Quantum Kernel Mapping Algorithms for Non-Linear Classification in Genomics

Abstract

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.