  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.
