A Machine Learning Approach for Optimizing Hybrid Quantum Noise Clusters for Gaussian Quantum Channel Capacity

This work contributes to the advancement of quantum communication by visualizing hybrid quantum noise in higher dimensions and optimizing the capacity of the quantum channel by using machine learning (ML). Employing the expectation maximization (EM) algorithm, the quantum channel parameters are iter...

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Bibliographic Details
Published in:arXiv.org 2024-04
Main Authors: Chakraborty, Mouli, Mukherjee, Anshu, Krikidis, Ioannis, Nag, Avishek, Chandra, Subhash
Format: Article
Language:English
Subjects:
Algorithms
Channel capacity
Clusters
Machine learning
Optimization
Parameter identification
Poisson distribution
Probabilistic models
Probability density functions
Statistical analysis
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Summary:This work contributes to the advancement of quantum communication by visualizing hybrid quantum noise in higher dimensions and optimizing the capacity of the quantum channel by using machine learning (ML). Employing the expectation maximization (EM) algorithm, the quantum channel parameters are iteratively adjusted to estimate the channel capacity, facilitating the categorization of quantum noise data in higher dimensions into a finite number of clusters. In contrast to previous investigations that represented the model in lower dimensions, our work describes the quantum noise as a Gaussian Mixture Model (GMM) with mixing weights derived from a Poisson distribution. The objective was to model the quantum noise using a finite mixture of Gaussian components while preserving the mixing coefficients from the Poisson distribution. Approximating the infinite Gaussian mixture with a finite number of components makes it feasible to visualize clusters of quantum noise data without modifying the original probability density function. By implementing the EM algorithm, the research fine-tuned the channel parameters, identified optimal clusters, improved channel capacity estimation, and offered insights into the characteristics of quantum noise within an ML framework.
ISSN:2331-8422