基于量子叠加的概率机器学习模型

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper investigates the potential of quantum superposition to enhance probabilistic machine learning models. Traditional machine learning algorithms often struggle with the inherent limitations of classical computation, particularly in handling complex data and computationally intensive tasks. We propose a novel probabilistic machine learning model predicated on quantum superposition, aiming to overcome these limitations through the strategic manipulation of model parameters within quantum states. This model leverages the unique properties of superposition to enhance prediction accuracy and robustness, presenting a fundamentally different approach to probabilistic learning. The core mechanism focuses on mapping model parameters into quantum states, utilizing quantum properties to optimize the learning process and improve overall performance. This research explores the practical implications of this approach, highlighting potential benefits and outlining future research directions.

DOI: 10.5281/zenodo.22489268

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