4.6 Article

Efficient flexible characterization of quantum processors with nested error models

Journal

NEW JOURNAL OF PHYSICS
Volume 23, Issue 9, Pages -

Publisher

IOP PUBLISHING LTD
DOI: 10.1088/1367-2630/ac20b9

Keywords

quantum characterization; model selection; QCVV

Funding

  1. US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research
  2. Laboratory Directed Research and Development program at Sandia National Laboratories
  3. US Department of Energy's National Nuclear Security Administration [DE-NA-0003525]

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The technique presented in the study iteratively tests a nested sequence of models to find a good error model for a quantum processor, while keeping track of the best-fit model and its wildcard error at each step. The characterization of a processor is constituted by each best-fit model and a quantification of its unmodeled error. Moreover, the technique allows for comparison of quantum processor models with experimental data and among themselves.
We present a simple and powerful technique for finding a good error model for a quantum processor. The technique iteratively tests a nested sequence of models against data obtained from the processor, and keeps track of the best-fit model and its wildcard error (a metric of the amount of unmodeled error) at each step. Each best-fit model, along with a quantification of its unmodeled error, constitutes a characterization of the processor. We explain how quantum processor models can be compared with experimental data and to each other. We demonstrate the technique by using it to characterize a simulated noisy two-qubit processor.

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