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.
翻译:量子力学的异国性质使机器学习(ML)在量子领域与古典应用不同。 ML可用于利用从量子系统中以广泛任务方式不断提取的信息进行知识发现。该模型接收流量子信息,用于学习和决策,从而导致对量子系统的即时反馈。作为一种流学方法,我们展示了从持续测量的qubit流数据中深入强化学习,在调整、减速和放松时进行。我们还调查了该物剂如何通过传输学习适应另一种量子噪音模式。流体学习为闭环量子控制提供了更好的理解,这可以为先进的量子技术铺平道路。