基于高阶矩阵的动态量子态的量子计算

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel quantum computing approach based on dynamic quantum states, leveraging the self-adaptive properties of high-rank matrices. We propose a quantum computing framework that utilizes the inherent ability of high-rank matrices to dynamically adjust quantum states, thereby enhancing the efficiency and precision of quantum computations. This design focuses on mitigating the limitations of traditional quantum algorithms and opens possibilities for more complex and robust quantum systems. The core mechanism involves constructing a quantum computing framework that adapts to the state, enabling efficient and accurate quantum processing. This paper explores the advantages and potential challenges of this approach, emphasizing the role of high-rank matrices in driving quantum performance.

DOI: 10.5281/zenodo.22205091

Related papers

基于自组织矩阵的动态量子算法

Jincheng Zhang

This paper presents a novel dynamic quantum algorithm framework based on self-organizing matrices. The core idea is to dynamically adjust quantum algorithm parameters through the inherent properties of these matrices, enabling automated optimization and enhanced performance. We detail the framework's architecture, the mechanisms driving parameter adjustments, and the advantages it offers over traditional approaches. The paper explores the potential of this approach to achieve more efficient and scalable quantum computations. The research focuses on the practical implications of this design, emphasizing its ability to adapt to a wider range of quantum systems and problems.

Sep 1, 20260 citations

基于量子纠缠的拓扑几何的自适应计算框架

Jincheng Zhang

Quantum entanglement, a cornerstone of quantum mechanics, presents a unique opportunity for computation beyond classical paradigms. This paper explores the application of quantum entanglement to develop a novel self-adaptive computing framework, leveraging the inherent properties of entangled states to dynamically adjust computational parameters within complex systems. We propose a method for establishing a dynamic mapping between the computational process and the entangled state parameters, enabling efficient optimization of systems exhibiting high complexity and non-linearity. The core claim centers on the ability of this framework to overcome the limitations of traditional computational methods through the utilization of quantum entanglement's self-adaptive nature. This research investigates the potential of such a framework to facilitate the design and execution of algorithms capable of achieving performance gains in challenging scenarios.

Sep 2, 20260 citations

自适应量子态的非线性量子纠缠计算

Jincheng Zhang

This paper introduces a novel framework for quantum state manipulation, specifically focused on the dynamic adjustment of quantum state to enhance computational accuracy. Traditional approaches to quantum state computation often rely on fixed, static parameters, limiting their adaptability to complex input data. We propose a system based on self-adaptive entanglement adjustment, which dynamically adjusts the entanglement between qubits to achieve improved precision in calculations. The core mechanism leverages a feedback loop to optimize the entanglement state, mitigating the limitations of static parameterization. This research explores the potential of this approach to tackle challenges in quantum computation, particularly in scenarios involving dynamic data and intricate entanglement structures. The paper details the theoretical foundation, implementation architecture, and preliminary results demonstrating the effectiveness of this self-adaptive system. The key contribution is a novel algorithm for self-adaptation and a detailed analysis of its impact on computational performance. We believe this framework represents a significant step towards more robust and adaptable quantum computation.

Aug 31, 20260 citations

基于自适应的量子计算的拓扑结构优化

Jincheng Zhang

Quantum computing offers the potential for revolutionary computational capabilities, but realizing this potential requires careful design of quantum circuits and algorithms. Quantum state optimization is a crucial step in this process, and traditional optimization methods often struggle with the inherent complexity of quantum systems. This paper presents a novel approach to quantum state optimization utilizing adaptive quantum state transformations, leveraging the self-adaptability of quantum states to achieve enhanced computational efficiency and precision. We detail a method that dynamically adjusts the quantum state topology to address specific challenges, resulting in improved performance compared to existing static optimization techniques. The core mechanism focuses on exploiting the quantum state's ability to evolve and adapt to changing conditions, thereby enabling the discovery of optimal quantum circuits. This work addresses the limitations of conventional optimization approaches by introducing a dynamic, self-adjusting process that directly interacts with the quantum system's inherent properties. The proposed method provides a promising path toward accelerating quantum algorithm development and enhancing the performance of various quantum computations.

Aug 29, 20260 citations

Quantum State Adaptive Computation Paradigm

Jincheng Zhang

This paper introduces a novel quantum state adaptive computation paradigm aimed at enhancing the efficiency of complex calculations. Traditional computing approaches often require manual parameter adjustments, limiting adaptability to intricate environments. We propose a quantum state-based adaptive computation framework leveraging the inherent self-adaptation properties of quantum states. This framework utilizes quantum transformations and control mechanisms to optimize computational processes, achieving improved performance in diverse scenarios. The core mechanism focuses on dynamically adjusting quantum states to achieve higher fidelity and throughput, overcoming the limitations of conventional methods. This research explores the potential of quantum states to significantly accelerate complex computations.

Aug 31, 20260 citations

High-Order Quantum Entanglement Computing Framework

Jincheng Zhang

This paper presents a novel high-order quantum entanglement computing framework designed to enhance computational efficiency and precision. The framework leverages advanced quantum entanglement to enable adaptive quantum algorithms, dynamically adjusting parameters to optimize performance. We explore the architectural design, core mechanisms, and potential benefits of this approach. The research focuses on overcoming limitations inherent in traditional quantum computing approaches, particularly concerning the ability to precisely control and manipulate entangled states. We demonstrate the framework's efficacy through theoretical analysis and preliminary experimental results, showcasing its potential to significantly improve the performance of complex quantum algorithms.

Aug 31, 20260 citations