Quantum-tailored machine-learning architectures

Future quantum technologies are expected to be fundamentally challenging to characterize and calibrate. For this reason, a heuristic will most likely be required for this task, and machine learning provides an attractive framework for developing such a heuristic. To this end, we introduce a machine-learning architecture for inferring the dynamics of a quantum device from time-series measurement data. Our architecture is recurrent in nature and leverages quantum-mechanical structure in its design to interpret measurement data from complex quantum devices more efficiently. We investigate how the architectural structure influences the way we learn from data generated by quantum experiments and address applications of our techniques to the calibration and characterization of superconducting quantum devices.