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Nonlinear Quantum Adaptive Geometry

Jincheng Zhang

Published Sep 6, 2026• 0 citations
Publisher

Abstract

This paper explores the application of nonlinear quantum self-adaptation to geometric topology, focusing on the generation of quantum states with specific distributions. We propose a novel algorithm that dynamically adjusts the geometric properties of random matrices based on input data, leading to the creation of quantum states exhibiting tailored characteristics. The core mechanism leverages the inherent properties of geometric topology to enhance the generative process, offering a significant advancement over traditional random matrix methods. This work demonstrates the potential for this approach to unlock new algorithms in image processing and machine learning.

DOI
10.5281/zenodo.22445072

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