Convergence of Bioinformatics and Quantum Computing: A Novel Framework for Genome Sequencing Acceleration

Abstract

The exponential growth of genomic data presents significant computational challenges in the field of bioinformatics, particularly in genome sequencing. Traditional methods struggle to meet the increasing demand for speed, accuracy, and efficiency. This paper introduces a novel framework that leverages quantum computing to accelerate genome sequencing processes. The proposed framework integrates quantum algorithms, such as Grover's search and quantum Fourier transform, with classical bioinformatics techniques to optimize key tasks, including sequence alignment, variant detection, and large-scale data analysis. By exploiting the inherent parallelism and computational power of quantum systems, the framework achieves substantial reductions in processing time while maintaining high accuracy. A case study on large-scale genomic datasets demonstrates the framework's ability to outperform state-of-the-art classical approaches, particularly in handling complex and large-scale sequencing tasks. Furthermore, this research highlights the potential of quantum computing in advancing bioinformatics, paving the way for more efficient and scalable solutions to address the computational bottlenecks of modern genomics. The implications of this interdisciplinary approach extend beyond genomics, offering transformative possibilities in data-driven life sciences.