial credit risk (p=84, n=28,400), and quantum chemistry energy prediction (p=48, n=2,840). QKSVM achieves the highest QML performance: AUC improvement over classical RBF-SVM of 2.4-8.4pp across five datasets, with largest gains on quantum chemistry (8.4pp) and drug- protein (6.4pp) datasets. QNNC underperforms classical baselines on three of five datasets due to barren plateau limitations at high dimensionality. QCTL achieves 4.4pp improvement on genomic classification.
Quantum machine learning has emerged as a promising frontier, leveraging the principles of quantum computing to enhance classical algorithms. This paper proposes an optimized Quantum Support Vector Classifier (QSVC) for the classification of the MNIST dataset (a benchmark in handwritten digit recognition). We employ a Chebyshev Parameterized Quantum Circuit (PQC) with an increased number of qubits and layers, and utilize an existing projected quantum kernel method to effectively capture the complex patterns in the data. To enhance model robustness, data augmentation techniques are applied, and dimensionality reduction is performed using Principal Component Analysis (PCA). The proposed QSVC demonstrates competitive accuracy on both the training and testing datasets, highlighting the potential of quantum kernel methods in practical machine learning applications. A comparative analysis with classical Support Vector Machines (SVM) demonstrates the feasibility of quantum methods for high-dimensional classification tasks. Additionally, we investigate the impact of varying the number of qubits in the quantum circuit, providing in-depth insights into the scalability and performance trade-offs of quantum kernel methods.