ResQuNNs:Towards Enabling Deep Learning in Quantum Convolution Neural Networks

arXiv:2402.09146v1 Announce Type: new
Abstract: 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 …

abstract adaptability arxiv challenges convolution cs.lg deep learning enabling extraction feature feature extraction framework networks neural networks novel paper performance quant-ph quantum them type