Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Quantum Computing Algorithms and Architecture
Abstract
This paper explores the application of quantum self-organized networks (QSON) reinforcement learning, leveraging quantum entanglement and coherence to enhance learning strategies and improve the performance of reinforcement learning agents. Traditional reinforcement learning relies on classical computers, while this research introduces a novel approach utilizing the unique properties of QSON. We propose a framework that integrates quantum entanglement and self-consistency to optimize learning, thereby achieving superior results compared to existing methods. The core mechanism involves carefully structuring the neural network architecture and training process to exploit these quantum properties. This work presents a significant advancement in reinforcement learning, demonstrating the potential for quantum-enhanced learning through the application of QSON principles.
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