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Dimension Generalization and Quantum Entanglement: A Novel Framework for Geometry and Quantum Information

Jincheng Zhang

Published Sep 3, 2026• 0 citations
Publisher

Abstract

This paper explores the convergence of dimension generalization and quantum entanglement, proposing a novel framework for creating and manipulating high-dimensional spaces utilizing quantum entanglement. Traditional dimensionality reduction methods often struggle with the inherent complexities of high-dimensional data, while the potential of quantum entanglement offers a pathway to fundamentally alter geometric relationships. This work introduces a new mechanism, termed "dimension anchoring," that leverages entangled quantum states to dynamically adjust dimensional parameters, bypassing the need for direct manipulation of the underlying geometric structure. We demonstrate the framework's potential for generating novel geometric structures and, critically, for exploiting entanglement to enhance quantum information security. This research aims to provide a new perspective on dimensionality reduction, quantum computation, and secure communication by integrating these two powerful fields.

DOI
10.5281/zenodo.22267847

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This paper explores the potential of leveraging quantum entanglement to facilitate the precise simulation of quantum phenomena within high-dimensional spaces. We propose a novel dimensionality extension technique, termed "Quantum Entanglement Self-Adaptation," that utilizes the inherent properties of quantum entanglement to dynamically adjust the dimensionality of the simulation, thereby mitigating the inherent errors associated with traditional dimensionality reduction techniques. The core mechanism of this approach centers around establishing a self-adaptive entanglement structure that adjusts the simulation space's geometry in response to the quantum state, offering a pathway towards more accurate and robust simulations. The resulting framework addresses the limitations of current methods by providing a fundamentally different approach to dimensionality extension, potentially unlocking new capabilities in quantum simulation.
This paper explores the potential of quantum entanglement to revolutionize geometry simulation, specifically focusing on the development of a novel dimension generalization geometry model. Traditional methods struggle with the complexities of high-dimensional geometric transformations, offering a promising avenue for enhanced computational efficiency. This work proposes a framework leveraging quantum entanglement to accurately simulate geometric changes across different dimensions, utilizing entanglement's unique properties for accelerated computation. We outline the core mechanism, detailing how entanglement is integrated into the simulation process, and highlight the potential benefits of this approach in terms of geometric analysis and computational speed.
This paper introduces a novel dimension generalization algorithm leveraging quantum entanglement to facilitate complex operations within high-dimensional spaces. Traditional approaches often rely on specific quantum entanglement patterns, presenting limitations in scalability and operational complexity. Our proposed method proposes a system utilizing a "quantum-induced state" that allows for a more generalized approach to dimension expansion, bypassing the need for direct entanglement manipulation. This offers a potential pathway to reduce entanglement costs and enhance the algorithm's overall feasibility. We demonstrate the effectiveness of this approach through a series of carefully designed simulations, highlighting its potential to improve the computational efficiency and broaden the applicability of dimension generalization techniques. The core mechanism centers around a dynamically generated quantum state that effectively "quantizes" the dimension space, enabling novel manipulations without the inherent challenges associated with entanglement.
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