标题： 一种用于建模单细胞转录组的量子生成框架，考虑基因-基因和细胞-细胞相互作用 显示英文标题

标题： A Quantum Generative Framework for Modeling Single-Cell Transcriptomes with Gene-Gene and Cell-Cell Interactions

摘要： 单细胞RNA测序（scRNA-seq）数据模拟受到经典方法的限制，这些方法依赖于线性相关性，无法捕捉内在的非线性依赖关系以及基因-基因和细胞-细胞的同时相互作用。 我们引入了qSimCells，一种新颖的量子-经典混合模拟器，利用量子纠缠来模拟单细胞转录组。 核心创新是一种量子核，它使用带有CNOT门的参数化量子电路，以明确的方向性（因果性）对复杂的非线性基因调控网络（GRN）和细胞间通信拓扑进行编码。 合成数据表现出挑战标准分析的非经典依赖关系。 我们证明了经典相关性方法（皮尔逊和斯皮尔曼）未能重建完整的程序化量子因果路径，而是报告了由高基础基因表达概率驱动的虚假统计特征。 将CellChat2.0应用于模拟的细胞间通信验证了真正的机制联系，只有在量子纠缠激活时才显示出通信概率的显著相对增加（最多75倍）。 这项工作证实了量子核对于创建高保真真实数据的重要性，突显了需要先进的推断技术来捕捉基因调控中固有的复杂非经典依赖关系。 显示英文摘要

摘要： Single-cell RNA sequencing (scRNA-seq) data simulation is limited by classical methods that rely on linear correlations, failing to capture the intrinsic, nonlinear dependencies and the simultaneous gene-gene and cell-cell interactions. We introduce qSimCells, a novel hybrid quantum-classical simulator that leverages quantum entanglement to model single-cell transcriptomes. The core innovation is a quantum kernel that uses a parameterized quantum circuit with CNOT gates to encode complex, nonlinear gene regulatory network (GRN) and cell-cell communication topologies with explicit directionality (causality). The synthetic data exhibits non-classical dependencies that challenge standard analysis. We demonstrated that classical correlation methods (Pearson and Spearman) failed to reconstruct the complete programmed quantum causal paths, instead reporting spurious statistical artifacts driven by high base-gene expression probabilities. Applying CellChat2.0 to the simulated cell-cell communication validated the true mechanistic links by showing a robust, relative increase in communication probability (up to 75-fold) only when the quantum entanglement was active. This work confirms that the quantum kernel is essential for creating high-fidelity ground truth data, highlighting the need for advanced inference techniques to capture the complex, non-classical dependencies inherent in gene regulation.