Quantum Machine Learning for Genomic Data Analysis: Unlocking Precision Medicine via Hybrid AI Systems

Abstract

Genomic data characterized by extreme dimensionality, complex structure, and subtle signal-to-noise ratios presents formidable computational and statistical challenges for precision medicine. Quantum machine learning (QML) and hybrid quantum–classical AI systems offer novel computational paradigms that may accelerate or improve aspects of genomic analysis: from sequence alignment and assembly to variant calling, haplotype phasing, population genetics, and multi-omics integration. This article develops a comprehensive, scholarly account of QML applied to genomics. We (i) review the theoretical foundations of quantum algorithms and QML models relevant to genomics (quantum kernels/feature maps, variational quantum circuits, quantum annealing); (ii) formalize problem mappings for core genomics tasks and provide explicit encodings (k-mer embeddings, binary/angle encodings, QUBO formulations); (iii) propose hybrid AI system architectures combining classical deep learning with quantum subroutines for discrete and high-dimensional subproblems; (iv) delineate experimental protocols, benchmarking strategies, and evaluation metrics that fairly compare QML to classical baselines; and (v) critically assess practical limitations (noise, scalability, data-encoding overhead), ethical and security implications, and a realistic roadmap for translational research. We ground our discussion with recent empirical findings and systematic reviews that evaluate QML’s promise and limits in biological data domains. While existing quantum hardware is in the NISQ (noisy intermediate-scale quantum) era, hybrid approaches where quantum processors solve discrete combinatorial or kernel-evaluation subproblems inside largely classical pipelines present a pragmatic path toward early utility in genomics. We conclude with concrete recommendations for researchers and practitioners seeking to responsibly explore QML for precision medicine.