Self-Adaptive Quantum Hiding Matrix Optimization Algorithm

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel optimization algorithm based on self-adaptive quantum hiding matrices. The algorithm leverages the unique properties of quantum hiding matrices to automatically adjust their parameters, enabling high-precision and efficient quantum computation. Traditional quantum algorithms often rely on manual parameter tuning, which can be inefficient and limit algorithm scalability. This proposed algorithm offers a significant advancement by incorporating dynamic parameter adjustment, directly addressing these limitations. We present a theoretical framework and experimental validation demonstrating the algorithm's effectiveness in optimizing quantum circuits and achieving superior performance compared to existing methods. The core mechanism centers around the algorithm's ability to dynamically modify the hiding matrix based on the quantum state, facilitating optimal parameter adjustments.

DOI: 10.5281/zenodo.22196221

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