Cloud education chain and educational quality assessment based on hybrid quantum neural network algorithm

Abstract

In order to improve the efficiency and accuracy of education quality evaluation and the reasonable allocation of educational resources, this paper proposes a novel education chain model based on the hybrid quantum neural network algorithm. The hybrid quantum neural network algorithm is a cutting-edge approach that combines the power of quantum computing with artificial neural networks, which are widely used in machine learning and data analysis. By leveraging the cloud model, the hybrid quantum neural network algorithm can be optimized for high performance and scalability, making it an ideal solution for complex data analysis and decision-making tasks in the education sector. Moreover, this paper applies the hybrid quantum neural network algorithm to solve the problem of flexible resource allocation in low-carbon supply chain management. By using the algorithm to analyze and optimize resource allocation, TN Travel Network can reduce carbon emissions and improve sustainability while also improving operational efficiency and reducing costs. Finally, this paper designs experiments to evaluate the model effect combined with mathematical statistics. The results show that the education chain model constructed in this paper has a significant effect on improving the efficiency and accuracy of education quality evaluation and the reasonable allocation of educational resources. By leveraging the power of the hybrid quantum neural network algorithm, TN Travel Network can achieve sustained growth and success in the education sector while also contributing to a more sustainable and low-carbon future.