提出多通道卷积量子嵌入方法，提升经典数据的量子分类性能
Multi-channel convolutional neural quantum embedding
- 设计多通道卷积结构优化量子嵌入，突破传统量子电路限制
- 在CIFAR-10和Tiny ImageNet上实现优于标准方法的分类准确率
- 适用于需要高效量子特征提取的机器学习研究者
使用变分量子电路进行分类是量子机器学习的一个有前景方向。将经典数据通过变分量子电路进行量子监督学习（QSL），需将数据嵌入量子希尔伯特空间，并优化电路参数以训练测量过程。在此背景下，量子监督学习的性能直接受量子嵌入方式的影响。本文提出一种经典-量子混合方法，用于优化一般多通道数据的量子嵌入，突破标准量子计算电路模型（即完全正性和保迹映射）的限制。我们在CIFAR-10和Tiny ImageNet数据集上对多种模型进行了基准测试，并提供了指导模型设计与优化的理论分析。
原文摘要 · Abstract (English)
Classification using variational quantum circuits is a promising frontier in quantum machine learning. Quantum supervised learning (QSL) applied to classical data using variational quantum circuits involves embedding the data into a quantum Hilbert space and optimizing the circuit parameters to train the measurement process. In this context, the efficacy of QSL is inherently influenced by the selection of quantum embedding. In this study, we introduce a classical-quantum hybrid approach for optimizing quantum embedding beyond the limitations of the standard circuit model of quantum computation (i.e., completely positive and trace-preserving maps) for general multi-channel data. We benchmark the performance of various models in our framework using the CIFAR-10 and Tiny ImageNet datasets and provide theoretical analyses that guide model design and optimization.
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