A hybrid quantum-classical neural network with deep residual learning
This work aims to improve the performance and robustness of quantum neural networks for learning unitary transformations in the presence of noise, which is a problem for quantum computing researchers.
This paper proposes a hybrid quantum-classical neural network (Res-HQCNN) incorporating deep residual learning. The model is trained end-to-end and demonstrated to learn unknown unitary transformations and exhibit stronger robustness to noisy quantum data compared to existing state-of-the-art methods.
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 analysis 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.