Quantum Machine Learning for Adaptive Reinforcement Learning

Abstract

Quantum Machine Learning (QML) offers a paradigm shift in computational power, potentially enabling breakthroughs across diverse fields. This paper investigates the application of QML to reinforcement learning, specifically focusing on designing a novel framework that leverages dynamic quantum fields to enhance learning efficiency and model generalization. We propose a mechanism utilizing "dynamic quantum fields" to dynamically adjust the reinforcement learning landscape, improving performance compared to traditional approaches. The core claim is that this framework achieves superior results through optimized exploration and exploitation, leading to more efficient and robust learning.