Adaptive Matrix Decomposition Quantum Computing Model

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel approach to quantum computing based on adaptive matrix decomposition, aiming to enhance the efficiency of quantum computations. Traditional quantum algorithms often necessitate manual parameter adjustment, hindering efficiency and increasing complexity. Our model dynamically adjusts quantum parameters based on the outcome of a computation, automating the process and offering a significant improvement over existing techniques. We present a detailed theoretical analysis and illustrative examples demonstrating the model's effectiveness in various quantum algorithms. This research addresses the need for automated parameter optimization, potentially revolutionizing the design and implementation of quantum algorithms.

DOI: 10.5281/zenodo.22589061

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