Application of Adaptive Quantum Phase Estimation Algorithms in Computational Biology

Abstract

Issues with the precision and speed of bioinformatics analysis have started to surface as biological data collecting has grown in both scope and depth. Adaptive quantum phase estimation techniques are being developed for quantum speedup in genomic sequence analysis and protein structure prediction. In this study, the identical sample data from the structure and genome fields will be used to compare quantum and classical approaches. In addition to dramatically improving the data processing speed of some analysis tasks, the implemented adaptive quantum method has significantly raised the prediction accuracy, surpassing 98% for moderate-scale molecular problems. According to the aforementioned quantitative resource analysis, the quantum approach is less feasible at larger scales because of increased hardware and running expenses, even though it is more appropriate for some scales. According to the aforementioned findings, adaptive quantum algorithms offer a wide range of applications and can successfully handle specific computational biology problems. However, their widespread adoption is hindered by some technical flaws. In addition to demonstrating various uses of adaptive quantum techniques in specific biological analyses, this study provides a standard reference for integrating quantum and classical technology in bioinformatics.