Quantum Entanglement-Based Self-Organizing Learning System

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper proposes a novel approach to quantum system control and optimization through the construction of a self-organizing learning system leveraging the principles of quantum entanglement. The core idea is to establish a feedback control mechanism utilizing entanglement's unique properties, enabling the system to autonomously adjust quantum bit (qubit) parameters based on observed states and desired objectives. This approach represents a new paradigm, moving beyond traditional, manually-tuned control methods towards a dynamically adaptive system. The system's self-organizing capability allows it to efficiently converge towards optimal states without explicit programming of control strategies. This work outlines the theoretical framework, key components, and potential applications of this entanglement-based self-organizing learning system. The system aims to achieve automated learning and optimization of quantum states, offering a promising direction for future quantum computing and information processing.

DOI: 10.5281/zenodo.22338641

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