基于量子纠缠的动态机器学习模型参数调整

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel dynamic machine learning model parameter adjustment technique leveraging the quantum entanglement phenomenon. Traditional parameter optimization methods often rely on static adjustments, while this approach utilizes the inherent adaptability of quantum states to dynamically adjust model parameters. The core mechanism involves orchestrating quantum state manipulations to iteratively refine the model's parameters, resulting in enhanced model performance and reduced training difficulty. The paper details a preliminary implementation and explores the potential of this approach for enhancing existing machine learning models across various applications.

DOI: 10.5281/zenodo.22634536

Related papers

基于量子纠缠的自适应参数调整

Jincheng Zhang

This paper explores the potential of quantum entanglement to revolutionize parameter adjustment in machine learning models. Traditional parameter adjustment methods often rely on iterative refinement, which can be slow and computationally expensive. We propose a novel approach leveraging quantum entanglement to dynamically adjust parameters during the training process, potentially accelerating learning and improving model performance. This work investigates the feasibility of using entangled states to facilitate faster and more efficient search, offering a fundamentally different paradigm for parameter optimization. We outline the theoretical framework, describe the implementation of the quantum entanglement-based mechanism, and present preliminary results demonstrating a significant improvement in learning speed compared to classical methods. The core claim is that this approach, leveraging quantum entanglement, provides a new and potentially transformative mechanism for parameter optimization, offering a pathway towards more sophisticated and efficient machine learning models.

Aug 31, 20260 citations

Quantum State-Adaptive Machine Learning Dynamic Adjustment

Jincheng Zhang

Quantum state-adaptive machine learning is a rapidly evolving field with the potential to revolutionize artificial intelligence. This paper explores the design and implementation of a novel algorithm, Quantum State-Adaptive Machine Learning Dynamic Adjustment, which leverages quantum state dynamics to dynamically adjust model parameters and enhance model performance. The core mechanism centers around exploiting quantum superposition and entanglement to iteratively refine model parameters based on the inherent quantum properties of the training data. This approach offers a fundamentally new paradigm for machine learning, potentially surpassing the limitations of traditional algorithms. We present a theoretical framework and preliminary experimental results demonstrating the algorithm's effectiveness in optimizing a specific machine learning task, illustrating its potential to significantly improve model accuracy and generalization.

Sep 4, 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

基于自适应的量子机器学习的参数调整策略

Jincheng Zhang

This paper introduces a novel approach to quantum machine learning, termed "Adaptive Quantum Machine Learning" (AQML), that leverages self-adaptation to dynamically adjust model parameters during training based on both model error and gradient information. Traditional parameter adjustment methods often rely on static adjustments, failing to fully exploit the complexities of quantum systems. AQML employs a quantum algorithm to iteratively refine model parameters, effectively optimizing the training process. The core mechanism involves a quantum optimization loop, combined with a robust error analysis framework. We present a preliminary implementation demonstrating the efficacy of this approach in a simulated quantum learning scenario. This work addresses the limitations of existing parameter adjustment techniques by introducing a dynamic, self-optimizing process that can significantly improve model performance.

Sep 4, 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

基于动态量子拓扑的自适应机器学习模型训练

Jincheng Zhang

This paper introduces a novel approach to machine learning training utilizing the principles of dynamic quantum topology. Traditional machine learning methods often rely on static parameter adjustments, while this work leverages the inherent dynamics of quantum topology to optimize model training efficiency. We propose a method that dynamically adjusts model parameters based on the evolution of the quantum topology, resulting in enhanced training speed and potentially improved model performance. The core mechanism centers around a feedback loop that continuously monitors and modifies the quantum topology, guiding the model towards optimal solutions. The proposed approach offers a fundamentally different paradigm for training machine learning models, potentially unlocking new possibilities in efficiency and performance.

Sep 6, 20260 citations