Quantum-Tailored Machine-Learning Characterization of a Superconducting Qubit

Machine learning (ML) is a promising approach for performing challenging quantum-information tasks such as device characterization, calibration, and control. ML models can train directly on the data produced by a quantum device while remaining agnostic to the quantum nature of the learning task. However, these generic models lack physical interpretability and usually require large datasets in order to learn accurately. Here we incorporate features of quantum mechanics in the design of our ML approach to characterize the dynamics of a quantum device and learn device parameters. This physics-inspired approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data obtained from continuous weak measurement of a driven superconducting transmon qubit. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task, thus laying the groundwork for more scalable characterization techniques.

Automated summary

By incorporating features of quantum mechanics, the ML approach in this study outperformed generic models in characterizing the dynamics of a quantum device and learning device parameters. Leveraging domain knowledge improved the accuracy and efficiency of the characterization task significantly.