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

Quantum Entanglement Matrix Operations

Jincheng Zhang

Published Sep 7, 2026• 0 citations
Publisher

Abstract

This paper investigates the potential of utilizing quantum entanglement to revolutionize matrix operations. The core claim is to design a novel matrix multiplication and addition method based on quantum entanglement, exhibiting quantum acceleration and self-correction mechanisms via entanglement parameters. Classical matrix operations are often plagued by errors, necessitating a new approach. This research explores the theoretical foundations and potential implementation of this paradigm shift, aiming to surpass the limitations of traditional methods through the unique properties of quantum entanglement.

DOI
10.5281/zenodo.22636403

Related papers

Nonlinear Matrix Operations: Quantum Entanglement Enhanced Computation

Jincheng Zhang

This paper explores the potential of quantum entanglement to revolutionize matrix operations, specifically aiming to overcome the limitations of classical computers. We propose a novel approach to matrix computation that leverages entanglement to transfer the core computational process across quantum systems, thereby achieving enhanced efficiency and precision. The core claim centers on the design of a quantum-based matrix multiplication algorithm that surpasses current computational capabilities. This research investigates the theoretical basis, potential implementation challenges, and preliminary experimental explorations of this quantum approach. The paper discusses the core mechanism, its implications for computational power, and its potential impact on both quantum computing and machine learning.

•Sep 3, 2026•0 citations

Quantum Entanglement-Based Complex Matrix Operations

Jincheng Zhang

Abstract: This paper explores the potential of quantum entanglement to revolutionize complex matrix operations. Traditional methods often struggle with numerical instability, limiting the scalability of these calculations. We propose a novel approach utilizing entangled qubits to perform matrix multiplications with significantly reduced error rates, offering a potential paradigm shift in computational efficiency. The core mechanism centers around a quantum process that intrinsically mitigates numerical errors inherent in classical methods.

•Sep 4, 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

Core Claim: Design a matrix multiplication algorithm based on quantum self-adaptation, utilizing quantum entanglement to accelerate the computational speed of matrix operations.

Jincheng Zhang

This paper investigates a novel matrix multiplication algorithm leveraging quantum self-adaptation to enhance computational efficiency and stability. Traditional matrix multiplication relies on classical computation, presenting limitations in terms of speed and robustness. This work proposes a quantum self-adaptation mechanism that introduces quantum entanglement into the matrix multiplication process, fundamentally altering the way computations are performed. The algorithm's core design centers around exploiting the unique properties of quantum entanglement to accelerate the core steps of the matrix multiplication. We present a theoretical analysis and preliminary implementation demonstrating a significant speedup compared to traditional methods. The study focuses on the potential for quantum algorithms to surpass classical capabilities in computationally intensive tasks, specifically within the realm of matrix operations. The paper concludes with a discussion of future research directions and potential applications.

•Sep 4, 2026•0 citations

Quantum Computing Foundation of High-Order Matrix Operations

Jincheng Zhang

This paper investigates the potential of utilizing quantum entanglement to revolutionize matrix operations, a crucial component of many linear algebra algorithms. The core claim is the development of a novel quantum computing approach that leverages quantum entanglement to accelerate complex linear algebra calculations. We propose a new algorithmic framework centered around quantum state manipulation, specifically designed to exploit the correlated properties of entangled qubits to significantly reduce computational complexity. This work will present a theoretical foundation for this approach, outlining the key mechanisms involved, and exploring the potential for practical implementation. The focus will be on demonstrating the theoretical benefits and providing a roadmap for future research.

•Sep 3, 2026•0 citations

基于量子纠缠的非线性矩阵乘法算法

Jincheng Zhang

This paper introduces a novel algorithmic framework for matrix multiplication based on the principles of quantum entanglement. Traditional matrix multiplication algorithms are inherently limited by the classical computational resources of computers. We propose a quantum-enhanced approach utilizing quantum entanglement to mitigate these limitations, aiming to improve computational efficiency and stability. The core mechanism involves mapping the matrix elements to entangled states, enabling parallel processing and inherent error correction. This approach offers a promising path towards accelerating matrix operations and potentially revolutionizing certain computational tasks. The paper details the theoretical framework, initial implementation, and preliminary experimental results demonstrating the algorithmic benefits of quantum entanglement in matrix multiplication.

•Sep 7, 2026•0 citations
This paper investigates the potential of utilizing quantum entanglement to revolutionize matrix operations. The core claim is to design a novel matrix multiplication and addition method based on quantum entanglement, exhibiting quantum acceleration and self-correction mechanisms via entanglement parameters. Classical matrix operations are often plagued by errors, necessitating a new approach. This research explores the theoretical foundations and potential implementation of this paradigm shift, aiming to surpass the limitations of traditional methods through the unique properties of quantum entanglement.
This paper explores the potential of quantum entanglement to revolutionize matrix operations, specifically aiming to overcome the limitations of classical computers. We propose a novel approach to matrix computation that leverages entanglement to transfer the core computational process across quantum systems, thereby achieving enhanced efficiency and precision. The core claim centers on the design of a quantum-based matrix multiplication algorithm that surpasses current computational capabilities. This research investigates the theoretical basis, potential implementation challenges, and preliminary experimental explorations of this quantum approach. The paper discusses the core mechanism, its implications for computational power, and its potential impact on both quantum computing and machine learning.
Abstract: This paper explores the potential of quantum entanglement to revolutionize complex matrix operations. Traditional methods often struggle with numerical instability, limiting the scalability of these calculations. We propose a novel approach utilizing entangled qubits to perform matrix multiplications with significantly reduced error rates, offering a potential paradigm shift in computational efficiency. The core mechanism centers around a quantum process that intrinsically mitigates numerical errors inherent in classical methods.
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 investigates a novel matrix multiplication algorithm leveraging quantum self-adaptation to enhance computational efficiency and stability. Traditional matrix multiplication relies on classical computation, presenting limitations in terms of speed and robustness. This work proposes a quantum self-adaptation mechanism that introduces quantum entanglement into the matrix multiplication process, fundamentally altering the way computations are performed. The algorithm's core design centers around exploiting the unique properties of quantum entanglement to accelerate the core steps of the matrix multiplication. We present a theoretical analysis and preliminary implementation demonstrating a significant speedup compared to traditional methods. The study focuses on the potential for quantum algorithms to surpass classical capabilities in computationally intensive tasks, specifically within the realm of matrix operations. The paper concludes with a discussion of future research directions and potential applications.
This paper investigates the potential of utilizing quantum entanglement to revolutionize matrix operations, a crucial component of many linear algebra algorithms. The core claim is the development of a novel quantum computing approach that leverages quantum entanglement to accelerate complex linear algebra calculations. We propose a new algorithmic framework centered around quantum state manipulation, specifically designed to exploit the correlated properties of entangled qubits to significantly reduce computational complexity. This work will present a theoretical foundation for this approach, outlining the key mechanisms involved, and exploring the potential for practical implementation. The focus will be on demonstrating the theoretical benefits and providing a roadmap for future research.
This paper introduces a novel algorithmic framework for matrix multiplication based on the principles of quantum entanglement. Traditional matrix multiplication algorithms are inherently limited by the classical computational resources of computers. We propose a quantum-enhanced approach utilizing quantum entanglement to mitigate these limitations, aiming to improve computational efficiency and stability. The core mechanism involves mapping the matrix elements to entangled states, enabling parallel processing and inherent error correction. This approach offers a promising path towards accelerating matrix operations and potentially revolutionizing certain computational tasks. The paper details the theoretical framework, initial implementation, and preliminary experimental results demonstrating the algorithmic benefits of quantum entanglement in matrix multiplication.