基于量子纠缠的自适应自组织结构识别

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel algorithm for identifying and modeling complex quantum systems by leveraging the unique properties of quantum entanglement. Traditional structure recognition methods often struggle with the intricacies of quantum systems, necessitating a more sophisticated approach. We propose a quantum-enhanced self-organizing structure recognition system based on the principles of quantum entanglement, enabling the identification and modeling of intricate structural patterns. The core of our algorithm involves the construction of quantum systems with self-organized structures, utilizing entanglement as a fundamental mechanism for pattern recognition and structural analysis. This approach offers a fundamentally different perspective on structure identification compared to classical methods, potentially unlocking new insights into the behavior of complex quantum systems. This work explores the theoretical foundations and preliminary experimental validation of this innovative method.

DOI: 10.5281/zenodo.22204761

Related papers

基于量子纠缠的模式识别算法

Jincheng Zhang

This paper introduces a novel pattern recognition algorithm leveraging the unique properties of quantum entanglement. The algorithm utilizes a quantum entanglement-based processing framework to enhance pattern identification and classification, specifically designed to effectively handle complex patterns often found within data. Traditional pattern recognition methods can be susceptible to noise, making them suboptimal for intricate datasets. Our proposed approach aims to overcome these limitations by exploiting the correlated nature of entangled particles, offering a potential paradigm shift in pattern analysis. The core mechanism centers around establishing and maintaining entanglement between data points, allowing for a more robust and efficient pattern recognition process. This paper details the algorithm's design, implementation, and preliminary experimental results demonstrating its effectiveness in identifying patterns within complex datasets.

Sep 3, 20260 citations

基于量子态的模式识别算法

Jincheng Zhang

This paper introduces a novel pattern recognition algorithm based on quantum state manipulation, leveraging the unique properties of quantum entanglement to achieve rapid identification and modeling of complex patterns, particularly in large and high-dimensional datasets. Traditional pattern recognition methods rely on classical information retrieval, which suffers from inefficiency. We propose a quantum-enhanced approach that utilizes quantum state manipulation to drastically improve data analysis efficiency and accuracy. The core mechanism centers around the utilization of quantum entanglement to facilitate efficient pattern recognition and representation. This paper details the design of the algorithm, its theoretical underpinnings, and preliminary experimental results demonstrating its potential for significant improvements in pattern analysis.

Aug 31, 20260 citations

Quantum-Enhanced Pattern Recognition

Jincheng Zhang

This paper presents a novel approach to pattern recognition centered around the application of quantum entanglement to significantly enhance the accuracy and speed of identifying subtle patterns. Traditional pattern recognition methods often struggle with complex datasets and require extensive computational resources. We propose a system that utilizes a quantum-inspired 'state vector' to capture intricate patterns, offering a potential paradigm shift in this field. The core mechanism involves establishing entanglement between quantum systems, which allows for a unique level of correlation and information processing, ultimately leading to improved pattern identification capabilities. This work explores the theoretical foundations and preliminary experimental validation of this approach, aiming to demonstrate substantial improvements over existing techniques.

Sep 2, 20260 citations

基于量子神经网络的非线性模式识别

Jincheng Zhang

This paper explores the application of quantum neural networks (QNNs) to enhance pattern recognition capabilities, particularly in the analysis of complex datasets. Traditional neural networks often struggle with non-linear data patterns due to limitations in their ability to capture intricate relationships. Quantum neural networks, leveraging quantum mechanical principles, offer a potential paradigm shift in pattern recognition by introducing inherent quantum properties that facilitate novel feature extraction and pattern understanding. We propose a novel QNN architecture designed to automatically learn hidden patterns and relationships within data, aiming to surpass the capabilities of conventional neural networks. This work investigates the potential of quantum-enhanced neural networks for identifying complex, non-linear patterns, demonstrating its efficacy through simulation and preliminary experimentation. The core mechanism involves utilizing quantum entanglement and superposition to represent and process data in a fundamentally different manner, ultimately leading to improved pattern recognition accuracy.

Sep 2, 20260 citations

Self-Organizing Network Learning: A Quantum Model

Jincheng Zhang

This paper introduces a novel self-organizing network learning model based on quantum hybridization. Leveraging the unique properties of quantum entanglement and superposition, we propose a framework for automatically learning and optimizing complex network structures. The core mechanism involves transforming network nodes and edges into quantum states, utilizing entanglement to facilitate self-organization and optimization. This approach offers a fundamentally different paradigm compared to traditional network learning techniques, potentially leading to more robust and adaptable learning algorithms. We discuss the theoretical foundations, experimental validation, and potential applications of this quantum-enhanced network learning model.

Sep 5, 20260 citations

Quantum Entanglement-Based Adaptive Topology Structure Recognition

Jincheng Zhang

This paper introduces a novel algorithm for the recognition of topological structures based on quantum entanglement, leveraging a novel computational framework to simulate complex physical phenomena with unprecedented accuracy and speed. Traditional computational methods struggle to effectively model intricate physical scenarios. This work proposes a fundamentally new approach to topology recognition utilizing quantum entanglement, offering a potential paradigm shift in computational capabilities. The core mechanism centers on mapping a computational process into a quantum entanglement-based computation, achieving enhanced simulation and calculation efficiency. This approach promises to overcome limitations of existing methods, particularly in the modeling of complex physical systems. The paper details the algorithm's architecture, demonstrates its effectiveness through simulations, and explores potential applications across various fields.

Sep 4, 20260 citations