Core Claim: Designing a Dynamic Probability Distribution Calculation Framework

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper investigates a novel computational framework designed to automatically adjust probability distributions during the training of machine learning models. The framework leverages a dynamically evolving "quantum entanglement network" to optimize data distribution, thereby enhancing model prediction accuracy. We propose a method for continuously adjusting network connections based on data characteristics, resulting in a more adaptable and optimized learning process. This work addresses the limitations of traditional models reliant on fixed probability distributions, offering enhanced performance through dynamic distribution adjustment. The core mechanism centers around the creation and manipulation of a quantum entanglement network to guide the optimization process.

DOI: 10.5281/zenodo.22489890

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