基于自适应量子算法的拓扑信息提取

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper presents a novel approach to extracting topological information from complex systems using a dynamically adaptive quantum algorithm. Traditional methods rely on classical computation, while this research leverages quantum computation's unique properties to achieve more precise and efficient topological information extraction. The core mechanism involves quantum simulation, followed by an adaptive quantum algorithm designed to identify and quantify key topological features. We demonstrate the effectiveness of this approach through a series of simulations of a simplified, yet representative, system exhibiting topological properties. The results highlight the potential of this method to overcome limitations of existing techniques, offering a pathway towards deeper insights into complex phenomena.

DOI: 10.5281/zenodo.22303560

Related papers

自适应量子纠缠的拓扑信息分析

Jincheng Zhang

This paper introduces a novel approach to topology analysis utilizing self-adapted quantum entanglement. Traditional topology methods rely on classical computation, while quantum entanglement offers the potential for significantly accelerated analysis. We propose a method that leverages the unique properties of entangled states to effectively analyze complex topological structures, ultimately providing a more efficient computational framework. The core mechanism centers on exploiting the superposition and entanglement dynamics inherent in quantum systems to uncover subtle topological relationships that are difficult to discern with classical techniques. This research explores the potential of this approach to enhance the analysis of intricate topological landscapes, offering a promising pathway toward improved computational efficiency and a deeper understanding of topological phenomena.

Aug 31, 20260 citations

基于量子算法的自适应拓扑结构分析

Jincheng Zhang

This paper explores the application of quantum algorithms to enhance the analysis of complex systems, with a particular focus on identifying and quantifying intricate structural properties. We propose a novel method utilizing quantum computation to accelerate the assessment of topological structures within systems exhibiting multiple nonlinear relationships. The core mechanism involves leveraging quantum algorithms to perform computationally intensive topological analysis, offering a potentially transformative approach to modeling and understanding these systems. This research addresses the limitations of traditional methods by incorporating quantum computation to achieve enhanced efficiency and accuracy. We present preliminary results demonstrating the effectiveness of this approach in analyzing a specific, albeit simplified, example system, highlighting the potential for wider applicability.

Sep 7, 20260 citations

基于自适应拓扑的量子特征提取

Jincheng Zhang

This paper presents a novel quantum feature extraction algorithm, termed "Adaptive Topological Feature Extraction" (ATFE), designed to efficiently extract complex system features leveraging the unique properties of quantum computation. The algorithm utilizes a dynamically constructed topological structure to represent and analyze data, offering a significant advantage over traditional methods in terms of computational efficiency and sensitivity to data variations. The core mechanism centers around the creation of a topologically-constrained representation, which is then analyzed using quantum algorithms to reveal underlying patterns and relationships within the input data. This paper explores the potential of ATFE to enhance data analysis and model building, specifically focusing on its application to complex systems. We demonstrate the algorithm's effectiveness through a series of illustrative examples and discuss its future directions.

Sep 4, 20260 citations

基于动态拓扑的基于量子计算的复杂系统建模

Jincheng Zhang

This paper presents a novel method for complex system modeling utilizing dynamic topology and quantum computing. Traditional approaches often rely on static parameters, limiting the system's ability to accurately represent complex behavior. This work introduces a new algorithm that leverages the unique properties of dynamic topology to simulate complex systems with enhanced precision and efficiency. The core mechanism focuses on dynamically evolving system states through quantum computations, offering a significant advancement over existing techniques. This advancement will improve the accuracy of simulation and prediction of complex systems.

Sep 6, 20260 citations

Core Claim: Develop a method for adaptive quantum entanglement-based topological computation, leveraging the properties of quantum entanglement to achieve precise calculation of topological structures and applying it to both quantum computing and quantum information processing.

Jincheng Zhang

Quantum entanglement is a cornerstone of quantum mechanics, offering a unique pathway to manipulating and exploring complex quantum states. This research focuses on leveraging this property to develop a novel adaptive topological computation method. The core of our approach involves harnessing the inherent correlations within entangled quantum systems to achieve precise calculations of topological structures. Traditional methods often struggle with the inherent complexities of topological calculations, necessitating significant computational resources. This work introduces a new algorithm, incorporating adaptive techniques to optimize the efficiency of this computation. We demonstrate the efficacy of this method through a series of carefully designed simulations and analyses, highlighting its potential to significantly advance both quantum computing and quantum information processing. The primary contribution lies in the development of a robust and adaptable framework for achieving precise topological structure determination, overcoming limitations inherent in conventional approaches. This research provides a foundational step towards more efficient and reliable topological computation, paving the way for broader applications in quantum technologies.

Aug 31, 20260 citations

基于自适应量子计算的拓扑优化算法

Jincheng Zhang

This paper introduces a novel quantum optimization algorithm, termed "Adaptive Topological Optimization," designed to enhance the performance of quantum computations. Traditional optimization methods often struggle with the inherent complexity of quantum systems, necessitating a shift towards quantum-specific approaches. This research focuses on leveraging the unique properties of quantum mechanics – particularly the topological nature of quantum states – to guide optimization processes, offering a potential paradigm shift in quantum algorithm design. The core of this algorithm utilizes a dynamically adjusted quantum state to explore the solution space more efficiently and identify optimal solutions with superior performance. We demonstrate the effectiveness of this approach through a series of simulations and preliminary results, highlighting its potential to significantly improve the efficiency and scalability of quantum computations. The paper concludes with a discussion of the algorithm's underlying principles, its potential applications, and future research directions.

Sep 2, 20260 citations