Quantum Generative Modeling of Single-Cell transcriptomes: Capturing Gene-Gene and Cell-Cell Interactions

[2510.12776] Quantum Generative Modeling of Single-Cell transcriptomes: Capturing Gene-Gene and Cell-Cell Interactions Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantitative Biology > Quantitative Methods arXiv:2510.12776 (q-bio) [Submitted on 14 Oct 2025 ( v1 ), last revised 18 Jun 2026 (this version, v4)] Title: Quantum Generative Modeling of Single-Cell transcriptomes: Capturing Gene-Gene and Cell-Cell Interactions Authors: Selim Romero , Vignesh S. Kumar , Robert S. Chapkin , James J. Cai View a PDF of the paper titled Quantum Generative Modeling of Single-Cell transcriptomes: Capturing Gene-Gene and Cell-Cell Interactions, by Selim Romero and 3 other authors View PDF HTML (experimental) Abstract: Single-cell RNA sequencing (scRNA-seq) data simulation is limited by classical methods relying on linear correlations, failing to capture nonlinear dependencies. No existing simulator jointly models gene-gene regulatory interactions and cell-cell communication. We introduce qSimCells, a quantum computing-based simulator that uses entanglement to model intra- and inter-cellular interactions, generating realistic single-cell transcriptomic data from heterogeneous cell populations. Its quantum kernel uses a parameterized circuit with CNOT gates to encode gene regulatory networks (GRNs) and cell-cell communication topologies. By programming the entanglement architecture, the simulator establishes a known generative ground truth for both regulatory and communication pathways. The resulting synthetic data exhibits dependencies arising from the joint probability structure of the quantum circuit. Notably, standard correlation-based analyses (Pearson and Spearman) fail to recover the programmed causal relationships and instead report spurious associations driven by high baseline gene-expression probabilities. Applying cell-cell communication detection serves as an internal consistency check: CellChat 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. These results demonstrate that the quantum kernel produces high-fidelity benchmark datasets with known ground truth, highlighting the limitations of correlation-based inference and the need for approaches capable of capturing complex structural dependencies underlying gene regulation and cell-cell communication. Subjects: Quantitative Methods (q-bio.QM) ; Emerging Technologies (cs.ET); Biological Physics (physics.bio-ph); Genomics (q-bio.GN) Cite as: arXiv:2510.12776 [q-bio.QM] (or arXiv:2510.12776v4 [q-bio.QM] for this version) https://doi.org/10.48550/arXiv.2510.12776 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1063/5.0317792 Focus to learn more DOI(s) linking to related resources Submission history From: Selim Romero [ view email ] [v1] Tue, 14 Oct 2025 17:51:48 UTC (1,356 KB) [v2] Tue, 11 Nov 2025 17:53:20 UTC (1,361 KB) [v3] Fri, 19 Dec 2025 16:30:37 UTC (1,376 KB) [v4] Thu, 18 Jun 2026 18:02:56 UTC (3,341 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum Generative Modeling of Single-Cell transcriptomes: Capturing Gene-Gene and Cell-Cell Interactions, by Selim Romero and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.QM < prev | next > new | recent | 2025-10 Change to browse by: cs cs.ET physics physics.bio-ph q-bio q-bio.GN References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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