基于自适应的隐冲突的量子计算

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper introduces a novel quantum computing framework leveraging adaptive entanglement dynamics to enhance the efficiency of quantum computation. Traditional approaches to entanglement-based quantum algorithms often rely on static parameter settings, limiting scalability and requiring significant computational resources. We propose a dynamic adaptation strategy that intelligently adjusts entanglement parameters based on the input data, leading to a more efficient and robust quantum computation. This framework addresses limitations inherent in existing methods by dynamically optimizing the entanglement landscape, ultimately improving the performance of quantum algorithms. The proposed method demonstrates significant improvements in specific benchmark tasks, showcasing its potential for advancing the field of quantum computing.

DOI: 10.5281/zenodo.22152695

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