Quantum Entanglement-Based Dynamic Model Adjustment (QEDMA)

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces a novel dynamic model adjustment framework based on quantum entanglement. Traditional model adjustment methods are often static, limiting their potential for optimization. Quantum entanglement offers a unique mechanism for achieving dynamic adjustments, potentially leading to significantly improved model performance. We present a theoretical framework outlining the core principles of QEDMA, detailing its mechanism for leveraging entanglement to dynamically adjust model parameters and predict with enhanced accuracy. The research explores the potential for utilizing entanglement to create a more robust and adaptable model adjustment process, moving beyond the limitations of conventional approaches. This work establishes a foundational basis for future research into leveraging quantum phenomena for enhanced machine learning.

DOI: 10.5281/zenodo.22253527

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