Machine Learning for Discriminating Quantum Measurement Trajectories and Improving Readout.

Autor:	Magesan E; IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, USA., Gambetta JM; IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, USA., Córcoles AD; IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, USA., Chow JM; IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, USA.
Jazyk:	angličtina
Zdroj:	Physical review letters [Phys Rev Lett] 2015 May 22; Vol. 114 (20), pp. 200501. Date of Electronic Publication: 2015 May 18.
DOI:	10.1103/PhysRevLett.114.200501
Abstrakt:	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.
Databáze:	MEDLINE
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