Quantum-Enhanced Genomic Data Analysis for Evolutionary Biology and Species Adaptation Modeling

Abstract

The exponential growth of genomic data has transformed evolutionary biology, enabling large-scale analysis of genetic variation, phylogenetic relationships, and adaptive mechanisms across species. However, classical computational approaches increasingly struggle with the combinatorial complexity, high dimensionality, and nonlinear interactions inherent in genomic datasets. This study explores how quantum-enhanced data analysis can augment evolutionary biology by enabling more efficient modeling of genomic variation and species adaptation. By leveraging quantum computing principles such as superposition, entanglement, and quantum parallelism, quantum algorithms offer the potential to accelerate sequence alignment, genotype–phenotype mapping, evolutionary optimization, and adaptive landscape exploration. Through qualitative synthesis of research in quantum computing, computational genomics, and evolutionary theory, this paper examines how quantum-enhanced frameworks can improve the detection of adaptive patterns, epistatic interactions, and evolutionary constraints. The analysis highlights potential applications in phylogenomic reconstruction, population genetics, and predictive modeling of species responses to environmental pressures. While acknowledging current technological limitations, the study argues that quantum-enhanced genomic analysis represents a promising pathway toward more scalable, precise, and predictive evolutionary models. Ultimately, this work positions quantum computing as a transformative tool for advancing our understanding of biological evolution and species resilience in rapidly changing ecosystems.