Information Entropy Quantum Computation

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Information entropy, a fundamental measure of uncertainty, plays a crucial role in various fields, including data analysis, cryptography, and machine learning. This paper explores the potential of utilizing quantum entanglement and information entropy to develop a novel computational model. We propose a quantum entanglement-based algorithm designed to efficiently compute complex probability distributions, offering a pathway toward deeper quantum computing applications. The core mechanism centers on leveraging the unique properties of quantum entanglement to accelerate calculations and facilitate the construction of advanced quantum machine learning models. This research investigates the feasibility of applying this approach to address challenges in traditional computational methods, specifically focusing on enhanced probabilistic computation and the exploration of novel quantum machine learning paradigms. We aim to demonstrate the potential of this approach to significantly improve computational efficiency and unlock new possibilities within quantum information processing.

DOI: 10.5281/zenodo.22596344

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