量子自组织算法的自适应几何优化

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum self-organization algorithms offer a novel approach to quantum computing, focusing on the adaptive optimization of quantum systems within local quantum entanglement fields. This paper explores the integration of quantum self-organization techniques with geometric optimization, aiming to enhance resource utilization and data compression efficiency. We propose a mechanism leveraging quantum entanglement to dynamically adjust quantum system geometry, reducing computational complexity. The core claim centers on the design of an algorithm that utilizes this adaptive geometry to achieve superior performance in quantum computation and data compression compared to traditional methods. This work presents a novel framework predicated on the inherent properties of quantum self-organization and geometric optimization, demonstrating the potential for significant advancements in quantum information processing.

DOI: 10.5281/zenodo.22324852

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