基于量子纠缠的自适应神经网络的训练

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum entanglement, a bizarre phenomenon where two particles become linked and share the same fate regardless of distance, has long been considered a theoretical resource with potential applications in quantum computing and quantum information processing. Recently, the concept of quantum entanglement has begun to be explored in machine learning, particularly in the context of quantum neural networks (QNNs). This paper investigates the feasibility of utilizing quantum entanglement to accelerate the training process of neural networks. We propose a novel training strategy that leverages entangled quantum states to enhance the learning dynamics, aiming to overcome limitations inherent in classical neural network training. This work presents a framework for utilizing entangled quantum states to dynamically adjust the weights and biases of a neural network, potentially leading to faster convergence and improved model performance. The core mechanism of this approach involves establishing entangled correlations between nodes and connections within the network, which allows for more efficient exploration of the solution space. We demonstrate the effectiveness of this method through simulations and preliminary experiments, suggesting a promising avenue for advancing the field of quantum machine learning.

DOI: 10.5281/zenodo.22302460

Related papers

基于量子纠缠的自适应机器学习模型

Jincheng Zhang

Quantum entanglement, a bizarre phenomenon where two or more particles become linked in such a way that they share the same fate no matter how far apart they are, has captivated physicists and intrigued mathematicians alike. Its potential applications extend far beyond the realm of theoretical physics, offering intriguing possibilities for advanced computation and information processing. This paper explores the use of quantum entanglement to design a novel self-adaptive machine learning model, aiming to enhance the model's adaptability and robustness. We propose a system where the model parameters are dynamically adjusted through quantum entanglement, leveraging the unique properties of entangled states to facilitate a more nuanced and efficient optimization process. The core mechanism centers around utilizing entanglement to establish a dynamic feedback loop, allowing the model to explore a wider range of parameter spaces and converge towards optimal solutions with enhanced adaptability. The research investigates the theoretical implications of this approach, examining its potential advantages over traditional machine learning techniques and highlighting the challenges involved in practical implementation. The paper concludes by discussing the preliminary results and potential future directions for this research.

Aug 31, 20260 citations

Quantum Entanglement Path Optimization

Jincheng Zhang

Quantum entanglement, a fundamental phenomenon in quantum mechanics, holds immense promise for revolutionizing machine learning, particularly in the optimization of neural networks. This paper explores the potential of leveraging quantum entanglement to significantly enhance the training process of neural networks, aiming to reduce computational complexity and improve model quality. We propose a novel algorithm, Quantum Entanglement Path Optimization, that utilizes entangled qubits to dynamically adjust network parameters during training, fostering a more efficient and effective learning cycle. The core mechanism involves strategically manipulating the entanglement between network elements to guide the optimization process. This paper details the algorithm's design, mathematical framework, and preliminary experimental results, demonstrating a potential for substantial improvements in training speed and model performance. We conclude with a discussion of future research directions and the broader implications of this approach.

Aug 29, 20260 citations

Quantum Entanglement-Based Machine Learning Model

Jincheng Zhang

Quantum entanglement, a bizarre quantum phenomenon, holds the potential to revolutionize various fields, including machine learning. This paper explores the use of quantum entanglement to accelerate machine learning model training and inference. We propose a novel machine learning model architecture that leverages the unique properties of entangled states to significantly enhance these processes. The core mechanism involves exploiting entanglement to expedite the exploration of the solution space, leading to faster convergence and improved accuracy. The research details the theoretical underpinnings of this approach, presents a preliminary implementation, and discusses potential future advancements.

Aug 31, 20260 citations

Quantum Entanglement-Based Neural Network Optimization

Jincheng Zhang

This paper explores the potential of quantum entanglement to revolutionize neural network optimization. We propose a novel algorithm, "Quantum Entanglement-Based Neural Network Optimization," that leverages the instantaneous correlations inherent in entangled states to accelerate training significantly. The core mechanism centers around the design of a specialized "纠缠神经元" network, utilizing quantum entanglement to expedite the learning process. This approach aims to overcome limitations imposed by classical optimization techniques, potentially leading to substantial improvements in model accuracy and training speed. The paper details the algorithm's design, mathematical foundation, and preliminary results, demonstrating the potential of quantum entanglement to enhance neural network optimization.

Sep 2, 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

Quantum Entanglement Adaptive Machine Learning Model

Jincheng Zhang

Quantum entanglement, a bizarre phenomenon where two particles become linked and share the same fate regardless of distance, has captivated scientists and philosophers alike. This research explores the potential of utilizing quantum entanglement to enhance machine learning models, specifically focusing on the concept of adaptive learning and automated optimization. Traditional machine learning algorithms often struggle to adapt to evolving data distributions, leading to suboptimal performance. This paper proposes a novel approach leveraging the inherent correlations within entangled quantum states to dynamically adjust model parameters and optimize the learning process. The core mechanism centers around exploiting the non-classical correlations to introduce a form of self-correction and continuous refinement, potentially surpassing the limitations of classical machine learning techniques. This work investigates the theoretical implications, preliminary experimental setups, and potential applications of this quantum-inspired learning paradigm. The resulting model demonstrates enhanced performance across a range of machine learning tasks, suggesting a significant advancement in adaptive learning capabilities.

Aug 31, 20260 citations