自适应量子纠缠的量子机器学习加速器

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the potential of utilizing adaptive quantum entanglement to accelerate quantum machine learning (QML) algorithms. The core concept revolves around designing a novel quantum entanglement mechanism that optimizes the interaction between quantum qubits, enabling more efficient data processing and feature extraction during the training phase. We propose a system that dynamically adjusts entanglement parameters based on the specific characteristics of the data being processed, ultimately leading to faster training times and improved parameter search. This research addresses a critical challenge in QML: the inherent computational complexity of quantum algorithms, particularly during the initial stages of training. We detail the design of this adaptive entanglement system, its mathematical foundation, and preliminary experimental results demonstrating its potential to significantly enhance QML performance. The paper concludes with a discussion of future research directions, focusing on scalability and integration with existing QML frameworks.

DOI: 10.5281/zenodo.22205219

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