Accelerated Randomized Benchmarking

Cassandra Granade1, 2, joint work with Christopher Ferrie3 and D. G. Cory1,4,5**

arXiv:1404.5275 SciRate

Presented 23 April, 2014 as a student seminar at IQC.

Slides: HTML IPython Notebook: download, view online


Producing useful quantum information devices requires efficiently assessing control of quantum systems, so that we can determine whether we have implemented a desired gate, and refine accordingly. Randomized benchmarking uses symmetry to reduce the difficulty of this task.

We bound the resources required for benchmarking and show that with prior information, orders of magnitude in accuracy can be obtained. We reach these accuracies with near-optimal resources, improving dramatically on curve fitting. Finally, we show that our approach is useful for physical devices by comparing to simulations.

Supplemental Material

view online download source

Software Resources

QInfer, a Python-language implementation the algorithms presented in this work, is available from GitHub.


Bibliography hosted on Zotero


  1. Institute for Quantum Computing, University of Waterloo.
  2. Department of Physics, University of Waterloo.
  3. Center for Quantum Information and Control, University of New Mexico.
  4. Department of Chemistry, University of Waterloo.
  5. Perimeter Institute for Theoretical Physics.