Artificial stochastic neural network on the base of double quantum wells

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.

arXiv comment20 pages, 16 figures. arXiv admin note: substantial text overlap with arXiv:1806.09664

References in corpus (8)

• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Coupled quantum dots as quantum gates
• Dynamical synapses causing self-organized criticality in neural networks
• Colloquium: Criticality and dynamical scaling in living systems
• Compressing Neural Networks using the Variational Information Bottleneck
• An atomic Boltzmann machine capable of on-chip learning
• An orbitally derived single-atom magnetic memory
• Edge of chaos as a guiding principle for modern neural network training