Design and Analysis of Quantum Transfer Fractal Priority Replay and Mirdad Priority Loss Algorithms for Quantum Reinforcement Learning

The research explores the Quantum Transfer Fractal Priority Replay (QTFPR), an innovative algorithm aimed at improving quantum system performance using reinforcement learning techniques. By combining Quantum Q-learning with prioritization strategies, QTFPR demonstrates remarkable convergence and efficiency improvements. It utilizes Transfer Neural Networks and Fractal techniques to effectively prioritize experiences that are relevant to specific tasks within the quantum replay buffer. The proposed Mirdad Loss Priority (MLP) function, which incorporates quantum amplitude damping, outperforms traditional loss functions. The study highlights the practical implementation of QTFPR on real quantum hardware, such as the IBM Quantum Computer with Qiskit libraries, promising significant advancements in quantum machine learning efficiency. Various metrics, including priority, experience, total reward, average reward, and accuracy, are employed to evaluate the algorithm’s performance. QTFPR integrated with Quantum Q-learning, both of which demonstrate remarkable success in Atari games.