Communication via Quantum Neural Network

TL;DR

This paper explores quantum neural networks for information transfer and virus spread modeling, demonstrating how network strength influences fidelity and infection rates, with potential control strategies for epidemic management.

Contribution

It introduces a quantum neural network-based approach to model information transfer and epidemic spread, highlighting the impact of network parameters on accuracy and infection control.

Findings

Increased network strength improves information transfer fidelity.

Network parameters significantly affect virus transmission rates.

Controlling network parameters can mitigate infection spread.

Abstract

In this study, the partially entangled neural networks is used to transfer information between two neurons, where the original teleportation protocol is employed this for this purpose. The effect of the network strength on the fidelity of the transported information is investigated. We show that as the strength of the network increases, the accuracy of the transformed information increases. As a practical application, we consider the spread of swine flu virus between two equivalent tranches of the community. In this treatment two factors are considered, one for humanity and the other for influence factor. The likelihood of infection between different age group is investigated, where we show that the strength of the neural network and the degree of infection plays an important role on transferring infection between different age group. From theoretical point of view, we show that it is possible to control the spread of the virus by controlling the network parameter. Also, by using local rotation, one can decrease the rate of infection between the young.