In this paper, we present a novel framework for enhancing the performance of
Quanvolutional Neural Networks (QuNNs) by introducing trainable quanvolutional
layers and addressing the critical challenges associated with them. Traditional
quanvolutional layers, although beneficial for feature extraction, have largely
been static, offering limited adaptability. Unlike state-of-the-art, our
research overcomes this limitation by enabling training within these layers,
significantly increasing the flexibility and potential of QuNNs. However, the
introduction of multiple trainable quanvolutional layers induces complexities
in gradient-based optimization, primarily due to the difficulty in accessing
gradients across these layers. To resolve this, we propose a novel
architecture, Residual Quanvolutional Neural Networks (ResQuNNs), leveraging
the concept of residual learning, which facilitates the flow of gradients by
adding skip connections between layers. By inserting residual blocks between
quanvolutional layers, we ensure enhanced gradient access throughout the
network, leading to improved training performance. Moreover, we provide
empirical evidence on the strategic placement of these residual blocks within
QuNNs. Through extensive experimentation, we identify an efficient
configuration of residual blocks, which enables gradients across all the layers
in the network that eventually results in efficient training. Our findings
suggest that the precise location of residual blocks plays a crucial role in
maximizing the performance gains in QuNNs. Our results mark a substantial step
forward in the evolution of quantum deep learning, offering new avenues for
both theoretical development and practical quantum computing applications.

在本文中，我们提出了一种新的框架来增强可训练的量子卷积神经网络（QuNNs）的性能，通过引入可训练的 quanvolutional 层并解决与其相关的关键挑战。我们的研究通过在这些层中实现训练，显著提高了 QuNNs 的灵活性和潜力。我们引入多个可训练的 quanvolutional 层，为基于梯度的优化引入了复杂性，并通过在层之间添加 skip 连接的残差学习概念，提出了一种新的架构 Residual Quanvolutional Neural Networks（ResQuNNs），从而解决了梯度访问的困难。通过在 QuNNs 中插入残差块，我们确保了网络中的增强梯度访问，从而提高了训练性能。通过广泛的实验，我们发现在 QuNNs 中合理放置残差块对于最大化性能提升至关重要。我们的发现对量子深度学习的发展和实际量子计算应用提供了新的途径。