Quantum Bayesian Inference for Uncertainty Quantification in Machine Learning

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum Bayesian Inference (QBI) offers a promising approach to quantifying uncertainty in machine learning models, particularly in the face of adversarial examples. This paper introduces a novel framework for QBI that leverages quantum mechanics to enhance model robustness. We demonstrate improvements in uncertainty quantification through a rigorous analysis of the impact of quantum noise on model predictions. The core mechanism involves utilizing quantum state tomography to construct a quantum representation of the model's uncertainty, which is then used to generate robust predictions. We also explore the potential of QBI to mitigate adversarial examples through a novel defense mechanism based on quantum entanglement. This work presents a significant advancement in uncertainty quantification techniques, addressing limitations of existing methods and opening new avenues for reliable machine learning.

DOI: 10.5281/zenodo.22241758

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