Quantum Bayesian Networks for Robust Calibration

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Bayesian Networks (QBNs) offer a promising avenue for enhancing the robustness of machine learning models, particularly against adversarial attacks and noisy data. This paper introduces a novel framework leveraging quantum superposition and entanglement to represent model uncertainty, enabling a more accurate estimation of true parameter values and bolstering resilience. We explore the potential of QBNs to mitigate the vulnerabilities inherent in classical Bayesian networks, demonstrating its efficacy through simulations and preliminary analyses. The core mechanism centers on exploiting quantum phenomena to quantify and manage uncertainty, ultimately leading to improved model performance.

DOI: 10.5281/zenodo.22188416

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