基于量子自适应的机器学习模型融合策略

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores a novel approach to machine learning model fusion using a quantum-inspired adaptation strategy. Traditional methods often rely on manual design, limiting the flexibility and efficiency of the fusion process. We propose a quantum-driven adaptation mechanism that automatically adjusts the fusion strategy, leading to improved model performance and generalization capabilities. The core of our approach involves a quantum-enhanced optimization loop that dynamically adjusts the weighting of different model components during the fusion process. This dynamic adjustment allows for a more robust and adaptable fusion strategy compared to conventional methods. We present experimental results demonstrating the effectiveness of this quantum-inspired adaptation approach across several benchmark datasets. The results show a significant improvement in accuracy and robustness compared to existing fusion techniques.

DOI: 10.5281/zenodo.22203417

Related papers

基于量子自适应的机器学习的损失函数调整策略

Jincheng Zhang

This paper introduces a novel approach to machine learning model optimization based on quantum self-adaptable loss function adjustment. Traditional methods rely on manual adjustment of loss functions, which can be time-consuming and suboptimal. This paper proposes a quantum-enhanced approach that dynamically adjusts the loss function's weighting based on the model's internal state, leading to improved model performance and robustness. We detail the core mechanism of this strategy, outlining its quantum computational advantages and practical implications. The study demonstrates the effectiveness of this method through simulations and a preliminary analysis of a benchmark dataset. The core claim is that this quantum self-adaptable loss function adjustment strategy offers a significant improvement over existing techniques by automating the optimization process and enhancing model generalization.

Aug 31, 20260 citations

异质性信息驱动的量子机器学习模型

Jincheng Zhang

This paper introduces a novel quantum machine learning model, termed "Heterogeneity-Driven Quantum Machine Learning," that leverages the principle of data distribution heterogeneity to dynamically adjust model parameters and enhance generalization. Traditional machine learning relies heavily on manually tuned parameters, limiting its ability to effectively exploit complex data distributions. This work proposes a quantum-enhanced approach that utilizes the inherent variability within datasets to automatically optimize model parameters, resulting in improved performance across diverse datasets. The core mechanism involves quantum-assisted parameter adjustment guided by data heterogeneity, leading to a more robust and adaptable learning process. The model's effectiveness is demonstrated through a series of benchmark experiments, showcasing enhanced generalization capabilities compared to traditional quantum machine learning methods.

Aug 31, 20260 citations

基于量子自适应的机器学习的噪声注入策略

Jincheng Zhang

This paper presents a novel approach to machine learning model robustness utilizing a quantum-inspired adaptive noise injection strategy. Traditional noise injection methods often involve manual adjustments, leading to suboptimal results and limited adaptability. We introduce a quantum-driven mechanism that automatically adjusts the noise injection process, enabling the model to dynamically respond to perturbations and improve its resilience to adversarial attacks and data variations. The core of our approach lies in leveraging quantum entanglement to introduce subtle, controlled noise, fostering a more robust and adaptable learning process. The effectiveness of this method is demonstrated through extensive experimental analysis, showcasing significant improvements in model accuracy and generalization performance compared to conventional techniques.

Aug 31, 20260 citations

基于量子自适应的机器学习算法

Jincheng Zhang

This paper presents a novel machine learning algorithm leveraging quantum self-adaptation to enhance model performance. Traditional machine learning relies heavily on manually defined model parameters, limiting potential for optimization. We introduce a quantum self-adaptation mechanism that dynamically adjusts model parameters during training, enabling automated parameter optimization and significantly improved predictive accuracy. This approach addresses limitations of existing methods by providing a computationally efficient and inherently adaptive learning strategy. The core mechanism utilizes quantum properties to optimize model parameters, resulting in enhanced predictive capabilities. The results demonstrate a substantial improvement in prediction accuracy compared to state-of-the-art machine learning models.

Sep 4, 20260 citations

基于量子自旋的机器学习模型训练算法

Jincheng Zhang

This paper introduces a novel machine learning model training algorithm based on quantum spin dynamics, aiming to enhance both training efficiency and generalization ability. Traditional machine learning methods often suffer from static training procedures, limiting their adaptability. We propose a "Quantum Spin Training" algorithm that leverages the inherent adaptability of quantum spin states to dynamically adjust the learning process. This approach allows for more efficient and robust model training, potentially addressing limitations inherent in existing algorithms. The core mechanism involves implementing a quantum state evolution process, where the spin dynamics are controlled to guide the optimization process. The resulting algorithm demonstrates improved performance across a range of benchmark datasets, suggesting a significant advancement in machine learning training techniques. This research explores the potential of quantum-enhanced machine learning for enhanced model performance.

Sep 6, 20260 citations

基于量子自适应的机器学习 - 量子特征的动态优化

Jincheng Zhang

This paper introduces a novel machine learning approach, termed "Quantum Adaptive Machine Learning" (QAML), that leverages quantum feature adaptation to enhance model generalization and efficiency. Traditional machine learning often relies on static, pre-defined features, limiting the model's ability to adapt to complex data patterns. QAML utilizes quantum feature adaptation to dynamically optimize the model's feature space, allowing for more flexible and robust representations. The paper details the fundamental principles, experimental setup, and results demonstrating the superior performance of QAML compared to conventional methods across a range of datasets. The core mechanism involves the application of quantum algorithms to iteratively refine feature representations, leading to a significant improvement in model accuracy and computational cost.

Sep 3, 20260 citations