 We propose a Quantum Accelerated Reinforcement Learning, QRL framework that uses a Quantum Accelerator to speed up the decision-making process of a learning agent. The Quantum Accelerator encodes probability distributions, enabling the agent to make decisions based on the most likely outcome. The Quantum Accelerator is used in conjunction with a classical reinforcement learning algorithm, such as Q Learning, to improve the agent's performance. We demonstrate the effectiveness of our proposed QRL framework by comparing it against a classical version of Q Learning. Our results show that the Quantum Accelerator reduces the computational complexity of the decision-making process while maintaining similar accuracy compared to the classical version. This article was authored by Isania, a Giordano, and Low-Gullo, and others.