Integrating Quantum Computing with Genomic Data Analysis: A Next-Generation Approach for Predicting Disease Susceptibility
Abstract
In this research, we offer an unprecedented opportunity of combination of quantum computing and genomic data analysis with the aim of though improvements in disease susceptibility prediction. Due to the limitations of traditional computational approaches when processing the data volume and complexity in dealing with genomic data, the given model benefits from the use of quantum feature encoding techniques, including angle and amplitude encoding, and Variational Quantum Classifier (VQC) element to present superior classification results. The raw data was normalized into genomic data, then the data was dimensionally reduced with PCA and encoded to a quantum circuit, enabling the VQC to perform more accurately, sensitively and efficiently than classical model of machine learning. The findings showed that the quantum model had better predictive power in addition to the fact that it took shorter time to train through its parallel nature of quantum operations. The multi-stage integration is an important step towards scalable, real-time and personalized healthcare support in the framework of genomic-based diagnostics.
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