A Grover's Algorithm-Driven Quantum Intelligence Framework for Sustainable DNA Clinical Trial Optimization and Precision Medicine

Abstract

While the field of genomic sequencing and DNA-based precision medicine has made tremendous strides, DNA clinical trials are still plagued with numerous problems, such as low recruitment for trials, disparate genomic data integration, difficult biomarker identification, long trial times, and inconsistent treatment decisions. Restrictions in this limit trial efficiency, cost of operation and limit the progress of sustainable precision medicine. To tackle these challenges, the authors in this paper suggest a framework for optimizing DNA clinical trial and pediatric precision medicine with quantum intelligence, based on Grover's Algorithm. The proposed framework combines all the genomic sequencing information, clinical records, biomarker profiles and therapeutic knowledge in a single quantum-assisted intelligence framework. Grover's Algorithm can be used to optimize patient recruitment and biomarker discovery in highdimensional genomic search spaces; adaptive machine learning models can be used to conduct early genetic risk assessment and for personalized therapeutic recommendations. Moreover, a sustainability conscious optimisation process not only reduces the complexity of computations, but also the use of resources and the time required for clinical trials, and keeps the predictive reliability high. The proposed framework is evaluated experimentally, which shows the accuracy of participant matching is 99.12%, the precision is 98.84%, the recall is 98.67%, the F1-score is 98.75%, and the time saved in trial optimization is 36.8% compared to the other optimization and artificial intelligence frameworks. These findings demonstrate the efficacy, scalability, and sustainability of the framework, offering valuable genomic decision support, optimizing clinical trial workflow, and paving the way for precision medicine in pediatrics.