Algorithmic Quantum Entanglement for Probabilistic Modeling

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum entanglement, a cornerstone of quantum mechanics, holds immense promise for revolutionizing probabilistic modeling. Current methods often struggle with the inherent limitations of representing complex systems with fidelity, particularly in scenarios requiring nuanced probabilistic outcomes. This paper introduces an algorithmic quantum entanglement framework designed to overcome these limitations, offering a self-improving process for constructing probabilistic models with enhanced accuracy. We explore a novel algorithm that dynamically adjusts entanglement parameters based on system state, fostering a continuous refinement of simulation fidelity. This work presents a significant advancement in the application of quantum mechanics to probabilistic modeling, aiming to unlock new possibilities in fields ranging from quantum chemistry to financial risk assessment.

DOI: 10.5281/zenodo.22490150

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