Adaptive Matrix Quantumization Calculation Non-linear Smoothing

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel approach to quantum computation, specifically focusing on adaptive matrix quantumization. The core challenge in quantum computing lies in maintaining computational stability, particularly when dealing with matrix operations, which are inherently susceptible to noise and variations. Traditional smoothing techniques often lack the adaptability required to respond effectively to these dynamic changes. We propose a self-adaptive smoothing algorithm that dynamically adjusts the matrix's smoothness, minimizing error and enhancing stability. This method leverages a feedback loop to continuously refine the matrix's representation, ultimately improving the fidelity of quantum computations. The effectiveness of this approach is demonstrated through a series of rigorous simulations, showcasing improved accuracy and robustness compared to static smoothing methods. The research highlights the potential of adaptive smoothing to address key challenges in quantum computing and paves the way for more reliable and efficient quantum algorithms.

DOI: 10.5281/zenodo.22189099

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