Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

基于动态量子拓扑的量子计算架构

Jincheng Zhang

Published Sep 3, 2026• 0 citations
Publisher

Abstract

This paper introduces a novel quantum computing architecture based on dynamic quantum topology. Traditional quantum computing architectures are often static, limiting the complexity and efficiency of computations. This research proposes a dynamic architecture that adapts quantum state and path adjustments based on the system's evolution, enabling more complex and efficient quantum calculations. The core mechanism involves exploiting the dynamic properties of quantum topology to optimize quantum operations and computational processes. We explore the design of this architecture, detailing its key components and potential benefits. The research presents a framework for dynamically adjusting quantum parameters, ultimately enhancing the performance of quantum algorithms.

DOI
10.5281/zenodo.22279883

Related papers

Quantum Topology: Adaptive Quantum Computing Architectures

Jincheng Zhang

Quantum topology, a fascinating and rapidly developing field, offers a fundamentally new approach to quantum computing. This paper explores the design of an adaptive quantum computing architecture based on quantum topology, aiming to overcome limitations of traditional methods through automated algorithm optimization and enhanced computational capabilities. We present a novel conceptual framework leveraging the unique properties of topological quantum states to dynamically adjust quantum operations, ultimately pushing the boundaries of quantum computation. The core mechanism focuses on exploiting the inherent adaptability of these states, enabling a process that can optimize algorithms and lead to significant performance improvements. This work investigates the potential of this approach to achieve quantum computing breakthroughs.

•Sep 4, 2026•0 citations

基于量子拓扑的自适应量子计算架构

Jincheng Zhang

This paper presents a novel quantum computing architecture based on quantum topology, designed for adaptive computation. Traditional quantum computing architectures rely on fixed parameters, limiting computational possibilities. This architecture leverages quantum topology, dynamically adjusting the quantum system's structure to optimize performance. We demonstrate a practical implementation utilizing a two-qubit system to illustrate the benefits of this approach. The core mechanism involves a self-regulating topology that adapts to the problem being addressed, leading to enhanced computational efficiency. This research explores the potential of quantum topology to revolutionize computational paradigms.

•Sep 2, 2026•0 citations

基于动态拓扑的自适应量子算法

Jincheng Zhang

This paper introduces a novel quantum algorithm based on dynamic topology, designed to enhance the efficiency and performance of quantum algorithms through adaptive parameter tuning. Traditional quantum algorithms often struggle to optimally address complex problems due to static parameter settings. This work proposes a system that dynamically adjusts quantum parameters to optimize the algorithm's execution, resulting in improved results. We detail the core mechanism of the algorithm, emphasizing the use of a dynamic topology structure and an adaptive parameter adjustment process. The paper explores the potential benefits of this approach in various quantum algorithm contexts, demonstrating improvements through simulations and preliminary analysis. This research contributes to the development of intelligent quantum algorithms capable of tackling complex problems with enhanced efficiency.

•Sep 4, 2026•0 citations

Dynamic Topology Quantum Adaptative Algorithm

Jincheng Zhang

This paper investigates the application of dynamic topology principles to quantum computing, specifically focusing on the design of an adaptive quantum algorithm. Traditional quantum algorithms often rely on static calculations, presenting limitations in scalability and efficiency. This research proposes a novel approach utilizing dynamic topology – a mathematical framework that allows for continuous structural changes – to enhance quantum computation. The core mechanism involves constructing a dynamic topology structure that adapts to the quantum state, enabling efficient quantum self-adaptation. The paper demonstrates the potential of this approach to significantly improve the performance of quantum algorithms, particularly in scenarios requiring rapid parameter adjustments.

•Sep 1, 2026•0 citations

基于动态拓扑的量子计算的量子拓扑优化

Jincheng Zhang

Quantum computation, leveraging the principles of quantum mechanics, offers the potential for unprecedented computational power. However, the design and optimization of quantum algorithms often involve complex quantum states, demanding sophisticated techniques. This paper introduces a novel approach to quantum computation – a framework based on dynamic topology – that dynamically adjusts quantum state parameters to optimize performance. We explore the application of this technique to quantum topology optimization, demonstrating its potential to enhance the efficiency of quantum algorithms. The core mechanism revolves around leveraging the inherent flexibility of dynamic topology to guide the evolution of quantum states, ultimately leading to improved computational outcomes. This research aims to provide a robust and adaptable method for quantum computation, addressing a critical challenge in the field.

•Aug 31, 2026•0 citations

自适应拓扑的量子计算的参数调整算法

Jincheng Zhang

This paper presents a novel algorithm for parameter adjustment in quantum computation, leveraging the principles of adaptive topology. Traditional parameter adjustment methods often rely on fixed algorithms, struggling to effectively handle complex environments. We propose a dynamic adjustment approach that utilizes the self-assembling process of quantum systems, dynamically modifying topological control parameters to optimize quantum computation performance and efficiency. The core mechanism involves a sophisticated optimization process guided by the inherent properties of the quantum system, ultimately leading to enhanced computational capabilities. This research explores the potential of this adaptive approach to overcome the limitations of conventional parameter tuning and advance the field of quantum computing.

•Sep 7, 2026•0 citations
This paper introduces a novel quantum computing architecture based on dynamic quantum topology. Traditional quantum computing architectures are often static, limiting the complexity and efficiency of computations. This research proposes a dynamic architecture that adapts quantum state and path adjustments based on the system's evolution, enabling more complex and efficient quantum calculations. The core mechanism involves exploiting the dynamic properties of quantum topology to optimize quantum operations and computational processes. We explore the design of this architecture, detailing its key components and potential benefits. The research presents a framework for dynamically adjusting quantum parameters, ultimately enhancing the performance of quantum algorithms.
Quantum topology, a fascinating and rapidly developing field, offers a fundamentally new approach to quantum computing. This paper explores the design of an adaptive quantum computing architecture based on quantum topology, aiming to overcome limitations of traditional methods through automated algorithm optimization and enhanced computational capabilities. We present a novel conceptual framework leveraging the unique properties of topological quantum states to dynamically adjust quantum operations, ultimately pushing the boundaries of quantum computation. The core mechanism focuses on exploiting the inherent adaptability of these states, enabling a process that can optimize algorithms and lead to significant performance improvements. This work investigates the potential of this approach to achieve quantum computing breakthroughs.
This paper presents a novel quantum computing architecture based on quantum topology, designed for adaptive computation. Traditional quantum computing architectures rely on fixed parameters, limiting computational possibilities. This architecture leverages quantum topology, dynamically adjusting the quantum system's structure to optimize performance. We demonstrate a practical implementation utilizing a two-qubit system to illustrate the benefits of this approach. The core mechanism involves a self-regulating topology that adapts to the problem being addressed, leading to enhanced computational efficiency. This research explores the potential of quantum topology to revolutionize computational paradigms.
This paper introduces a novel quantum algorithm based on dynamic topology, designed to enhance the efficiency and performance of quantum algorithms through adaptive parameter tuning. Traditional quantum algorithms often struggle to optimally address complex problems due to static parameter settings. This work proposes a system that dynamically adjusts quantum parameters to optimize the algorithm's execution, resulting in improved results. We detail the core mechanism of the algorithm, emphasizing the use of a dynamic topology structure and an adaptive parameter adjustment process. The paper explores the potential benefits of this approach in various quantum algorithm contexts, demonstrating improvements through simulations and preliminary analysis. This research contributes to the development of intelligent quantum algorithms capable of tackling complex problems with enhanced efficiency.
This paper investigates the application of dynamic topology principles to quantum computing, specifically focusing on the design of an adaptive quantum algorithm. Traditional quantum algorithms often rely on static calculations, presenting limitations in scalability and efficiency. This research proposes a novel approach utilizing dynamic topology – a mathematical framework that allows for continuous structural changes – to enhance quantum computation. The core mechanism involves constructing a dynamic topology structure that adapts to the quantum state, enabling efficient quantum self-adaptation. The paper demonstrates the potential of this approach to significantly improve the performance of quantum algorithms, particularly in scenarios requiring rapid parameter adjustments.
Quantum computation, leveraging the principles of quantum mechanics, offers the potential for unprecedented computational power. However, the design and optimization of quantum algorithms often involve complex quantum states, demanding sophisticated techniques. This paper introduces a novel approach to quantum computation – a framework based on dynamic topology – that dynamically adjusts quantum state parameters to optimize performance. We explore the application of this technique to quantum topology optimization, demonstrating its potential to enhance the efficiency of quantum algorithms. The core mechanism revolves around leveraging the inherent flexibility of dynamic topology to guide the evolution of quantum states, ultimately leading to improved computational outcomes. This research aims to provide a robust and adaptable method for quantum computation, addressing a critical challenge in the field.
This paper presents a novel algorithm for parameter adjustment in quantum computation, leveraging the principles of adaptive topology. Traditional parameter adjustment methods often rely on fixed algorithms, struggling to effectively handle complex environments. We propose a dynamic adjustment approach that utilizes the self-assembling process of quantum systems, dynamically modifying topological control parameters to optimize quantum computation performance and efficiency. The core mechanism involves a sophisticated optimization process guided by the inherent properties of the quantum system, ultimately leading to enhanced computational capabilities. This research explores the potential of this adaptive approach to overcome the limitations of conventional parameter tuning and advance the field of quantum computing.