Core Claim: Design a self-adaptive geometric transformation quantum algorithm for quantum computing, enabling geometric transformations within quantum computation, and improving quantum algorithm efficiency.

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper presents a novel quantum algorithm for geometric transformations, aiming to enhance the efficiency of quantum computations through automated optimization and transformation. The algorithm leverages the unique properties of quantum transformations to achieve adaptive optimization of geometric operations. We introduce a mechanism predicated on quantum transformations to dynamically adjust and optimize geometric transformations, thereby improving the performance of quantum algorithms. The research explores the potential of quantum transformations to address limitations of traditional quantum algorithms in geometric contexts. The core claim of this work is the design of a self-adaptive geometric transformation quantum algorithm, demonstrating significant improvements in quantum algorithm efficiency through a novel approach to geometric optimization. This work contributes to the advancement of quantum computation by providing a practical framework for adapting geometric transformations within quantum algorithms.

DOI: 10.5281/zenodo.22326848

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