  The exotic nature of quantum mechanics makes machine learning (ML) be
different in the quantum realm compared to classical applications. ML can be
used for knowledge discovery using information continuously extracted from a
quantum system in a broad range of tasks. The model receives streaming quantum
information for learning and decision-making, resulting in instant feedback on
the quantum system. As a stream learning approach, we present a deep
reinforcement learning on streaming data from a continuously measured qubit at
the presence of detuning, dephasing, and relaxation. We also investigate how
the agent adapts to another quantum noise pattern by transfer learning. Stream
learning provides a better understanding of closed-loop quantum control, which
may pave the way for advanced quantum technologies.
