Quantum generative modeling of single-cell transcriptomes: Capturing gene–gene and cell–cell interactions

Single-cell RNA sequencing data simulation is limited by classical methods that primarily rely on linear correlations, failing to capture the nonlinear dependencies. Existing simulators do not jointly model gene–gene regulatory interactions and cell–cell communication. We introduce qSimCells, a quantum computing-based simulator that employs entanglement to model intra- and inter-cellular interactions, generating biologically inspired synthetic single-cell transcriptomic data from heterogeneous cell populations. The core innovation is a quantum kernel that uses a parameterized quantum circuit with CNOT gates to encode complex, nonlinear gene regulatory networks (GRNs) and cell–cell communication topologies, establishing a known generative ground truth for both regulatory and communication pathways. Notably, standard correlation-based analyses (Pearson and Spearman) recover the programmed causal relationships only at lenient thresholds where spurious associations driven by high baseline gene-expression probabilities also proliferate and lose them at stricter thresholds; tree-based methods (GRNBoost2 and GENIE3) show substantially better alignment, with GENIE3 achieving full recovery in the non-interacting control case. Furthermore, applying cell–cell communication detection to the simulated data serves as an internal consistency check, where CellChat, a widely adopted communication detection framework, correctly identifies the true ligand–receptor pairs when inter-state entanglement is active, revealing a robust, up to 98-fold relative increase in inferred communication probability compared to the non-interacting control. These results demonstrate that the quantum kernel is instrumental for producing high-fidelity benchmark datasets with known ground truth, highlighting the limitations of conventional correlation-based inference methods and the need for advanced analytical approaches capable of capturing the complex structural dependencies underlying gene regulation and cell–cell communication.