Quantum Entanglement-Based Non-Linear Adaptive Geometry Reconstruction

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement offers a fundamentally different approach to geometric reconstruction, moving beyond the limitations of classical computation. This paper explores the design of a novel algorithm leveraging quantum entanglement to achieve accurate and dynamically adjustable geometric reconstructions. We propose a method centered around continuous parameter adjustment guided by the instantaneous correlation between entangled particles, enabling the algorithm to adapt to the inherent properties of the input data. The core claim is that this approach can surpass current geometric reconstruction techniques, particularly in handling complex datasets and achieving a level of precision unattainable through conventional methods.

DOI: 10.5281/zenodo.22197501

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