Work

Closed-loop control of a noisy qubit with reinforcement learning

Year: 2023

Type: article

Abstract: 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.

Source: Machine Learning Science and Technology

DOI: 10.1088/2632-2153/acd048

Authors: Yongcheng Ding, Xi Chen, Rafael Magdalena-Benedito, José D Martín-Guerrero

Institutions: University of the Basque Country, Universitat de València

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,405

Funders: European Commission, National Natural Science Foundation of China, Eusko Jaurlaritza, Science and Technology Commission of Shanghai Municipality