Quantum-Inspired and Caching-Enhanced Needleman-Wunsch Algorithm for Scalable Global Sequence Alignment

Genome sequence alignment is a foundational task in bioinformatics, with the Needleman-Wunsch algorithm widely recognized for producing optimal global alignments. However, as genomic data scales rapidly due to next-generation sequencing technologies, the classical Needleman-Wunsch method faces limitations in computational efficiency and scalability, primarily due to its repeated scoring operations across matrix cells. This study retains Needleman-Wunsch as the benchmark algorithm and introduces an enhanced version, Needleman-Wunsch with Quantum-Inspired Scoring (NWQIS), which integrates a caching mechanism to avoid redundant computations and employs quantum-inspired techniques to accelerate scoring. The enhancement motivation is to preserve alignment accuracy while improving performance for large-scale and high-throughput applications. NWQIS was benchmarked against Dynamic Programming Alignment (DPA) and the classical Needleman-Wunsch Alignment (NWA), evaluated on execution time, memory usage, and alignment accuracy across varying sequence lengths and distributed environments. Results demonstrate that NWQIS matches traditional alignment accuracy while delivering substantial improvements in runtime and scalability.