基于自适应量子拓扑的机器学习模型优化

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

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.

DOI: 10.5281/zenodo.22242051

Related papers

基于量子计算的自适应机器学习模型

Jincheng Zhang

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.

Sep 1, 20260 citations

基于量子纠缠的自适应机器学习模型优化

Jincheng Zhang

This paper explores the potential of utilizing quantum entanglement for adaptive machine learning model optimization. Traditional optimization algorithms often struggle with inherent limitations in convergence and stability, particularly when dealing with complex datasets and intricate model architectures. We propose a novel approach that leverages quantum entanglement to dynamically adjust model parameters, thereby enhancing model robustness and generalization ability. The core mechanism centers on establishing entangled states between model parameters and the optimization landscape, enabling instantaneous and coordinated adjustments. We analyze the advantages and challenges of this quantum-enhanced optimization paradigm, demonstrating its potential for significantly improving model performance across various machine learning tasks. This work contributes to the development of more stable and efficient optimization techniques, paving the way for advancements in deep learning and other areas of machine learning.

Sep 1, 20260 citations

基于量子拓扑的自适应机器学习

Jincheng Zhang

This paper introduces a novel self-adaptive machine learning approach leveraging the unique properties of quantum topology. Traditional machine learning algorithms struggle to effectively adapt to complex environments, necessitating the exploration of alternative paradigms. We propose a method that transforms machine learning models into quantum operations, facilitated by quantum entanglement, to achieve enhanced model adaptability and generalization capabilities. This research explores the potential of quantum topology as a foundation for a fundamentally different approach to machine learning, addressing limitations inherent in classical methods. The core mechanism focuses on utilizing quantum entanglement to enable dynamic model adjustment and improve the model's robustness to variations in input data. This work provides a foundation for future research into quantum-enhanced machine learning, offering a promising pathway for addressing challenges in data-intensive applications.

Sep 7, 20260 citations

基于量子纠缠的自适应机器学习的优化器

Jincheng Zhang

This paper introduces a novel quantum-enhanced machine learning optimization algorithm based on the principles of quantum entanglement. We propose a system that utilizes entangled qubits to dynamically adjust learning rates and regularization parameters during the training process, resulting in enhanced model performance compared to traditional optimization techniques. The core mechanism involves leveraging the inherent correlations within entangled states to facilitate faster and more effective gradient propagation and parameter updates. This approach offers a potential paradigm shift in machine learning optimization, capitalizing on the unique properties of quantum mechanics to improve model efficiency and generalization. The research explores the theoretical framework and initial implementation of this algorithm, demonstrating its potential for accelerating and improving the training of complex machine learning models.

Sep 3, 20260 citations

自适应拓扑参数调整的量子机器学习

Jincheng Zhang

This paper introduces a novel quantum machine learning framework, termed "Adaptive Topology Parameter Adjustment" (ATPA), that dynamically adjusts quantum model parameters through a self-adaptive topology structure. Traditional machine learning often relies on static models, limiting the ability to effectively leverage quantum systems. ATPA addresses this limitation by employing a mechanism that automatically adjusts parameters, fostering increased model performance and adaptability. We detail the framework's design, the core mechanism, and the resulting benefits through theoretical analysis and illustrative examples. This work demonstrates the potential for significantly enhanced machine learning capabilities through the integration of adaptive topology in quantum systems.

Sep 4, 20260 citations

基于量子拓扑的自适应机器学习算法

Jincheng Zhang

This paper introduces a novel machine learning algorithm based on quantum topology, termed "Quantum Adaptive Machine Learning" (QAML). QAML leverages the unique properties of quantum topology to dynamically adjust machine learning model parameters, resulting in enhanced generalization and robustness. The algorithm constructs a quantum topology model that adapts to the problem's characteristics, optimizing model performance across diverse scenarios. We present a theoretical framework and preliminary experimental results demonstrating the effectiveness of QAML in a range of machine learning tasks, highlighting its potential to overcome limitations of traditional approaches.

Sep 4, 20260 citations