arXiv subjects

James J. Cai

Publications and source records attributed to James J. Cai.

4 recordsLinked to original sources

arXiv2026

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

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.

q-bio.QM↗

arXiv2026

QuantumXCT: Learning Interaction-Induced State Transformation in Cell-Cell Communication via Quantum Entanglement and Generative Modeling

Inferring cell-cell communication (CCC) from single-cell transcriptomics remains fundamentally limited by reliance on curated ligand-receptor databases, which primarily capture co-expression rather than the system-level effects of signaling on cellular states. Here, we introduce QuantumXCT, a hybrid quantum-classical generative framework that reframes CCC as a problem of learning interaction-induced state transformations between cellular state distributions. By encoding transcriptomic profiles into a high-dimensional Hilbert space, QuantumXCT trains parameterized quantum circuits to learn a unitary transformation that maps a baseline non-interacting cellular state to an interacting state. This approach enables the discovery of communication-driven changes in cellular state distributions without requiring prior biological assumptions. We validate QuantumXCT using both synthetic data with known ground-truth interactions and single-cell RNA-seq data from ovarian cancer-fibroblast co-culture model. The QuantumXCT model accurately recovered complex regulatory dependencies, including feedback structures, and identified dominant communication hubs such as the PDGFB-PDGFRB-STAT3 axis. Importantly, the learned quantum circuit is interpretable: its entangling topology was translated into biologically meaningful interaction networks, while post hoc contribution analysis quantified the relative influence of individual interactions on the observed state transitions. Notably, by shifting CCC inference from static interaction lookup to learning data-driven state transformations, QuantumXCT provides a generative framework for modeling intercellular communication. This work establishes a new paradigm for de novo discovery of communication programs in complex biological systems and highlights the potential of quantum machine learning in the context of single-cell biology.

cs.ET↗

arXiv2025

Quantum Annealing for Enhanced Feature Selection in Single-Cell RNA Sequencing Data Analysis

Feature selection is a machine learning technique for identifying relevant variables in classification and regression models. In single-cell RNA sequencing (scRNA-seq) data analysis, feature selection is used to identify relevant genes that are crucial for understanding cellular processes. Traditional feature selection methods often struggle with the complexity of scRNA-seq data and suffer from interpretation difficulties. Quantum annealing presents a promising alternative approach. In this study, we implement quantum annealing-empowered quadratic unconstrained binary optimization (QUBO) for feature selection in scRNA-seq data. Using data from a human cell differentiation system and an anticancer drug resistance study, we demonstrate that QUBO feature selection effectively identifies genes whose expression patterns reflect critical cell state transitions associated with differentiation and drug resistance development. Our findings indicate that quantum annealing-powered QUBO reveals complex gene expression patterns potentially missed by traditional methods, thereby enhancing scRNA-seq data analysis and interpretation.

q-bio.GN↗

arXiv2022

Quantum gene regulatory networks

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.

cs.ET↗