  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.
