Quantum Network Information Gain Maximization

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper investigates the maximization of information gain within quantum networks, a critical challenge for realizing their full potential in secure communication and distributed quantum computing. We propose a novel optimization algorithm leveraging quantum information theory principles to dynamically manage quantum entanglement distribution, directly aiming to maximize the information gain achievable within the network. The core mechanism involves a dynamic adjustment of entanglement allocation based on the network's current state and the specific information demands. This approach represents a new paradigm for quantum network design and optimization, moving beyond static entanglement distribution strategies. The algorithm is formulated using concepts from quantum entropy, von Neumann entropy, and quantum mutual information. We present a theoretical framework and outline the key components of the algorithm, focusing on the mathematical formalism underlying the optimization process. The potential impact of this method is significant, offering a pathway to more efficient and robust quantum networks capable of handling complex information processing tasks. We define key performance metrics, such as the average information gain per communication round, and discuss the scalability challenges associated with implementing this approach. Further research will explore the practical implementation and performance validation of this algorithm.

DOI: 10.5281/zenodo.22338625

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