Self-Adaptive Geometric Groups: Quantum Algorithm Design

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the design of quantum algorithms utilizing self-adaptive geometric groups. We investigate how these groups, leveraging quantum entanglement and geometric properties, can facilitate the development of efficient quantum algorithms. The core mechanism centers on dynamically adjusting the geometric structure of the group to optimize performance. The research focuses on establishing a novel approach to quantum algorithm optimization, emphasizing the synergistic interplay between self-adaptation and quantum mechanics. The paper presents a framework for designing quantum circuits that exhibit enhanced performance through this approach. Specifically, we detail a method for parameterizing the geometric group, enabling the algorithm to adapt to the input data. The work introduces a new optimization strategy based on entanglement and geometric transformations, ultimately aiming to improve the efficiency and robustness of quantum computations. The significance of this research lies in its potential to unlock new possibilities within the field of quantum algorithm design.

DOI: 10.5281/zenodo.22189019

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