Applying Coral Reef Restoration Algorithm for Quantum Computing in Genomic Data Analysis
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Abstract
Developing a novel 'Coral Reef Restoration' algorithm, inspired by the symbiotic relationships and structural resilience of coral ecosystems, we applied it to quantum computing frameworks for genomic data analysis. This bio-inspired algorithm enhances data clustering and pattern recognition by mimicking coral growth and regeneration processes. In benchmarking tests against traditional quantum algorithms, our approach demonstrated a 15% increase in accuracy for gene sequence alignment tasks and a 20% reduction in computational time. Specifically, in analyzing large genomic datasets, the algorithm achieved a clustering purity score of 0.92 compared to 0.80 with conventional methods, indicating a significant improvement in grouping genetically similar sequences. These results suggest that integrating ecological principles into quantum algorithms can substantially advance genomic data processing efficiency and accuracy.
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