Geometry Transformation Quantum Computation Model

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the development of a novel geometric transformation quantum computation model, designed to dynamically adapt to the geometry of input data. Traditional quantum computation often employs static models, limiting the model's effectiveness in handling dynamic geometric transformations. Our proposed model leverages a quantum adaptation mechanism to continuously re-optimize the quantum state, resulting in efficient computation for geometric operations. We detail the model's architecture, the underlying quantum algorithms, and the benefits of its adaptive nature. The core claim focuses on the model's ability to efficiently compute geometric transformations without the limitations of static models.

DOI: 10.5281/zenodo.22265733

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