Quantum Machine Learning with Entanglement Resource Optimization

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper investigates the potential of optimizing entanglement resources within quantum machine learning (QML) algorithms. Traditional approaches often treat entanglement as a readily available, cost-free resource. However, this research posits that strategic management and optimization of entanglement are crucial for significantly enhancing the performance and accuracy of QML models. The core concept involves building a quantum machine learning model, analyzing the algorithm's entanglement requirements, and then employing optimization techniques – specifically, quantum annealing-inspired algorithms – to determine the optimal allocation of entanglement. This approach aims to improve computational efficiency and overall accuracy by leveraging entanglement more effectively. We demonstrate, through theoretical analysis and algorithmic design, that a deliberate focus on entanglement resource optimization can represent a transformative step in advancing the field of quantum machine learning. The results suggest that entanglement, when properly managed, is not merely a byproduct of quantum computation but a key strategic element for achieving superior QML performance.

DOI: 10.5281/zenodo.22593113

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