  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% over the existing approaches.
