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

基于自适应拓扑结构的量子态匹配算法

Jincheng Zhang

Published Aug 31, 2026• 0 citations
Publisher

Abstract

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.

DOI
10.5281/zenodo.22195284

Related papers

基于拓扑学的量子态控制的自适应算法

Jincheng Zhang

This paper presents a novel quantum state control algorithm based on topological structures, aiming to enhance the precision and control capabilities of quantum computation. Traditional quantum control methods often rely on simple pulses, while topological control offers a fundamentally different approach. We introduce a dynamic topological structure adjustment mechanism to precisely manipulate quantum states and facilitate state transitions, demonstrating significant improvements in control accuracy compared to existing techniques. The core mechanism centers on constructing and manipulating topological states, allowing for a more robust and controllable quantum system. This research explores the potential of topological approaches to revolutionize quantum 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

基于拓扑结构的动态量子态学习算法

Jincheng Zhang

This paper introduces a novel dynamic quantum state learning algorithm based on topological structures. Traditional machine learning methods often require manual parameter tuning, limiting adaptability to complex environments. We propose a method that leverages topological structures as a guiding principle, enabling the system to dynamically evolve quantum states and optimize quantum computing performance. The core mechanism centers around identifying and exploiting the inherent topological properties of a quantum system, facilitating rapid and intelligent state evolution. This approach overcomes limitations of traditional methods by providing a more robust and adaptable framework for quantum state manipulation. The presented algorithm demonstrates significant improvements in quantum state fidelity and computational efficiency compared to existing techniques.

•Aug 31, 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

基于拓扑的量子态跃迁的动态调整

Jincheng Zhang

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.

•Sep 1, 2026•0 citations

自适应拓扑结构的量子纠缠学习算法

Jincheng Zhang

This paper presents a novel quantum entanglement learning algorithm leveraging the principles of adaptive topology. Traditional quantum entanglement learning methods often rely on manipulating individual qubits, while this approach aims to dynamically construct and refine a quantum state to optimize entanglement properties. We introduce a framework centered around the construction of a 'topology map' that guides the evolution of the quantum state, allowing for a more efficient and accurate learning process. The core mechanism focuses on exploiting the inherent properties of quantum topology to represent and manipulate entanglement, leading to improved entanglement fidelity and reduced computational complexity. The algorithm demonstrates promising results in achieving precise entanglement manipulation, offering a potential pathway to enhanced quantum information processing.

•Sep 1, 2026•0 citations
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 presents a novel quantum state control algorithm based on topological structures, aiming to enhance the precision and control capabilities of quantum computation. Traditional quantum control methods often rely on simple pulses, while topological control offers a fundamentally different approach. We introduce a dynamic topological structure adjustment mechanism to precisely manipulate quantum states and facilitate state transitions, demonstrating significant improvements in control accuracy compared to existing techniques. The core mechanism centers on constructing and manipulating topological states, allowing for a more robust and controllable quantum system. This research explores the potential of topological approaches to revolutionize quantum 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 introduces a novel dynamic quantum state learning algorithm based on topological structures. Traditional machine learning methods often require manual parameter tuning, limiting adaptability to complex environments. We propose a method that leverages topological structures as a guiding principle, enabling the system to dynamically evolve quantum states and optimize quantum computing performance. The core mechanism centers around identifying and exploiting the inherent topological properties of a quantum system, facilitating rapid and intelligent state evolution. This approach overcomes limitations of traditional methods by providing a more robust and adaptable framework for quantum state manipulation. The presented algorithm demonstrates significant improvements in quantum state fidelity and computational efficiency compared to existing techniques.
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.
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 quantum entanglement learning algorithm leveraging the principles of adaptive topology. Traditional quantum entanglement learning methods often rely on manipulating individual qubits, while this approach aims to dynamically construct and refine a quantum state to optimize entanglement properties. We introduce a framework centered around the construction of a 'topology map' that guides the evolution of the quantum state, allowing for a more efficient and accurate learning process. The core mechanism focuses on exploiting the inherent properties of quantum topology to represent and manipulate entanglement, leading to improved entanglement fidelity and reduced computational complexity. The algorithm demonstrates promising results in achieving precise entanglement manipulation, offering a potential pathway to enhanced quantum information processing.