Quantum Hidden Matrix Geometry Optimization and Topology

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Hidden Matrix Geometry Optimization and Topology addresses the challenge of optimizing quantum computation by employing a novel adaptive geometric optimization algorithm. This algorithm leverages the dynamic evolution of quantum states to iteratively refine the quantum state, ultimately aiming to enhance the efficiency and stability of quantum calculations. The core mechanism centers around dynamically adjusting the quantum potential field to guide the system towards a more optimal configuration. This approach distinguishes itself from traditional methods by offering a flexible and adaptive optimization strategy. The resulting optimization process contributes to improved computational performance and robustness. This paper presents a comprehensive investigation of the algorithm's design, implementation, and performance evaluation, highlighting its potential impact on quantum computing.

DOI: 10.5281/zenodo.22445511

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