量子CNN通过傅里叶多路复用器实现像素平移等变性

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型量子卷积神经网络（QCNN），该网络能够实现像素移位等变性，这是图像识别任务的关键属性。这一进展解决了现有QCNN中平移等变性通常仅限于量子比特循环排列的不足。所提出的QCNN利用傅里叶变换实现了精确的像素循环移位等变性，有望提高量子数据处理任务的性能。 AI

影响引入了一种新的QCNN架构，可以提高量子计算机上的图像处理能力。

排序理由这是一篇详细介绍新型QCNN架构的研究论文。

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arXiv cs.LG TIER_1 English(EN) · Dmitry Chirkov, Igor Lobanov · 2026-04-28 04:00

通过傅里叶多路复用器实现像素-翻译等变量子卷积神经网络

arXiv:2604.06094v2 Announce Type: replace-cross Abstract: Convolutional neural networks owe much of their success to hard-coding translation equivariance. Quantum convolutional neural networks (QCNNs) have been proposed as near-term quantum analogues, but the relevant notion of t…