  In this work, we present a quantum circuit model for inferring gene
regulatory networks (GRNs). The model is based on the idea of using qubit-qubit
entanglement to simulate interactions between genes. We provide preliminary
results that suggest our quantum GRN modeling method is competitive and
warrants further investigation. Specifically, we present the results derived
from the single-cell transcriptomic data of human cell lines, focusing on genes
in involving innate immunity regulation. We demonstrate that our quantum
circuit model can be used to predict the presence or absence of regulatory
interactions between genes and estimate the strength and direction of the
interactions, setting the stage for further investigations on how quantum
computing finds applications in data-driven life sciences and, more
importantly, to invite exploration of quantum algorithm design that takes
advantage of the single-cell data. The application of quantum computing on
single-cell transcriptomic data likewise contributes to a novel understanding
of GRNs, given that the relationship between fully interconnected genes can be
approached more effectively by quantum modeling than by statistical
correlations.
