基于动态矩阵的非线性矩阵的自适应加速算法

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel self-adaptive acceleration algorithm for non-linear matrix operations based on dynamic matrices. Traditional matrix computations rely on classical algorithms, while this approach leverages the inherent adaptability of dynamic matrices to significantly accelerate these calculations. The core mechanism involves introducing quantum entanglement within the matrix structure, enabling a substantial speedup in matrix operations. We explore the theoretical advantages and practical implementations of this algorithm, demonstrating its potential to revolutionize matrix computation. The paper presents a detailed analysis of the algorithm's design, optimization, and performance evaluation, highlighting its efficiency and scalability.

DOI: 10.5281/zenodo.22291057

Related papers

自适应矩阵运算的量子化

Jincheng Zhang

This paper introduces a novel quantum algorithm, termed "Adaptive Matrix Operation," designed to revolutionize quantum computation through the implementation of self-adaptive matrix operations. Traditional matrix operations are plagued by numerical precision limitations, hindering the scalability of quantum algorithms. Our approach leverages quantum entanglement and instantaneous correlation to dynamically adjust matrix structures and operations, effectively mitigating these issues and opening avenues for enhanced quantum performance. The core mechanism involves constructing a quantum state that evolves based on input data, allowing the algorithm to tailor its internal representation and execution to optimize the outcome. This paper details the theoretical framework, preliminary experimental results, and potential implications of this innovative quantum algorithm.

Sep 3, 20260 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, 20260 citations

自适应矩阵的非线性几何变换的量子加速计算

Jincheng Zhang

This paper explores the potential of adaptive matrix transformations to accelerate quantum computation, specifically focusing on the design of a novel quantum acceleration framework. Traditional quantum algorithms often struggle with complex geometric transformations, necessitating static approaches. We propose a new approach that leverages the inherent properties of quantum mechanics to dynamically adjust matrix operations, thereby significantly enhancing computational efficiency. The core mechanism revolves around the creation of a self-adapting matrix transformation process, allowing for efficient handling of non-linear geometric concepts within quantum circuits. This framework addresses the limitations of conventional algorithms by providing a flexible and potentially transformative path towards accelerating quantum calculations. The paper details the design of the framework, its mathematical underpinnings, and preliminary experimental validation demonstrating its efficacy in specific matrix operations. We present a case study demonstrating a significant improvement in computational complexity for a particular geometric transformation, highlighting the framework's potential for broader applicability in quantum computing.

Sep 6, 20260 citations

基于量子自适应矩阵的非线性运算

Jincheng Zhang

This paper presents a novel approach to non-linear operations based on quantum self-adapted matrices. Traditional methods often rely on classical computers, while quantum self-adapted matrices offer potential for enhanced computational efficiency. We develop a new algorithm utilizing these matrices to efficiently handle complex linear equation systems. The core mechanism involves optimizing the matrix transformations through quantum self-adaptation, leading to improved performance compared to existing techniques. This work explores the fundamental principles behind this approach and demonstrates its potential for tackling computationally challenging problems. The paper concludes with a discussion of the advantages and limitations of this method.

Aug 31, 20260 citations

基于量子自适应的矩阵的动态调整算法

Jincheng Zhang

This paper introduces a novel dynamic matrix adjustment algorithm based on quantum self-adaptation. Traditional matrix computations are often limited by classical algorithms, and this algorithm leverages quantum self-adaptation to dynamically adjust parameters during matrix operations, leading to enhanced computational efficiency and stability. The algorithm utilizes quantum entanglement and superposition to achieve this adaptation, significantly reducing computational complexity and improving the accuracy of matrix calculations. We detail the core mechanism, including the quantum self-adaptation process, the implementation of the dynamic adjustment algorithm, and a preliminary analysis of its performance. The paper concludes with a discussion of the potential applications and future research directions within this field.

Sep 4, 20260 citations

Title: Adaptive Non-Linear Matrix Operations for Quantum Algorithms (ANMQ)

Jincheng Zhang

This paper introduces the ANMQ, a novel quantum algorithm leveraging adaptive matrix operations to enhance the efficiency of large-scale matrix computations. Traditional quantum algorithms often employ static complexity, limiting performance in scenarios requiring high throughput. ANMQ dynamically adjusts the computational complexity of matrix operations, enabling efficient processing of massive datasets. The core mechanism involves integrating quantum algorithms with adaptive matrix operations, allowing for a flexible and optimized computation strategy. This approach addresses the challenges associated with scaling quantum computation to large matrices, offering a potentially transformative advancement in quantum algorithm design. The paper details the algorithm's architecture, demonstrates its efficacy through theoretical analysis and preliminary simulations, and explores potential applications across various quantum computing domains.

Sep 7, 20260 citations