The paper introduces the concept of trainable quanvolutional layers in Quantum Convolutional Neural Networks (QuNNs) to enhance their adaptability and feature extraction capabilities. It identifies a key challenge in optimizing multiple quanvolutional layers, where gradients are exclusively accessible only in the last layer, limiting the optimization process.
To address this challenge, the paper proposes Residual Quantum Convolutional Neural Networks (ResQuNNs), which incorporate residual blocks between quanvolutional layers. These residual blocks facilitate comprehensive gradient access, thereby improving the training performance of QuNNs.
The authors conduct extensive experiments to determine the optimal locations for inserting residual blocks within networks comprising two and three quanvolutional layers. The results demonstrate that certain residual configurations, such as (X+O1)+O2 and (O1+O2)+O3, enable the propagation of gradients through all quanvolutional layers, leading to enhanced training performance compared to configurations where gradients are only accessible in the last layer.
The paper also provides a comparative analysis between models with trainable quanvolutional layers and benchmark models with untrainable quanvolutional layers, highlighting the importance of quanvolutional layer trainability and the role of the classical layer in the learning process.
The proposed ResQuNN architecture represents a significant advancement in the field of quantum deep learning, offering new avenues for both theoretical development and practical quantum computing applications.
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by Muhammad Kas... at arxiv.org 05-02-2024
https://arxiv.org/pdf/2402.09146.pdfDeeper Inquiries