The project comprises a strategic collaboration between Florida A&M University (FAMU) and Florida State University (FSU) involving both research and education, aiming to develop capacity in genome research via quantum machine learning (QML) at both universities. Researchers at both universities plan to address current challenges by developing scalable, interpretable, Artificial Intelligence (AI)-based computational tools to interpret single-cell data for understanding the evolution of cancer via single-cell sequencing. Single-cell sequencing - such as single-cell DNA sequencing (scDNA-seq) and single-cell RNA sequencing (scRNA-seq) - has been used to explain and predict how cancer cells evolve. Yet, current phylogenetic tree-inference tools are not scalable to the thousands of cells sequenced by scDNA-seq. In addition, no existing methods can fully automate the process of identifying normal cells in scRNA-seq data, a key step before inferring the cancer evolutionary tree. Consequently, the three main objectives of this project are the following: (1) using quantum-inspired computing to increase the scalability of building a phylogenetic tree on scDNA-seq data; (2) building fully automated, interpretable, deep-learning tools to distinguish between tumor and normal cells on scRNA-seq data; and (3) increasing the number of students studying explainable AI and QML in genome data. Broader-impact aspects of the work include also dissemination of the project via publications as well