量子机器学习的基于自适应损失函数设计

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) is a rapidly evolving field of artificial intelligence that leverages the principles of quantum mechanics to enhance machine learning algorithms. This paper presents a novel quantum machine learning algorithm, termed "Adaptive Loss Function Design" (ALFD), focusing on dynamically adjusting loss functions to optimize model performance and achieve more precise predictions. Traditional loss functions often suffer from suboptimal performance and limited adaptability, while QML offers the potential for significantly improved learning efficiency and accuracy through the inherent capabilities of quantum systems. ALFD employs a self-adjusting mechanism that iteratively modifies the loss function based on data fluctuations, creating a continuous optimization process. The core mechanism leverages quantum entanglement and superposition to efficiently explore the loss function space, leading to enhanced model generalization and predictive capabilities. This paper provides a detailed analysis of ALFD, including its mathematical formulation, implementation details, and experimental results demonstrating its superior performance compared to traditional loss functions and existing QML algorithms. The advantages of this approach lie in its adaptability, potentially leading to more robust and accurate models across diverse datasets.

DOI: 10.5281/zenodo.22509423

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