论文标题
具有深层学习深度学习的混合量子古典神经网络
A hybrid quantum-classical neural network with deep residual learning
论文作者
论文摘要
受经典神经网络成功的启发，已经付出了巨大的努力，将经典的有效神经网络发展为量子概念。在本文中，提出了一种具有深层残留学习（RES-HQCNN）的新型混合量子古典神经网络。我们首先分析如何将残留块结构与量子神经网络联系起来，并提供相应的训练算法。同时，将深层残余学习转变为量子概念的优势和缺点。结果，该模型可以以端到端的方式进行训练，类似于古典神经网络中的倒退。 为了探索RES-HQCNN的有效性，我们在经典计算机上进行有或没有噪音的量子数据进行了广泛的实验。实验结果表明，与艺术状态相比，RES-HQCNN的表现更好，可以学习未知的单一转换，对嘈杂数据具有更强的鲁棒性。此外，还讨论了将残留学习与量子神经网络相结合的可能方法。
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.
