基于动态自适应的几何群的量子计算

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the potential of dynamically adaptive geometric group quantum computation to enhance quantum computing capabilities. Traditional quantum computation relies heavily on fixed qubit architectures, limiting scalability and complexity. We propose a novel framework leveraging dynamic self-adaptation within geometric group quantum algorithms, aiming to overcome these limitations. This framework integrates quantum entanglement and quantum computation principles to achieve improved performance. The core mechanism involves a carefully orchestrated evolution of the geometric group's parameters, dynamically adjusting its properties to optimize quantum state manipulation and information processing. We demonstrate a preliminary implementation of this framework, showcasing its potential for enhanced quantum algorithms and a more robust approach to quantum computation. The resulting analysis highlights the significant advantages offered by this dynamically adaptive approach.

DOI: 10.5281/zenodo.22188934

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