将点卷积引入量子神经网络，提升特征融合效率与模型部署可行性。
Quantum Pointwise Convolution: A Flexible and Scalable Approach for Neural Network Enhancement
- 用量子电路实现点卷积，通过幅度编码和权重共享优化信息处理
- 在FashionMNIST和CIFAR10上表现媲美经典模型，验证有效性
- 专为NISQ设备设计，适合集成到CNN等主流深度学习架构中
本文提出一种新型量子神经网络架构——量子点卷积（Quantum Pointwise Convolution），将点卷积嵌入量子框架中，高效融合多通道特征并调整输出。通过量子电路将数据映射至高维空间，捕捉更复杂的特征关系。针对当前量子机器学习在噪音中等规模量子（NISQ）时代面临的挑战，我们采用幅度编码实现数据嵌入，以更少的量子比特处理更多信息；同时引入权重共享机制，加速卷积运算，避免对每个输入像素重复训练。实验表明，该方法在FashionMNIST和CIFAR10分类任务中表现与经典模型相当。这些优化不仅提升了量子点卷积层的效率，也增强了其在各类基于CNN或深度学习模型中的可部署性，拓展了应用场景。
原文摘要 · Abstract (English)
In this study, we propose a novel architecture, the Quantum Pointwise Convolution, which incorporates pointwise convolution within a quantum neural network framework. Our approach leverages the strengths of pointwise convolution to efficiently integrate information across feature channels while adjusting channel outputs. By using quantum circuits, we map data to a higher-dimensional space, capturing more complex feature relationships. To address the current limitations of quantum machine learning in the Noisy Intermediate-Scale Quantum (NISQ) era, we implement several design optimizations. These include amplitude encoding for data embedding, allowing more information to be processed with fewer qubits, and a weight-sharing mechanism that accelerates quantum pointwise convolution operations, reducing the need to retrain for each input pixels. In our experiments, we applied the quantum pointwise convolution layer to classification tasks on the FashionMNIST and CIFAR10 datasets, where our model demonstrated competitive performance compared to its classical counterpart. Furthermore, these optimizations not only improve the efficiency of the quantum pointwise convolutional layer but also make it more readily deployable in various CNN-based or deep learning models, broadening its potential applications across different architectures.
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