Quantum Entanglement Information Encoding for Adaptive Reinforcement Learning

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum entanglement offers a fundamentally different approach to learning, potentially surpassing the limitations of classical reinforcement learning. This paper introduces a novel quantum-enhanced reinforcement learning algorithm leveraging entangled states for instantaneous information transfer. We propose a mechanism that utilizes a "hidden feedback" system, enabling gradients to propagate via entangled states, accelerating learning and promoting adaptive behavioral patterns. The core claim is that this approach enhances the learning efficiency and generalization capabilities of reinforcement learning compared to traditional methods. We explore the potential of this approach and present preliminary results demonstrating its effectiveness.

DOI: 10.5281/zenodo.22223223

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