Artificial stochastic neural network on the base of double quantum wells

TL;DR

This paper introduces a quantum-inspired neural network model using particles in double quantum wells, employing Monte Carlo methods to simulate neuron interactions and demonstrating basic logical and convolutional operations.

Contribution

It presents a novel quantum-mechanical particle-based neural network model with specific potentials and demonstrates logical and convolutional functionalities.

Findings

Successfully modeled logical gates (AND, OR, NOT)

Implemented a basic convolutional network within the quantum framework

Used Monte Carlo integration to simulate quantum neural interactions

Abstract

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.