基于量子计算的自适应机器学习模型

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper introduces a novel framework for machine learning, leveraging the unique properties of quantum computing to enhance both training and generalization. Traditional machine learning algorithms often rely on manual parameter tuning, which can be a time-consuming and inefficient process, especially when dealing with large datasets and complex models. We propose a "Quantum Machine Learning Self-Adaptive" mechanism that dynamically adjusts model parameters based on quantum computation's inherent adaptability, leading to significantly improved efficiency and robustness. The core mechanism centers around a quantum-enhanced optimization process that allows the model to explore a wider parameter space and avoid local optima. We demonstrate the effectiveness of this approach through simulations and preliminary experiments, showcasing its potential to overcome limitations of classical machine learning techniques. This research represents a significant step toward creating more efficient and adaptable machine learning algorithms, particularly in scenarios with substantial data and intricate model architectures.

DOI: 10.5281/zenodo.22219645

Related papers

基于自适应量子拓扑的机器学习模型优化

Jincheng Zhang

This paper introduces a novel machine learning framework, termed "Quantum-Aware Optimization for Machine Learning," leveraging the unique properties of quantum topology to optimize the performance of machine learning models. Traditional machine learning approaches often struggle with overfitting and limited generalization capabilities, particularly when dealing with complex datasets. This framework utilizes quantum computing to enhance model parameter optimization, potentially leading to improved generalization and robustness. We propose a method that dynamically adjusts the model's structure and parameters based on quantum-induced fluctuations, effectively exploring a vast parameter space and mitigating local optima. This approach offers a paradigm shift in machine learning optimization, promising enhanced model performance and adaptability. The paper details the core mechanism, analyzes the potential benefits through theoretical considerations, and presents preliminary results demonstrating the effectiveness of the proposed framework.

Sep 2, 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

基于自适应量子势的机器学习算法

Jincheng Zhang

This paper presents a novel machine learning algorithm based on adaptive quantum potentials. Traditional machine learning often relies on manual parameter tuning, which can be time-consuming and suboptimal. We propose a new approach where the quantum potential itself is dynamically adjusted during the training process, leading to improved model accuracy and enhanced quantum machine learning capabilities. The core mechanism involves a self-tuning quantum potential landscape, allowing the algorithm to automatically optimize the interaction between data and the quantum system. The algorithm's effectiveness is demonstrated through a series of benchmark datasets and analysis of its performance compared to existing methods. This work introduces a fundamentally new paradigm in quantum machine learning, offering a potential pathway to more efficient and accurate quantum computations.

Sep 3, 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

基于自适应的量子算法优化策略

Jincheng Zhang

This paper proposes a novel quantum algorithm optimization strategy that leverages adaptive learning algorithms to dynamically adjust quantum algorithm parameters. Traditional approaches often rely on manual parameter tuning, which can be time-consuming and suboptimal. Our method aims to improve quantum algorithm efficiency and accuracy by automatically optimizing these parameters based on the input data. We present a framework that incorporates a self-learning mechanism to adaptively adjust parameters, offering a significant improvement over existing methods. The core of our approach involves a dynamic parameter adjustment process guided by a learned model that constantly evaluates the algorithm's performance. We demonstrate the effectiveness of this strategy through theoretical analysis and simulations, showcasing its potential to accelerate algorithm execution and enhance results.

Sep 6, 20260 citations