This work presents a novel Evolutionary Quantum Neural Network (EQNN) based workload prediction model for Cloud datacenter. It exploits the computational efficiency of quantum computing by encoding workload information into qubits and propagating this information through the network to estimate the workload or resource demands with enhanced accuracy proactively. The rotation and reverse rotation effects of the Controlled-NOT (C-NOT) gate serve activation function at the hidden and output layers to adjust the qubit weights. In addition, a Self Balanced Adaptive Differential Evolution (SB-ADE) algorithm is developed to optimize qubit network weights. The accuracy of the EQNN prediction model is extensively evaluated and compared with seven state-of-the-art methods using eight real world benchmark datasets of three different categories. Experimental results reveal that the use of the quantum approach to evolutionary neural network substantially improves the prediction accuracy up to 91.6 percent over the existing approaches.

中文翻译：

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一种实现云工作负载自适应预测的量子方法

这项工作提出了一种新颖的基于进化量子神经网络 (EQNN) 的云数据中心工作负载预测模型。它通过将工作负载信息编码为量子位并通过网络传播该信息来利用量子计算的计算效率，从而以更高的准确度主动估计工作负载或资源需求。Controlled-NOT (C-NOT) 门的旋转和反向旋转效应在隐藏层和输出层提供激活功能，以调整量子位权重。此外，还开发了一种自平衡自适应差分进化 (SB-ADE) 算法来优化量子比特网络权重。EQNN 预测模型的准确性得到了广泛的评估，并使用三个不同类别的八个真实世界基准数据集与七种最先进的方法进行了比较。