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English(EN) Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification

量子模型通过结合学习到的特征图和经典方法来增强遥感分类

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员探索了使用变分量子分类器（VQC）对多光谱卫星图像进行土地覆盖分类。他们的研究（重点关注 EuroSAT-MS 数据集）发现，具有线性读出的 VQC 在性能上并未超越 RBF-SVM 等经典方法。然而，当将量子训练的特征图集成到经典基于核的决策框架中时，性能得到了显著提升。研究结果表明，将学习到的量子特征图与经典决策机制相结合，比直接替换经典模型能带来更切实的优势。 AI

影响 表明混合量子-经典方法可能在特定分类任务上提供优于纯量子模型的近期优势。

排序理由 详细介绍量子分类器研究结果的学术论文。

在 arXiv cs.LG 阅读 →

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量子模型通过结合学习到的特征图和经典方法来增强遥感分类

报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Ralntion Komini, Aikaterini Mandilara, Georgios Maragkopoulos, Dimitris Syvridis ·

    Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification

    arXiv:2604.26675v1 Announce Type: cross Abstract: 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…

  2. arXiv cs.LG TIER_1 English(EN) · Dimitris Syvridis ·

    Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification

    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 exp…

  3. arXiv cs.CV TIER_1 English(EN) · Md Aminur Hossain, Ayush V. Patel, Biplab Banerjee ·

    QMC-Net: Data-Aware Quantum Representations for Remote Sensing Image Classification

    arXiv:2604.11817v2 Announce Type: replace-cross Abstract: Hybrid quantum-classical models offer a promising route for learning from complex data; however, their application to multi-band remote sensing imagery often relies on generic, data-agnostic quantum circuits that fail to a…

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