Self-Adaptive Quantum Information Entropy Optimization Algorithm

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum information entropy is a critical parameter in quantum computation, and its optimization is essential for improving quantum algorithm efficiency. This paper presents a novel self-adaptive quantum information entropy optimization algorithm designed to dynamically adjust the entropy of quantum information, thereby enhancing quantum computation's performance. The algorithm leverages the dynamic evolution of the entropy and employs sophisticated optimization techniques to maximize the utility of quantum information. This research addresses a long-standing challenge in quantum computing – the need for robust and adaptable entropy control – and proposes a promising approach to achieve this goal. This work demonstrates the effectiveness of the proposed algorithm through theoretical analysis and preliminary experimental validation, showcasing its potential for significant advancements in quantum information processing.

DOI: 10.5281/zenodo.22266678

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