Abstract
Inspired by the success of classical neural networks, there has been tremendous effort to develop classical effective neural networks into quantum concept. In this paper, a novel hybrid quantum–classical neural network with deep residual learning (Res-HQCNN) is proposed. We firstly analyse how to connect residual block structure with a quantum neural network, and give the corresponding training algorithm. At the same time, the advantages and disadvantages of transforming deep residual learning into quantum concept are provided. As a result, the model can be trained in an end-to-end fashion, analogue to the backpropagation in classical neural networks. To explore the effectiveness of Res-HQCNN , we perform extensive experiments for quantum data with or without noisy on classical computer. The experimental results show the Res-HQCNN performs better to learn an unknown unitary transformation and has stronger robustness for noisy data, when compared to state of the arts. Moreover, the possible methods of combining residual learning with quantum neural networks are also discussed.
Highlights
Artificial neural networks (ANNs) stand for one of the most prosperous computational paradigms (Nielsen, 2015)
Compared with the training algorithm on classical computer of quantum neural networks (QNNs) in Beer et al (2020), we find that Kjl(s) in QNNs is no more than the one in Res-HQCNN
We have developed a hybrid quantum–classical neural network with deep residual learning to improve the performance of cost function for deeper networks
Summary
Artificial neural networks (ANNs) stand for one of the most prosperous computational paradigms (Nielsen, 2015). Neural networks achieve significant success and have wide applications in various machine learning fields like pattern recognition, video analysis, medical diagnosis, and robot control (Amato et al, 2013; Bishop et al, 1995; Mitchell & Thrun, 1993; Nishani & Çiço, 2017). In 2016, deep residual networks (ResNets) are proposed with extremely deep architectures showing excellent accuracy and nice convergence behaviors (He et al, 2016a, 2016b). Their result won the 1st place on the ImageNet Large Scale Visual Recognition Challenge 2015 classification task for ImageNet classification and ResNet (and its variants) achieves revolutionary success in many research and industry applications
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