Quantum convolutional neural networks to optimize the design of synthetic immune cells

I will discuss the application of quantum convolutional neural networks (QCNNs) as a novel machine learning model to guide immune cell design. Chimeric antigen receptor (CAR) costimulatory domains govern the phenotypic output of therapeutic T cells. Classic CNN-based model reached 70% accuracy when predicting CAR T-cell phenotype. QCNN occasionally exceeds the CNN performance. Employing larger QCNNs may further enhance performance, resulting in a superior predictive tool for CAR T cell design.