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

基于拓扑的量子态跃迁的动态调整

Jincheng Zhang

Published Sep 1, 2026• 0 citations
Publisher

Abstract

This paper presents a novel dynamic adjustment algorithm for quantum state transitions, leveraging topological structure to optimize the transition process. Traditional quantum state transition algorithms often rely on static, deterministic procedures, limiting their flexibility and efficiency. This work introduces a dynamic adjustment mechanism that utilizes the inherent topological properties of quantum states to dynamically modify transition parameters, resulting in improved efficiency and precision in these transitions. We explore a specific instance of this algorithm, demonstrating its potential to enhance the fidelity and speed of quantum state manipulations. The core concept is to engineer the transition process through a carefully orchestrated manipulation of the underlying topological constraints. The algorithm's effectiveness is evaluated through simulations and analysis, providing a framework for future research and development in quantum state control.

DOI
10.5281/zenodo.22220685

Related papers

自适应拓扑量子态的几何拓扑优化算法

Jincheng Zhang

This paper presents a novel algorithm for optimizing quantum states, specifically focusing on adaptive topology optimization. Traditional quantum computing approaches often rely on static configurations, limiting the ability to dynamically adjust and control quantum states. We introduce a new algorithm, termed "Dynamic Topological Optimization," that leverages the inherent geometric evolution of quantum states to dynamically adjust and control these states. The core mechanism involves learning a mapping between the current quantum state and its geometric transformation, allowing for controlled evolution through a tailored optimization process. This approach promises to enhance computational efficiency and provide novel capabilities in quantum systems. We detail the algorithm's design, provide a theoretical analysis, and demonstrate its effectiveness through simulation results. The paper concludes with a discussion of the potential implications and future research directions.

•Sep 6, 2026•0 citations

The Topological Quantum State Evolution and Manipulation

Jincheng Zhang

This paper explores the application of topological quantum state evolution and manipulation, leveraging the inherent properties of topological structures, to develop a novel algorithm for precise state control. Traditional quantum computing relies on static qubit states, while topology offers a flexible framework for constructing and manipulating quantum states. We propose a method that utilizes dynamic topology adjustments to achieve accurate state manipulation and significantly improve the precision of quantum computations. The research focuses on establishing a robust and efficient framework for simulating complex quantum states and performing precise control operations. This work introduces a new approach to quantum state evolution and manipulation, driven by the principles of topology, aiming to advance the capabilities of quantum computing. The core mechanism centers on the generation of complex topological structures that dynamically modify the system's state.

•Aug 31, 2026•0 citations

Adaptive Dynamic Quantum Topology Adjustment

Jincheng Zhang

Quantum topology is a fascinating field of physics that explores the interplay between quantum mechanics and topology. It posits that space is not fundamentally discrete but rather emerges from the topology of its quantum states. Dynamic quantum topology provides a framework for manipulating these states, offering the potential for novel phenomena and quantum technologies. This paper introduces an innovative algorithm, Adaptive Dynamic Quantum Topology Adjustment, designed to enhance the accuracy and flexibility of quantum topology simulations. The core mechanism leverages a self-adaptive adjustment process, iteratively refining the quantum topology structure through a series of carefully orchestrated operations. This approach overcomes limitations inherent in static topological simulation techniques, offering a pathway toward more realistic and controllable quantum systems. The proposed algorithm demonstrates improved fidelity in simulating complex topological structures and provides a foundation for future advancements in quantum materials and information processing.

•Aug 31, 2026•0 citations

Adaptive Topological Structures for Quantum State Control

Jincheng Zhang

Quantum state control is a fundamental challenge in quantum computation, requiring precise manipulation of quantum states. Traditional approaches often rely on meticulous parameter tuning, limiting the scalability and efficiency of quantum systems. This paper introduces an innovative algorithm leveraging adaptive topological structures to achieve precise state control and efficient state transfer. We propose a novel method for dynamically adjusting the encoding and manipulation of quantum states, utilizing the inherent topological properties to enforce precise control. This approach offers a significant advancement over existing techniques, promising increased control fidelity and improved computational efficiency. The core mechanism centers around the application of self-organizing topological phases, allowing for precise manipulation of quantum states through controlled topology. This work demonstrates the potential of this approach to enhance quantum computation by providing a robust and adaptable framework for state control.

•Aug 31, 2026•0 citations

基于自适应拓扑结构的量子态匹配算法

Jincheng Zhang

This paper presents a novel quantum state matching algorithm based on an adaptive topological structure. Traditional quantum state matching approaches often rely on static matching methods, while our algorithm leverages the inherent properties of topological structures to dynamically match quantum states and enhance system stability. This work introduces a robust framework for quantum state manipulation, offering a significant improvement over existing methods. The algorithm's core mechanism centers around constructing a topological map that facilitates precise state alignment, achieving higher fidelity and stability in quantum systems. This research explores the potential of utilizing topological structures to address challenges in quantum state preservation and control.

•Aug 31, 2026•0 citations

基于量子拓扑的动态拓扑几何变形

Jincheng Zhang

This paper introduces a novel dynamic topology geometric transformation algorithm based on quantum topology. Traditional topological transformation methods often rely on static transformations, limiting their flexibility and efficiency. We propose a method that dynamically adjusts the topology of a system, leveraging the unique properties of quantum topology to achieve transformative changes. This algorithm aims to provide a more adaptable and powerful approach to complex system manipulation. The core mechanism involves a continuous, iterative process of quantum state manipulation, resulting in significant improvements in the transformation speed and the ability to handle intricate geometric arrangements. We demonstrate the effectiveness of this approach through simulations illustrating the dynamic transformation of a 2D lattice structure.

•Sep 4, 2026•0 citations
This paper presents a novel dynamic adjustment algorithm for quantum state transitions, leveraging topological structure to optimize the transition process. Traditional quantum state transition algorithms often rely on static, deterministic procedures, limiting their flexibility and efficiency. This work introduces a dynamic adjustment mechanism that utilizes the inherent topological properties of quantum states to dynamically modify transition parameters, resulting in improved efficiency and precision in these transitions. We explore a specific instance of this algorithm, demonstrating its potential to enhance the fidelity and speed of quantum state manipulations. The core concept is to engineer the transition process through a carefully orchestrated manipulation of the underlying topological constraints. The algorithm's effectiveness is evaluated through simulations and analysis, providing a framework for future research and development in quantum state control.
This paper presents a novel algorithm for optimizing quantum states, specifically focusing on adaptive topology optimization. Traditional quantum computing approaches often rely on static configurations, limiting the ability to dynamically adjust and control quantum states. We introduce a new algorithm, termed "Dynamic Topological Optimization," that leverages the inherent geometric evolution of quantum states to dynamically adjust and control these states. The core mechanism involves learning a mapping between the current quantum state and its geometric transformation, allowing for controlled evolution through a tailored optimization process. This approach promises to enhance computational efficiency and provide novel capabilities in quantum systems. We detail the algorithm's design, provide a theoretical analysis, and demonstrate its effectiveness through simulation results. The paper concludes with a discussion of the potential implications and future research directions.
This paper explores the application of topological quantum state evolution and manipulation, leveraging the inherent properties of topological structures, to develop a novel algorithm for precise state control. Traditional quantum computing relies on static qubit states, while topology offers a flexible framework for constructing and manipulating quantum states. We propose a method that utilizes dynamic topology adjustments to achieve accurate state manipulation and significantly improve the precision of quantum computations. The research focuses on establishing a robust and efficient framework for simulating complex quantum states and performing precise control operations. This work introduces a new approach to quantum state evolution and manipulation, driven by the principles of topology, aiming to advance the capabilities of quantum computing. The core mechanism centers on the generation of complex topological structures that dynamically modify the system's state.
Quantum topology is a fascinating field of physics that explores the interplay between quantum mechanics and topology. It posits that space is not fundamentally discrete but rather emerges from the topology of its quantum states. Dynamic quantum topology provides a framework for manipulating these states, offering the potential for novel phenomena and quantum technologies. This paper introduces an innovative algorithm, Adaptive Dynamic Quantum Topology Adjustment, designed to enhance the accuracy and flexibility of quantum topology simulations. The core mechanism leverages a self-adaptive adjustment process, iteratively refining the quantum topology structure through a series of carefully orchestrated operations. This approach overcomes limitations inherent in static topological simulation techniques, offering a pathway toward more realistic and controllable quantum systems. The proposed algorithm demonstrates improved fidelity in simulating complex topological structures and provides a foundation for future advancements in quantum materials and information processing.
Quantum state control is a fundamental challenge in quantum computation, requiring precise manipulation of quantum states. Traditional approaches often rely on meticulous parameter tuning, limiting the scalability and efficiency of quantum systems. This paper introduces an innovative algorithm leveraging adaptive topological structures to achieve precise state control and efficient state transfer. We propose a novel method for dynamically adjusting the encoding and manipulation of quantum states, utilizing the inherent topological properties to enforce precise control. This approach offers a significant advancement over existing techniques, promising increased control fidelity and improved computational efficiency. The core mechanism centers around the application of self-organizing topological phases, allowing for precise manipulation of quantum states through controlled topology. This work demonstrates the potential of this approach to enhance quantum computation by providing a robust and adaptable framework for state control.
This paper presents a novel quantum state matching algorithm based on an adaptive topological structure. Traditional quantum state matching approaches often rely on static matching methods, while our algorithm leverages the inherent properties of topological structures to dynamically match quantum states and enhance system stability. This work introduces a robust framework for quantum state manipulation, offering a significant improvement over existing methods. The algorithm's core mechanism centers around constructing a topological map that facilitates precise state alignment, achieving higher fidelity and stability in quantum systems. This research explores the potential of utilizing topological structures to address challenges in quantum state preservation and control.
This paper introduces a novel dynamic topology geometric transformation algorithm based on quantum topology. Traditional topological transformation methods often rely on static transformations, limiting their flexibility and efficiency. We propose a method that dynamically adjusts the topology of a system, leveraging the unique properties of quantum topology to achieve transformative changes. This algorithm aims to provide a more adaptable and powerful approach to complex system manipulation. The core mechanism involves a continuous, iterative process of quantum state manipulation, resulting in significant improvements in the transformation speed and the ability to handle intricate geometric arrangements. We demonstrate the effectiveness of this approach through simulations illustrating the dynamic transformation of a 2D lattice structure.