Quantum Leap: Reshaping Genetic Diagnostics Using Quantum Computing

This study investigates the use of quantum computing, particularly Grover's algorithm, to improve genetic diagnostics for DiGeorge syndrome compared to conventional computational techniques. We employed the IBM Qiskit framework to simulate Grover's algorithm for the rapid and precise identification of pathogenic gene sequences. Background: Conventional genetic diagnostic methods are laborious, delaying essential treatment decisions. Quantum computing, capable of swiftly processing large datasets, offers substantial improvements in diagnostic speed and precision. Materials and Methods: We executed Grover's algorithm using Qiskit, evaluating its performance relative to classical algorithms based on diagnostic time and accuracy. We visualized results using R Studio with the ggplot2 and dplyr libraries. Results: The quantum methodology significantly reduced diagnostic duration from 300 seconds to 45 seconds and improved accuracy from 85% to 98%, surpassing traditional techniques. Conclusions: Our findings indicate that quantum computing can transform genetic diagnostics by enabling faster and more accurate identification of genetic disorders, thus promoting earlier and more personalized treatments. Future research should focus on improving the scalability of quantum computers and incorporating effective quantum algorithms into clinical workflows.