参数化量子电路作为特征映射：多光谱土地覆盖分类中的表示质量和读出效应
Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification
AI总结：
本文研究了用于多光谱卫星图像土地覆盖分类的变分量子分类器，从特征映射角度探讨了量子电路定义的非线性数据嵌入与读出策略的交互作用，发现量子特征映射在核决策框架中可显著提升性能。
摘要：
我们研究了用于多光谱卫星图像土地覆盖分类的变分量子分类器（VQCs），从特征映射角度探讨了量子电路定义的非线性数据嵌入与读出策略的交互作用。使用EuroSAT-MS数据集，在受控实验协议下系统评估了所有类对的性能，比较了经典基线（逻辑回归、SVM、神经网络）与采用线性读出和量子核SVM策略的VQCs。结果表明，虽然具有线性读出的VQCs在RBF-SVM等强大经典基线面前不占优势，但相同训练的量子特征映射在核决策框架中可显著提升性能。进一步的量子比特数扫描揭示了指数希尔伯特空间维度与线性参数缩放之间的不匹配导致的饱和效应。总体而言，我们的发现强调了量子模型的有效性取决于表示与读出之间的相互作用，并且通过结合学习的量子特征映射与经典决策机制可能获得有意义的提升，而非直接替代经典模型。
英文摘要：
We investigate variational quantum classifiers (VQCs) for land-cover classification from multispectral satellite imagery, adopting a feature-map perspective in which the quantum circuit defines a nonlinear data embedding while the readout determines how this representation is exploited. Using the EuroSAT-MS dataset, we perform a systematic one-vs-one evaluation across all class pairs under a controlled experimental protocol, comparing classical baselines (logistic regression, SVMs, neural networks) with VQCs employing both linear readout and quantum-kernel SVM strategies. Our results show that, while VQCs with linear readout do not outperform strong classical baselines such as RBF-SVM, the same trained quantum feature map can significantly improve performance when reused within a kernel-based decision framework. A qubit-count sweep further reveals saturation effects consistent with the mismatch between exponential Hilbert space dimension and linear parameter scaling. Overall, our findings highlight that the effectiveness of quantum models depends critically on the interplay between representation and readout, and that meaningful gains may arise from combining learned quantum feature maps with classical decision mechanisms rather than seeking direct replacement of classical models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
