Closed-loop control of a noisy qubit with reinforcement learning

The exotic nature of quantum mechanics differentiates machine learning applications in the quantum realm from classical ones. Stream learning is a powerful approach that can be applied to extract knowledge continuously from quantum systems in a wide range of tasks. In this paper, we propose a deep reinforcement learning method that uses streaming data from a continuously measured qubit in the presence of detuning, dephasing, and relaxation. The model receives streaming quantum information for learning and decision-making, providing instant feedback on the quantum system. We also explore the agent's adaptability to other quantum noise patterns through transfer learning. Our protocol offers insights into closed-loop quantum control, potentially advancing the development of quantum technologies.