AI 中文总结
该研究以5个二分类数据集为对象，测试不同量子特征编码策略对QSVM性能的影响，发现编码需结合数据结构与电路复杂度评估，且复杂电路未必提升性能。
AI 中文摘要
经典数据编码为量子态的方式对量子机器学习中的分类性能和量子电路复杂度均有重要影响。本研究使用5个二分类数据集，探究不同量子特征编码策略对量子支持向量机（QSVM）性能的影响，具体通过RY(θ)和受控-RY(θ)门将特征间的统计关系融入量子电路，并与传统量子特征映射方法对比。结果表明，在编码过程中融入统计关系会影响分类性能，但更复杂、纠缠更密集的电路未必能带来更高性能。此外，采用复合评估指标共同评估预测性能、泛化能力和电路成本。5个数据集的结果显示，量子特征编码策略的选择应考虑数据的底层结构，且需结合量子电路复杂度一同评估预测性能。
英文摘要
The way in which classical data are encoded into quantum states plays a significant role in both classification performance and quantum circuit complexity in Quantum Machine Learning. In this study, the effects of different quantum feature encoding strategies on Quantum Support Vector Machine performance were investigated using five binary classification datasets. In particular, the statistical relationships between features were incorporated into quantum circuits through \(RY(θ)\) and controlled-\(RY(θ)\) gates, and this approach was compared with conventional quantum feature maps. The results demonstrate that incorporating statistical relationships into the encoding process can influence classification performance. However, more complex and densely entangled circuits do not necessarily yield higher performance. In addition, a composite evaluation metric was employed to jointly assess predictive performance, generalization, and circuit cost. The findings across the five datasets indicate that the choice of quantum feature encoding strategy should account for the underlying structure of the data and that predictive performance should be evaluated together with quantum circuit complexity.
Comments62 pages, 61 figures
