标题： 量子机器学习中构造性纠缠拓扑的随机纠缠配置及其在心脏MRI中的应用 显示英文标题

标题： Stochastic Entanglement Configuration for Constructive Entanglement Topologies in Quantum Machine Learning with Application to Cardiac MRI

摘要： 高效的纠缠策略对于推进用于量子机器学习(QML)的变分量子电路(VQCs)至关重要。然而，目前大多数方法使用固定的纠缠拓扑结构，这些结构无法适应任务需求，限制了相对于经典模型的潜在优势。我们引入了一种新颖的随机纠缠配置方法，系统地生成多样化的纠缠拓扑结构，以确定一个构造性纠缠配置子空间，定义为能够提升混合模型性能（例如分类准确率）的纠缠拓扑结构，超过经典基线。每个配置被编码为一个随机二进制矩阵，表示量子比特之间的定向纠缠。这使得可以使用纠缠密度和每个量子比特约束作为关键指标，对候选纠缠拓扑结构的超空间进行可扩展探索。我们定义了无约束和有约束的采样模式，以控制每个量子比特的纠缠程度。使用我们的方法，在心脏MRI疾病分类的混合QML中生成并评估了400个随机配置。我们识别出64（16%）个新颖的构造性纠缠配置，这些配置始终优于经典基线。顶级配置的集成聚合达到了约0.92的分类准确率，比经典模型（约0.87）高出超过5%。与四种传统拓扑结构（环形、最近邻、无纠缠、完全纠缠）相比，没有一种能超越经典基线（最高准确率约0.82），而我们的配置实现了高达约20%的更高准确率。因此，突显了所识别的构造性纠缠的鲁棒性和泛化能力。 显示英文摘要

摘要： Efficient entanglement strategies are essential for advancing variational quantum circuits (VQCs) for quantum machine learning (QML). However, most current approaches use fixed entanglement topologies that are not adaptive to task requirements, limiting potential gains over classical models. We introduce a novel stochastic entanglement configuration method that systematically generates diverse entanglement topologies to identify a subspace of constructive entanglement configurations, defined as entanglement topologies that boost hybrid model performance (e.g., classification accuracy) beyond classical baselines. Each configuration is encoded as a stochastic binary matrix, denoting directed entanglement between qubits. This enables scalable exploration of the hyperspace of candidate entanglement topologies using entanglement density and per-qubit constraints as key metrics. We define unconstrained and constrained sampling modes, controlling entanglement per qubit. Using our method, 400 stochastic configurations were generated and evaluated in a hybrid QML for cardiac MRI disease classification. We identified 64 (16%) novel constructive entanglement configurations that consistently outperformed the classical baseline. Ensemble aggregation of top-performing configurations achieved ~0.92 classification accuracy, exceeding the classical model (~0.87) by over 5%. Compared to four conventional topologies (ring, nearest neighbor, no entanglement, fully entangled), none surpassed the classical baseline (maximum accuracy ~0.82), while our configurations delivered up to ~20% higher accuracy. Thus, highlighting the robustness and generalizability of the identified constructive entanglements.