Quantum gene regulatory networks

TL;DR

This paper introduces a quantum circuit model for inferring gene regulatory networks using qubit entanglement, demonstrating potential advantages in modeling complex gene interactions from single-cell data.

Contribution

It presents a novel quantum modeling approach for gene regulatory networks, leveraging entanglement to improve inference from transcriptomic data.

Findings

Quantum model predicts gene interactions effectively

Model estimates interaction strength and direction

Preliminary results show competitiveness with classical methods

Abstract

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.