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Quantum Topology Information Encoding: An Adaptive Geometric Mapping Algorithm

Jincheng Zhang

Published Sep 1, 2026• 0 citations
Publisher

Abstract

Quantum topology information encoding provides a powerful framework for representing and manipulating complex geometric structures, offering unprecedented capabilities for numerical simulations. This paper introduces a novel algorithm, termed "Adaptive Geometric Mapping," that leverages quantum entanglement to achieve precise modeling and computation of intricate geometric forms. The core mechanism centers around a dynamically adjusted mapping process guided by quantum entanglement, significantly enhancing the accuracy and efficiency of numerical simulations. We propose a method for generating and manipulating geometric representations using entangled qubits, enabling the resolution of problems previously intractable for classical methods. The proposed algorithm demonstrates significant improvements in the simulation of topological structures, particularly in the context of quantum field theory and geometric analysis. Furthermore, the paper discusses the theoretical underpinnings of the adaptive mapping process and outlines future research directions, including exploring the potential for scaling this approach to larger, more complex geometries.

DOI
10.5281/zenodo.22223778

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Quantum topology information encoding offers a novel approach to data manipulation and optimization by leveraging the unique properties of quantum mechanics. This paper introduces an adaptive mapping algorithm based on quantum topology information encoding, capable of dynamically adjusting the encoding of quantum entangled states to precisely transform and optimize complex data structures. We demonstrate the algorithm's effectiveness through a series of simulations and analyses, showcasing its ability to achieve significant improvements in data transformation tasks. The core mechanism centers on the manipulation of quantum entanglement, providing a robust and adaptable framework for data processing. This research highlights the potential of quantum topology information encoding as a powerful tool for advanced data manipulation and optimization.
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