基于量子自适应的机器学习的噪声注入策略

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

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.

DOI: 10.5281/zenodo.22203457

Related papers

基于量子自适应的机器学习模型融合策略

Jincheng Zhang

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.

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

基于量子信息的自适应学习框架

Jincheng Zhang

This paper presents a novel learning framework leveraging quantum information theory for adaptive learning. Traditional machine learning models often rely on static parameter adjustments, limiting their ability to efficiently adapt to complex data and optimize performance. We propose a quantum-inspired approach that dynamically adjusts model parameters based on quantum state changes during the learning process. This dynamic adjustment significantly enhances learning efficiency and generalization capabilities. The framework employs a novel quantum state evolution mechanism, allowing for a more nuanced and responsive learning process compared to conventional approaches. The core mechanism focuses on harnessing the inherent quantum properties of data to guide the learning algorithm. We demonstrate the effectiveness of this framework through a series of experiments, showcasing improved learning speed and robustness in a variety of machine learning tasks.

Sep 1, 20260 citations

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

Quantum Machine Learning: Adaptive Noise Injection for Enhanced Generalization and Robustness

Jincheng Zhang

Quantum Machine Learning (QML) is a rapidly evolving field that explores the potential of quantum algorithms to revolutionize machine learning tasks. A critical challenge in QML is the need for robust models that can generalize well to unseen data and exhibit resilience to perturbations. This paper introduces a novel QML model, Quantum Machine Learning with Adaptive Noise Injection (QML-ALI), designed to address this challenge by incorporating a dynamic noise injection mechanism. We propose a mechanism that intelligently adjusts the amount of noise injected during training based on the observed training dynamics, ultimately promoting improved generalization and robustness. This approach contrasts with traditional methods that rely on fixed noise levels, offering a more adaptable and potentially superior solution. The core claims and mechanisms are detailed, and the paper concludes with a discussion of the potential implications and future directions of this research.

Sep 6, 20260 citations

基于量子自适应的机器学习的正则化策略

Jincheng Zhang

This paper introduces a novel regularization strategy based on quantum self-adaptation, aiming to automate the selection and tuning of regularization parameters. Traditional regularization methods rely on manual adjustments, often leading to suboptimal model performance. This work leverages the principles of quantum mechanics to dynamically adjust the regularization strength, resulting in improved generalization capabilities. We present a framework where the quantum self-adaptation process optimizes the regularization parameter space directly, eliminating the need for manual parameter tuning. This approach offers a promising alternative to conventional regularization techniques, potentially leading to more robust and accurate models. The theoretical foundations and experimental validation demonstrate the effectiveness of this quantum self-adaptation strategy in various machine learning tasks.

Aug 31, 20260 citations