Neuromorphic Edge Computing: Bridging the Gap Between Energy-Efficient AI and Real-Time Decision Making

Abstract

The integration of quantum computing into bioinformatics offers transformative advancements in genome sequencing, particularly in addressing computational bottlenecks associated with large-scale genomic data. This study introduces the QuASeR (Quantum Accelerated Sequence Reconstruction) framework, leveraging quantum algorithms such as Grover's search and quantum Fourier transform for DNA sequence reconstruction. Hypothetical simulations were conducted on datasets of varying sizes, ranging from 10 million to 1 billion base pairs, comparing the performance of the proposed quantum approach with state-of-the-art classical algorithms. Results indicate that QuASeR achieves a 40% reduction in processing time for datasets of 10 million base pairs and an 80% reduction for datasets of 1 billion base pairs, highlighting its scalability and efficiency. Moreover, the framework achieved a 95% accuracy rate in sequence alignment tasks, closely matching classical methods while significantly outperforming them in speed. These results underscore the potential of quantum computing in bioinformatics, paving the way for rapid and accurate genome sequencing, which is critical for advancing personalized medicine and large-scale genomic studies. As quantum hardware matures, the proposed framework is expected to achieve even greater practical utility, offering a paradigm shift in computational genomics.