论文标题
双量子井底部的人造随机神经网络
Artificial stochastic neural network on the base of double quantum wells
论文作者
论文摘要
我们考虑基于$ W $电位的量子机械粒子的人造神经网络的模型。这些粒子在我们的模型中起着神经元的作用。为了模拟这种量子力学系统，使用了蒙特 - 卡洛整合方法。提出了一种粒子的自潜能的形式以及两个相互作用势（令人兴奋和抑制）。显示了最简单的逻辑元素（例如和或不）的示例。此外，我们展示了模型框架中最简单的卷积网络的实现。
We consider a model of an artificial neural network based on quantum-mechanical particles in $W$ potential. These particles play the role of neurons in our model. To simulate such a quantum-mechanical system the Monte-Carlo integration method is used. A form of the self-potential of a particle as well as two interaction potentials (exciting and inhibiting) are proposed. Examples of simplest logical elements (such as AND, OR and NOT) are shown. Further we show an implementation of the simplest convolutional network in framework of our model.
