Since the first quantum neural network based on quantum computing was proposed by famous scholar Kak, much attention has been taken focus on designing new quantum neural network models. In this paper, a novel efficient quantum feed-forward neural network based on quantum computing is established, which adopts genetic algorithm to improve the traditional back propagation algorithm in parameters learning process. We clearly show the mathematical process of the new proposed quantum network model and improved algorithm. The experimental results of MATLAB simulations show that the new network model which makes the best use of fast quantum neural computation does a better job in function approximation and prediction of educational short video’s spreading capacity than traditional back propagation neural network, and the improved algorithm is more efficient than common back propagation algorithm in the proposed quantum network model. Our model can be widely used in weather prediction, handwriting recognition, speech recognition, and other aspects.

中文翻译：

---

基于量子计算的新型神经网络

自从著名学者 Kak 提出第一个基于量子计算的量子神经网络以来，设计新的量子神经网络模型就备受关注。本文建立了一种基于量子计算的新型高效量子前馈神经网络，该网络采用遗传算法改进传统的反向传播算法的参数学习过程。我们清楚地展示了新提出的量子网络模型和改进算法的数学过程。MATLAB仿真实验结果表明，充分利用快速量子神经计算的新型网络模型比传统的反向传播神经网络在教育短视频传播能力的函数逼近和预测方面做得更好，在提出的量子网络模型中，改进算法比普通反向传播算法更有效。我们的模型可以广泛应用于天气预报、手写识别、语音识别等方面。