A Quantum Neural Network for Modeling of T Helper Cell Dynamics

Abstract Understanding the dynamics of T helper 1 (Th1) and T helper 2 (Th2) cells is a key challenge in designing immunotherapy and has garnered significant interest over the past decade. Although quantum machine intelligence has emerged as a novel approach to dynamic modeling, it has yet to be applied to this specific biological problem. In this study, we introduce an architecture of Quantum Neural Networks (QNNs) inspired by the biological mechanisms of T-cell differentiation. The findings show that the proposed model effectively captures Th1/Th2 dynamics and cytokine profiles across five case studies. Furthermore, additional insights from the model inference reveal T cell dominance patterns within patient groups and disease stratification.