Collaborative Research: Quantum Algorithms for High-Performance Analysis of Single-Cell Omics Data and Explainable Drug Discovery

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 combinations. The work will formulate these problems as quadratic unconstrained binary optimization models and solve them using quantum annealing approaches on D-Wave machines. In addition, gate-based quantum algorithms will be implemented and tested on IonQ platforms, alongside hybrid classical-quantum approaches. The algorithms will be applied to real single-cell transcriptomic datasets from the mouse brain and targeted studies of drug response in multiple myeloma and ovarian cancer, demonstrating the advantages of quantum-enabled analysis. A central deliverable will be the creation of QOTBox, a scalable quantum computing platform tailored for single-cell data analysis. All algorithms and code will be openly shared, with educational materials including online tutorials and interactive notebooks to support adoption across the scientific community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.