Quantum Bioinformatics Dynamic Model Construction

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum bioinformatics is rapidly advancing, offering unprecedented capabilities in analyzing biological data. However, traditional approaches often struggle with complex biological systems and require extensive manual parameter tuning. This paper introduces a novel dynamic model construction algorithm in quantum bioinformatics, leveraging the inherent quantum properties of states to dynamically adjust model parameters and significantly enhance prediction accuracy. We propose a framework where quantum states evolve based on input data, allowing for adaptive model refinement. This approach addresses key challenges in biological data analysis, offering a computationally efficient and highly adaptable method for improving prediction capabilities. The core mechanism involves manipulating quantum states to represent and evolve model parameters, leading to improved results. This research demonstrates the potential of quantum computing to revolutionize biological information analysis.

DOI: 10.5281/zenodo.22596153

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