Hybrid Quantum Noise Approximation and Pattern Analysis on Parameterized Component Distributions

TL;DR

This paper introduces a hybrid quantum noise model combining quantum Poisson noise and classical Gaussian noise, providing a finite mixture approximation to analyze noise entropy and pattern characteristics in quantum channels.

Contribution

It develops a finite mixture approximation for hybrid quantum noise modeled as an infinite Gaussian-Poisson mixture, enabling better entropy calculation and pattern analysis.

Findings

Finite mixture approximation effectively models hybrid quantum noise.

Mathematical analysis links Poisson parameters to noise characteristics.

Enhanced understanding of hybrid noise entropy in quantum channels.

Abstract

Noise is a vital factor in determining the accuracy of processing the information of the quantum channel. One must consider classical noise effects associated with quantum noise sources for more realistic modelling of quantum channels. A hybrid quantum noise model incorporating both quantum Poisson noise and classical additive white Gaussian noise (AWGN) can be interpreted as an infinite mixture of Gaussians with weightage from the Poisson distribution. The entropy measure of this function is difficult to calculate. This research developed how the infinite mixture can be well approximated by a finite mixture distribution depending on the Poisson parametric setting compared to the number of mixture components. The mathematical analysis of the characterization of hybrid quantum noise has been demonstrated based on Gaussian and Poisson parametric analysis. This helps in the pattern analysis of the parametric values of the component distribution, and it also helps in the calculation of hybrid noise entropy to understand hybrid quantum noise better.