Matrix Self-Association and Quantum Computing: A Deep Dive Utilizing Dynamic Topology

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper explores the intersection of matrix self-association and quantum computing, focusing on the design of a novel dynamic topology algorithm for deep quantum applications. We present a theoretical framework centered around the concept of self-associated matrices, demonstrating how these matrices can be leveraged to optimize quantum state evolution and facilitate complex quantum algorithms. The core mechanism involves constructing a dynamic topology algorithm that iteratively adjusts the quantum state based on the self-associated matrix, enabling the exploration of novel quantum states and the potential for enhanced computational power. This research proposes a paradigm shift in quantum algorithm design, moving beyond static approaches and embracing the dynamic nature of quantum systems. The paper details the theoretical foundation, algorithm implementation, and initial experimental explorations demonstrating the potential of this approach. Furthermore, it addresses the challenges and future directions of this research, emphasizing the benefits of dynamic topology for advancing quantum computing.

DOI: 10.5281/zenodo.22299106

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