A hybrid quantum-classical neural network with deep residual learning

TL;DR

This paper introduces a novel hybrid quantum-classical neural network with deep residual learning, demonstrating improved performance and robustness in learning quantum data, especially under noisy conditions.

Contribution

It proposes a new Res-HQCNN model that integrates residual blocks with quantum neural networks and provides an end-to-end training algorithm.

Findings

Res-HQCNN outperforms existing methods in learning unknown unitary transformations.

The model shows increased robustness to noisy quantum data.

Extensive experiments validate the effectiveness of the proposed approach.

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 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.