基于量子自适应的机器学习的梯度裁剪策略

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel gradient clipping strategy based on quantum self-adaptation, designed to enhance model stability in machine learning applications. Traditional gradient clipping methods rely on manual parameter tuning, which can be time-consuming and prone to suboptimal results. This work leverages quantum self-adaptation to dynamically adjust the gradient clipping parameters, resulting in more robust and stable training cycles. We present a framework incorporating quantum algorithms to efficiently manage the gradient accumulation and minimize numerical instability. The proposed method demonstrates improved convergence speed and increased model resilience compared to conventional gradient clipping techniques. We provide a detailed analysis of the algorithm's performance and discuss its potential implications for various machine learning tasks.

DOI: 10.5281/zenodo.22203475

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