Title: Adaptive Quantum Hiding Matrix Computation Framework

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces an adaptive quantum hiding matrix computation framework designed to address the limitations of traditional methods in handling complex data. The core innovation lies in the ability to dynamically adjust the parameters of a hidden matrix, enabling precise computations and optimizations across a range of data types. We present a novel algorithm that leverages a self-tuning parameter adjustment mechanism, demonstrating significant improvements in accuracy and computational efficiency compared to existing approaches. The framework's adaptability is crucial for applications requiring fine-grained data manipulation and optimization. The paper details the framework's architecture, the key algorithm, and preliminary results showcasing its effectiveness.

DOI: 10.5281/zenodo.22299371

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