基于非对称矩阵的自适应量子计算框架

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel quantum computing framework based on asymmetric matrix adaptation. The framework leverages a self-adaptive update mechanism for asymmetric matrices to facilitate rapid learning and adaptation of quantum computations, ultimately enhancing computational efficiency. We present a method for dynamically adjusting the parameters of the asymmetric matrix, enabling the quantum computer to respond effectively to the input data. This approach contrasts with traditional static frameworks, offering a more flexible and adaptive solution for complex quantum algorithms. The core mechanism focuses on iterative refinement of the asymmetric matrix using self-adaptive updates, leading to improved performance across a range of quantum computations. We demonstrate the effectiveness of this framework through a series of simulations and analysis, showcasing its potential to accelerate quantum algorithm development and enable more efficient quantum computations.

DOI: 10.5281/zenodo.22300026

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