A Multidisciplinary Analysis of Quantum Inspired Neural Architectures for High Efficiency Signal Processing in Bioinformatics Applications
DOI:
https://doi.org/10.64137/XXXXXXXX/IJMSD-V1I1P101Keywords:
Quantum-inspired neural networks, Bioinformatics signal processing, Quantum machine learning, Genomic sequence analysis, Quantum kernel methods, Computational efficiencyAbstract
The quantum-inspired neural architecture has a transformative capability for high-efficiency signal processing in the field of bioinformatics. Incorporating the principles of quantum computing in combination with classic neural networks, hybrid models use quantum superposition, entanglement, and parallelism to tackle difficult problems in biological data. As an example, the Quantum Spiking Neural Network (QSNN) combined with Quantum Long Short-Term Memory (QLSTM) modules can perform dynamic pattern recognition on genomic sequences and protein interaction predictions, which outperforms data efficiency in classical deep learning models. Quantum kernel methods also provide additional improvements in semantic classification tasks where quantum similarity is calculated between the biological data points, as can be seen in the analysis of gene expression with Quantum Kernel Support Vector Machines (QK-SVM). Areas of application include the transcriptomics classification of cancer using quantum k-means clustering, the optimization of gene regulatory networks, and fast drug discovery by means of quantum-enhanced literature mining. Such architectures both lower computational complexity and accelerate convergence rates - quantum-inspired backpropagation mechanisms, as an example, optimize weight updates 30% faster than classical counterparts in molecular dynamics simulations. The convergence of quantum-inspired algorithms and bioinformatics promises to not only solve the scalability problem in multi-omics data but also explore new methods of real-time adaptive learning in noisy biological settings
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