Clinical Trial Optimization via Quantum Computing for Drug Development

Abstract

Clinical trials represent one of the most resource-intensive and time-consuming phases of drug development, often constrained by complex patient stratification, high attrition rates, and logistical inefficiencies. Recent advances in quantum computing introduce new computational paradigms capable of addressing combinatorial optimization, probabilistic modeling, and high-dimensional data analysis challenges inherent in clinical research. This paper examines clinical trial optimization via quantum computing, exploring how quantum algorithms and hybrid quantum–classical frameworks can enhance patient recruitment, adaptive trial design, biomarker discovery, and outcome prediction. By synthesizing developments in quantum optimization, machine learning, and biomedical data analytics, the study evaluates the feasibility and potential impact of quantum-enabled methodologies in accelerating drug development pipelines. The findings suggest that quantum computing, when strategically integrated with classical infrastructures, may reduce development timelines, improve decision accuracy, and support more personalized and efficient clinical trial processes.

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