Recent advances in genomic sequencing technologies have made it possible to examine the behavior of individual cells at unprecedented scale and resolution. These technologies generate massive amounts of complex biological data, especially from emerging single-cell studies that are revolutionizing our understanding of tissue function, disease mechanisms and therapeutic responses. However, current computer-based methods often fall short in analyzing these large datasets accurately and efficiently, limiting the pace of scientific discovery. This project introduces a new approach using quantum computing, a cutting-edge technology that uses the principles of quantum mechanics to solve certain types of problems more efficiently than classical computers. By applying quantum computing to single-cell omics data, this research aims to build faster and more powerful tools for advancing data analysis and studying how cells behave, interact and respond to treatments. The project also includes public sharing of software tools and educational resources to help train the next generation of scientists at the intersection of biology, computer science and quantum technology. This project will develop a suite of novel quantum algorithms specifically designed for analyzing single-cell omics data. These algorithms will address complex computational tasks such as optimal cell clustering, comparative analysis across biological conditions, and modeling of cellular dynamics responses to drug combi