Quantum Stream Learning
This work addresses closed-loop quantum control for researchers in quantum computing, but it appears incremental as it applies existing methods to a new quantum data context.
The paper tackled the problem of machine learning with streaming quantum data by applying deep reinforcement learning to a continuously measured qubit under noise conditions like detuning and dephasing, resulting in instant feedback for quantum control. It also explored transfer learning to adapt to different noise patterns, potentially advancing quantum technologies.
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.