Quantum gene regulatory networks

In this work, we present a quantum circuit model for inferring gene regulatory networks (GRNs) from single-cell transcriptomic data. The model employs qubit entanglement to simulate interactions between genes, resulting in competitive performance and promising potential for further exploration. We applied our quantum GRN modeling approach to single-cell transcriptomic data from human lymphoblastoid cells, focusing on a small set of genes involved in innate immunity regulation. Our quantum circuit model successfully predicted the presence and absence of regulatory interactions between genes, while also estimating the strength of these interactions. We argue that the application of quantum computing in biology has the potential to provide a better understanding of single-cell GRNs by more effectively approaching the relationship between fully interconnected genes compared to conventional statistical methods such as correlation and regression. Our results encourage further investigation into the creation of quantum algorithms that utilize single-cell data, paving the way for future research into the intersection of quantum computing and biology.

Automated summary

In this study, a quantum circuit model was proposed to infer gene regulatory networks (GRNs) from single-cell transcriptomic data. The model utilized qubit entanglement to simulate interactions between genes, showing competitive performance and potential for further exploration. The application of the quantum GRN modeling approach to human lymphoblastoid cells successfully predicted regulatory interactions between genes and estimated the strength of these interactions. This work highlights the potential of quantum computing in biology for a better understanding of single-cell GRNs.