Quantum State Mapping and High-Dimensional Geometry-Adaptive Algorithms

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum state mapping represents a paradigm shift in computational geometry, offering the potential to model complex geometric structures with unprecedented precision and efficiency. This paper investigates the application of non-linear mapping techniques to quantum states, exploring their utility in high-dimensional geometric modeling and optimization. We demonstrate how these mappings can generate more robust geometric representations and significantly enhance computational performance. The core mechanism centers around the creation of a mapping that transforms a high-dimensional quantum state into a lower-dimensional geometric space, simplifying geometric analysis and enabling novel optimization strategies. This research proposes a new algorithmic framework that leverages non-linear mappings to construct increasingly sophisticated geometric models, ultimately pushing the boundaries of geometric computation.

DOI: 10.5281/zenodo.22296672

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