Dynamic Quantum State Decoding with Probabilistic Graphical Models

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper explores the application of probabilistic graphical models for dynamic quantum state decoding, aiming to enhance the reliability of quantum information transmission. The core concept involves constructing a quantum state decoder based on a probabilistic graphical model, leveraging measurement results to infer the original quantum state. A Bayesian optimization algorithm is then employed to refine the model's parameters, adapting to evolving measurement data and improving decoding accuracy. The proposed approach offers a novel framework for understanding and optimizing the inherently probabilistic nature of quantum state decoding. The key contributions lie in the integration of graphical models with Bayesian optimization, providing a flexible and adaptive solution compared to traditional decoding methods. This work demonstrates the potential for improved quantum communication protocols by dynamically adjusting the decoding process based on observed data.

DOI: 10.5281/zenodo.22445799

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