Dynamic Entropy Quantum Computing

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

The current landscape of quantum computing faces significant challenges in terms of algorithm development and parameter optimization. Traditional methods often rely on manual design and iterative adjustments, leading to inefficiencies and limited performance. This paper introduces a novel approach to quantum computing parameter optimization leveraging the concept of dynamic information entropy. We propose a dynamic information entropy-based model that automatically adjusts quantum parameters to enhance computational efficiency and precision. This methodology offers a significant step forward by automating the optimization process, potentially unlocking new capabilities in quantum algorithms. The core claim highlights the development of a method that can dynamically adjust quantum parameters to achieve superior results. This research explores the potential of dynamically adjusting quantum parameters to overcome limitations inherent in traditional optimization techniques.

DOI: 10.5281/zenodo.22158191

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