Quantum Algorithms for Genome Sequencing and Analysis

The task of aligning DNA sequences has been pivotal in bioinformatics since the Needleman-Wunsch algorithm in 1970 [1]. Recent advances in sequencing technology have dramatically reduced the cost of DNA data acquisition, outpacing Moore's Law for computing costs. The COVID-19 pandemic further intensified data generation from viral and human samples, creating significant computational challenges. This study investigates the potential of near-term quantum computing to address these bottlenecks. We introduce two quantum algorithms tailored for crucial bioinformatics tasks. The first algorithm applies a quantum dynamic programming approach, inspired by Ambainis et al. [2], to pairwise Sequence Alignment, demonstrating minimal speedup. The second algorithm employs a quantum search via random walk, based on Magniez et al. [3], for the Center String problem, achieving a near-quadratic speedup over the best classical algorithm for unbounded alphabets and superior performance for bounded alphabets in key parameter regimes. Our findings suggest that while quantum computing offers promising speedups for certain bioinformatics problems, the extent of its benefits varies significantly across different tasks. Future work will investigate the potential of quantum computing in analysing graph-based representations of DNA data, such as pangenome models.