Adaptive Spin Matrix Decomposition

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper presents an innovative approach to quantum state decomposition, leveraging adaptive machine learning to dynamically adjust matrix decomposition parameters. Traditional methods often rely on manual parameter tuning, limiting scalability and precision. We introduce a novel algorithm that employs an adaptive neural network to learn and optimize these parameters, offering a significant advancement over existing techniques. The core mechanism centers around a self-adjusting learning process, resulting in improved accuracy and efficiency in handling high-dimensional quantum states. The paper details the algorithm's implementation, demonstrates its effectiveness through illustrative examples, and discusses its potential impact on quantum computing.

DOI: 10.5281/zenodo.22332165

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