Title: Self-Organizing Quantum Field Networks (SOQFNs)

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum field networks, representing a fundamental element of quantum physics, offer potential for unprecedented computational capabilities. Traditional models often rely on predefined topologies, limiting their adaptability and requiring significant engineering effort. This paper explores the concept of Self-Organizing Quantum Field Networks (SOQFNs), a novel paradigm leveraging reinforcement learning to dynamically adjust the topology of these networks. The core claim is that we can design and implement a network of quantum fields that exhibits self-organizing behavior, creating a structure optimized for specific tasks through continuous adaptation. This is a significant departure from existing static quantum field models, offering a dynamic and adaptive framework with the potential for transformative applications.

DOI: 10.5281/zenodo.22186823

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