Sergey Bravyi, Andrew W. Cross, et al.
Nature
Current methods for classifying measurement trajectories in superconducting qubit systems produce fidelities systematically lower than those predicted by experimental parameters. Here, we place current classification methods within the framework of machine learning (ML) algorithms and improve on them by investigating more sophisticated ML approaches. We find that nonlinear algorithms and clustering methods produce significantly higher assignment fidelities that help close the gap to the fidelity possible under ideal noise conditions. Clustering methods group trajectories into natural subsets within the data, which allows for the diagnosis of systematic errors. We find large clusters in the data associated with T1 processes and show these are the main source of discrepancy between our experimental and ideal fidelities. These error diagnosis techniques help provide a path forward to improve qubit measurements.
Sergey Bravyi, Andrew W. Cross, et al.
Nature
Nicholas T. Bronn, Baleegh Abdo, et al.
WOLTE 2016
Sergey Bravyi, Oliver Dial, et al.
Journal of Applied Physics
Doug T McClure, Hanhee Paik, et al.
Physical Review Applied