English(EN) On the Complementarity of Quantum and Classical Features: Adaptive Hybrid Quantum-Classical Feature Fusion for Breast Cancer Classification

混合量子-经典模型提升乳腺癌分类准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-28 04:00

研究人员开发了一种新颖的混合量子-经典架构用于乳腺癌分类，旨在克服将量子机器学习与经典深度学习相结合的挑战。所提出的框架提取并统一了来自经典模型（如ResNet）和量子电路的互补特征。一种新的温度缩放混合融合（TSHF）策略动态平衡梯度以解决优化瓶颈，在BreastMNIST数据集上达到了87.82%的峰值准确率。 AI

影响这项研究展示了通过结合量子和经典人工智能方法来增强诊断工具的潜在途径。

排序理由这是一篇研究论文，详细介绍了一种用于特定分类任务的新颖混合量子-经典架构。

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arXiv cs.CV TIER_1 English(EN) · Yasmin Rodrigues Sobrinho, Jo\~ao Renato Ribeiro Manesco, Jo\~ao Paulo Papa · 2026-04-28 04:00

On the Complementarity of Quantum and Classical Features: Adaptive Hybrid Quantum-Classical Feature Fusion for Breast Cancer Classification

arXiv:2604.22903v1 Announce Type: new Abstract: The integration of quantum machine learning with classical deep learning offers promising avenues for medical image analysis by mapping data into high-dimensional Hilbert spaces. However, effectively unifying these distinct paradigm…

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On the Complementarity of Quantum and Classical Features: Adaptive Hybrid Quantum-Classical Feature Fusion for Breast Cancer Classification

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