Closed-loop control of a noisy qubit with reinforcement learning

• Main Authors: Chen, X. (Author), Ding, Y. (Author), Magdalena-Benedito, R. (Author), Martín-Guerrero, J.D (Author)
• Format: Article
• Language: English
• Published: Institute of Physics 2023
• Subjects: Closed loop control systems; Closed-loop; Closed-loop control; closed-loop quantum control; Closed-loop quantum control; Control theory; Decision making; Deep learning; deep reinforcement learning; Deep reinforcement learning; Learning systems; Machine learning applications; noisy qubit; Noisy qubit; Open loop control; Quantum control; Quantum noise; Quantum optics; Quantum system; Qubits; Reinforcement learning; Reinforcement learning method; Reinforcement learnings
• Online Access: View Fulltext in Publisher; View in Scopus

 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. © 2023 The Author(s). Published by IOP Publishing Ltd.