发表机构
Hassan II University of Casablanca（卡萨布兰卡哈桑二世大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用WDBC数据集，通过三种不同纠缠结构的变分量子分类器，发现更高纠缠度与乳腺癌诊断性能提升相关，但仅为关联而非因果。
AI 中文摘要
本研究探讨了变分量子分类器（VQC）的纠缠结构与其在乳腺癌诊断中的性能之间的关系，使用了威斯康星诊断乳腺癌（WDBC）数据集。我们测试了三种共享相同EfficientSU2拟设、COBYLA优化器和分层五折交叉验证的三量子位配置，但它们的纠缠结构不同：单次重复的Ising型耦合（A）、两次重复的线性Ising型耦合（B）以及全连接的Heisenberg型耦合（C）。纠缠通过冯·诺依曼熵和Wootters并发度进行测量。从A到C，平均熵从0.40升至0.71，准确率从92.98%升至93.68%，F1分数从90.32%升至91.28%。配置C在交叉验证各折中也最为稳定。然而，各折中纠缠与性能之间的相关性较弱且不显著。更丰富的拓扑结构也增加了电路表达能力，因此这两种效应无法完全分离。这些结果表明纠缠结构与VQC性能之间存在关联，而非已证明的因果关系。
英文摘要
This study looks at how the entangling structure of a variational quantum classifier (VQC) relates to its performance in breast cancer diagnosis, using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. We tested three three-qubit configurations that share the same EfficientSU2 ansatz, COBYLA optimizer, and stratified five-fold cross-validation, but differ in their entangling structure: a single- repetition Ising-type coupling (A), a two-repetition linear Ising-type coupling (B), and a fully connected Heisenberg-type coupling (C). Entanglement was measured with the von Neumann entropy and the Wootters concurrence. From A to C, the mean entropy rose from 0.40 to 0.71, accuracy rose from 92.98% to 93.68%, and F1-score rose from 90.32% to 91.28%. Configuration C was also the most stable across folds. However, the fold-level correlations between entanglement and performance were weak and not significant. Richer topologies also increase circuit expressivity, so the two effects cannot be fully separated. These results show an association, not a proven causal link, between entangling structure and VQC performance.
