基于量子自适应的机器学习的生成模型优化

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel generative model optimization strategy leveraging the principles of quantum self-adaptation. Traditional optimization methods often require manual tuning of model parameters, which can be time-consuming and suboptimal. We propose a quantum self-adaptation approach that dynamically adjusts the parameters of a generative model through quantum computations, leading to improved generation quality and efficiency. The core mechanism centers around quantum state manipulation to automatically optimize the model's output distribution. This approach offers a significant advantage over existing methods by automating the parameter adjustment process and potentially unlocking enhanced generative capabilities. We present a theoretical framework and preliminary experimental results demonstrating the effectiveness of this quantum-enhanced optimization strategy.

DOI: 10.5281/zenodo.22203374

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