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

基于动态粒子的量子隐形矩阵计算

Jincheng Zhang

Published Sep 1, 2026• 0 citations
Publisher

Abstract

This paper presents a novel approach to quantum computation, utilizing a dynamic particle system to efficiently compute quantum hidden-matrix operations. Traditional quantum algorithms often rely on classical computation, limiting their potential for complex calculations. We introduce a new method that leverages the unique properties of dynamic particles – specifically, quantum entanglement and the ability to manipulate quantum states – to achieve a significant improvement in computational speed and efficiency. This work explores the potential of dynamically evolving particle states to accelerate the process of quantum computation, offering a fundamentally different paradigm compared to existing approaches. The core mechanism involves employing a dynamically generated quantum state, guided by entanglement, to perform the required calculations, ultimately demonstrating a substantial reduction in computational complexity. This research provides a foundation for future advancements in quantum algorithm design and implementation.

DOI
10.5281/zenodo.22222102

Related papers

基于非对称矩阵运算的自适应量子计算框架

Jincheng Zhang

This paper introduces a novel quantum computing framework based on asymmetric matrix operations, leveraging quantum entanglement to accelerate computation. Traditional quantum computing relies on classical computation, while this framework introduces a unique approach that utilizes the inherent properties of asymmetric matrix operations and quantum entanglement for enhanced efficiency and stability. The core mechanism involves strategically introducing entanglement within matrix operations to dramatically improve computational speed. This work addresses the limitations of existing quantum algorithms by offering a fundamentally different approach to processing information, potentially unlocking new capabilities in complex calculations. The framework demonstrates significant improvements in specific quantum algorithms through the application of these novel techniques. This research explores the potential for significant advancements in quantum computation by introducing a new paradigm centered around asymmetric matrix operations and entanglement.

•Sep 4, 2026•0 citations

量化信息聚合的非线性矩阵运算

Jincheng Zhang

This paper introduces a novel matrix operation based on quantum entanglement, designed to significantly enhance the efficiency and computational capabilities of quantum computing. Traditional matrix operations often face limitations in speed and memory usage, particularly when dealing with large, complex quantum states. We propose a quantum entanglement-driven approach, leveraging the unique properties of quantum entanglement to accelerate the processing of quantum information. This research explores a new method for efficiently aggregating quantum information by encoding and manipulating quantum states through entangled correlations. The core mechanism centers on establishing a quantum entanglement framework that facilitates rapid information transfer and operation, offering a potential paradigm shift in quantum computation. The study demonstrates the feasibility and potential benefits of this approach, highlighting its promise for advancing quantum algorithms and computational paradigms.

•Sep 5, 2026•0 citations

Quantum Entanglement Matrix Operations Dynamic Mapping

Jincheng Zhang

This paper presents a novel dynamic mapping algorithm based on quantum entanglement, designed to accelerate matrix computations and handle more complex matrix operations than traditional methods. The core innovation lies in leveraging the unique properties of quantum entanglement to achieve faster and more efficient calculations. We introduce a framework utilizing entangled qubit states to represent and manipulate matrix elements, enabling a significant reduction in computational complexity. The algorithm's effectiveness is demonstrated through theoretical analysis and preliminary experimental validation using a simplified matrix multiplication task. The research explores the potential of entanglement to overcome limitations in classical matrix operations, offering a promising avenue for advancements in numerical linear algebra and related fields.

•Aug 31, 2026•0 citations

Core Claim: We propose a novel matrix computation method based on quantum entanglement, leveraging the entanglement properties to enhance the computational efficiency of matrix operations.

Jincheng Zhang

Quantum entanglement, a fundamental phenomenon in quantum mechanics, offers a unique pathway to computational advantage. This paper investigates a new approach to matrix computation, utilizing quantum entanglement as a core mechanism to achieve significantly improved computational efficiency compared to classical methods. The proposed method explores the potential of manipulating entangled states to perform matrix operations, demonstrating a promising pathway towards a more efficient and scalable quantum computation. We present a detailed theoretical framework and preliminary implementation demonstrating the potential of this approach. The research focuses on enhancing matrix multiplication and eigenvalue calculations, offering a foundation for future explorations in quantum algorithms and hardware development.

•Sep 5, 2026•0 citations

Dynamic Quantum Entanglement Computation Framework

Jincheng Zhang

This paper introduces a novel dynamic quantum entanglement computation framework designed to address the limitations of traditional quantum computing. The framework leverages the unique properties of quantum entanglement to enable complex calculations with enhanced efficiency. We explore a novel approach utilizing entanglement as a computational resource, creating a dynamically adjustable quantum state that facilitates a streamlined and accelerated computation process. The paper details the core mechanism, outlining the framework's architecture and the mathematical foundations underpinning its effectiveness. This design offers significant advantages in terms of algorithm complexity and computational speed, potentially unlocking new possibilities for quantum algorithm development. The work presents a practical implementation demonstrating the framework's capabilities, showcasing its potential for significantly advancing quantum computation.

•Aug 31, 2026•0 citations

Dynamic Quantum Entanglement Computation

Jincheng Zhang

This paper introduces a novel computational algorithm, Dynamic Quantum Entanglement Computation (DQEC), designed to accelerate the computation of quantum entanglement states. Traditional quantum computing approaches often struggle with the complexity associated with dynamically evolving quantum states. DQEC leverages a dynamic adjustment of the quantum state to achieve significantly improved computational efficiency, offering a promising path towards enhanced quantum computation and information processing capabilities. The core mechanism focuses on dynamically modifying the quantum state through a carefully designed control sequence, optimizing for the specific entanglement calculations required. We present a theoretical analysis demonstrating the algorithm's potential for substantial speedups compared to existing methods, and provide an implementation outline for further exploration. This work contributes to the advancement of quantum algorithms and addresses a key challenge in scaling quantum systems.

•Sep 4, 2026•0 citations
This paper presents a novel approach to quantum computation, utilizing a dynamic particle system to efficiently compute quantum hidden-matrix operations. Traditional quantum algorithms often rely on classical computation, limiting their potential for complex calculations. We introduce a new method that leverages the unique properties of dynamic particles – specifically, quantum entanglement and the ability to manipulate quantum states – to achieve a significant improvement in computational speed and efficiency. This work explores the potential of dynamically evolving particle states to accelerate the process of quantum computation, offering a fundamentally different paradigm compared to existing approaches. The core mechanism involves employing a dynamically generated quantum state, guided by entanglement, to perform the required calculations, ultimately demonstrating a substantial reduction in computational complexity. This research provides a foundation for future advancements in quantum algorithm design and implementation.
This paper introduces a novel quantum computing framework based on asymmetric matrix operations, leveraging quantum entanglement to accelerate computation. Traditional quantum computing relies on classical computation, while this framework introduces a unique approach that utilizes the inherent properties of asymmetric matrix operations and quantum entanglement for enhanced efficiency and stability. The core mechanism involves strategically introducing entanglement within matrix operations to dramatically improve computational speed. This work addresses the limitations of existing quantum algorithms by offering a fundamentally different approach to processing information, potentially unlocking new capabilities in complex calculations. The framework demonstrates significant improvements in specific quantum algorithms through the application of these novel techniques. This research explores the potential for significant advancements in quantum computation by introducing a new paradigm centered around asymmetric matrix operations and entanglement.
This paper introduces a novel matrix operation based on quantum entanglement, designed to significantly enhance the efficiency and computational capabilities of quantum computing. Traditional matrix operations often face limitations in speed and memory usage, particularly when dealing with large, complex quantum states. We propose a quantum entanglement-driven approach, leveraging the unique properties of quantum entanglement to accelerate the processing of quantum information. This research explores a new method for efficiently aggregating quantum information by encoding and manipulating quantum states through entangled correlations. The core mechanism centers on establishing a quantum entanglement framework that facilitates rapid information transfer and operation, offering a potential paradigm shift in quantum computation. The study demonstrates the feasibility and potential benefits of this approach, highlighting its promise for advancing quantum algorithms and computational paradigms.
This paper presents a novel dynamic mapping algorithm based on quantum entanglement, designed to accelerate matrix computations and handle more complex matrix operations than traditional methods. The core innovation lies in leveraging the unique properties of quantum entanglement to achieve faster and more efficient calculations. We introduce a framework utilizing entangled qubit states to represent and manipulate matrix elements, enabling a significant reduction in computational complexity. The algorithm's effectiveness is demonstrated through theoretical analysis and preliminary experimental validation using a simplified matrix multiplication task. The research explores the potential of entanglement to overcome limitations in classical matrix operations, offering a promising avenue for advancements in numerical linear algebra and related fields.
Quantum entanglement, a fundamental phenomenon in quantum mechanics, offers a unique pathway to computational advantage. This paper investigates a new approach to matrix computation, utilizing quantum entanglement as a core mechanism to achieve significantly improved computational efficiency compared to classical methods. The proposed method explores the potential of manipulating entangled states to perform matrix operations, demonstrating a promising pathway towards a more efficient and scalable quantum computation. We present a detailed theoretical framework and preliminary implementation demonstrating the potential of this approach. The research focuses on enhancing matrix multiplication and eigenvalue calculations, offering a foundation for future explorations in quantum algorithms and hardware development.
This paper introduces a novel dynamic quantum entanglement computation framework designed to address the limitations of traditional quantum computing. The framework leverages the unique properties of quantum entanglement to enable complex calculations with enhanced efficiency. We explore a novel approach utilizing entanglement as a computational resource, creating a dynamically adjustable quantum state that facilitates a streamlined and accelerated computation process. The paper details the core mechanism, outlining the framework's architecture and the mathematical foundations underpinning its effectiveness. This design offers significant advantages in terms of algorithm complexity and computational speed, potentially unlocking new possibilities for quantum algorithm development. The work presents a practical implementation demonstrating the framework's capabilities, showcasing its potential for significantly advancing quantum computation.
This paper introduces a novel computational algorithm, Dynamic Quantum Entanglement Computation (DQEC), designed to accelerate the computation of quantum entanglement states. Traditional quantum computing approaches often struggle with the complexity associated with dynamically evolving quantum states. DQEC leverages a dynamic adjustment of the quantum state to achieve significantly improved computational efficiency, offering a promising path towards enhanced quantum computation and information processing capabilities. The core mechanism focuses on dynamically modifying the quantum state through a carefully designed control sequence, optimizing for the specific entanglement calculations required. We present a theoretical analysis demonstrating the algorithm's potential for substantial speedups compared to existing methods, and provide an implementation outline for further exploration. This work contributes to the advancement of quantum algorithms and addresses a key challenge in scaling quantum systems.