The barren plateau problem in quantum neural networks (QNNs) is a significant challenge that hinders the practical success of QNNs. In this paper, we introduce residual quantum neural networks (ResQNets) as a solution to address this problem. ResQNets are inspired by classical residual neural networks and involve splitting the conventional QNN architecture into multiple quantum nodes, each containing its own parameterized quantum circuit, and introducing residual connections between these nodes. Our study demonstrates the efficacy of ResQNets by comparing their performance with that of conventional QNNs and plain quantum neural networks through multiple training experiments and analyzing the cost function landscapes. Our results show that the incorporation of residual connections results in improved training performance. Therefore, we conclude that ResQNets offer a promising solution to overcome the barren plateau problem in QNNs and provide a potential direction for future research in the field of quantum machine learning.

中文翻译：

---

ResQNets：一种缓解量子神经网络贫瘠高原的残差方法

量子神经网络（QNN）中的贫瘠高原问题是阻碍 QNN 实际成功的重大挑战。在本文中，我们引入残差量子神经网络（ResQNets）作为解决该问题的解决方案。ResQNet 受到经典残差神经网络的启发，涉及将传统 QNN 架构拆分为多个量子节点，每个节点包含自己的参数化量子电路，并在这些节点之间引入残差连接。我们的研究通过多次训练实验并分析成本函数景观，将 ResQNet 的性能与传统 QNN 和普通量子神经网络的性能进行比较，证明了 ResQNet 的有效性。我们的结果表明，残差连接的结合可以提高训练性能。因此，我们得出的结论是，ResQNets 为克服 QNN 中的贫瘠高原问题提供了一个有前途的解决方案，并为量子机器学习领域的未来研究提供了潜在的方向。