Title: Adaptive Quantum Network Model Adjustment

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces a novel adaptive model adjustment framework based on quantum networks, leveraging quantum entanglement and superposition to dynamically adjust model parameters. The proposed method aims to enhance model performance and generalization capabilities through automated parameter optimization. We present a theoretical analysis of the potential benefits and challenges of this approach, outlining the key mechanisms and potential improvements. The core claim centers on the utilization of quantum phenomena to automate model parameter tuning, representing a significant departure from traditional optimization techniques. This research explores the implications of this approach on various machine learning tasks, demonstrating its potential for improved generalization and performance.

DOI: 10.5281/zenodo.22253458

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