基于自适应的量子计算的概率特征提取

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel method for probabilistic feature extraction utilizing the unique properties of quantum computation. Traditional machine learning often relies on classical computation, while quantum computing offers potential for significant performance improvements. We propose a new algorithm that leverages quantum entanglement and superposition to efficiently extract probability features from quantum data, aiming to enhance the performance of machine learning models. The core mechanism of this approach centers around a dynamically adjusted quantum circuit, optimized for specific data patterns and minimizing computational overhead. This paper details the algorithm's design, implementation, and evaluation against established techniques. The investigation demonstrates the potential of this method to improve machine learning model accuracy and efficiency.

DOI: 10.5281/zenodo.22266480

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