Quantum Bayesian Inference with Entangled Noise

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper proposes a novel approach to Bayesian inference that exploits the inherent noise present in quantum systems. The core idea is to utilize entangled quantum systems to generate noise patterns exhibiting strong statistical correlations. These correlations, when measured, are then directly incorporated as prior probabilities within a Bayesian inference engine, leading to improved accuracy compared to traditional Bayesian methods relying solely on classical noise. The methodology moves beyond merely utilizing quantum randomness, actively harnessing the correlated information contained within quantum noise to reshape the inference process. We demonstrate, theoretically, the potential for significant improvements in model accuracy by strategically leveraging these correlations. The framework presented offers a promising avenue for enhancing Bayesian inference in complex scenarios, particularly where classical noise sources are limited in their ability to provide correlated information. The mathematical formalism presented outlines the key principles and equations governing this process.

DOI: 10.5281/zenodo.22469730

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