Randomised Benchmarking: Difference between revisions

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==Properties==
==Properties==
* This is a sub-technique of [[Fidelity Estimation]] technique.
* The noise model is assumed to be IID.
* The noise model is assumed to be IID.
* The figure of merit is average error rate, average gate fidelity.
* The figure of merit is average error rate, average fidelity of a noise quantum circuit.
* This method is a certification technique which has lower sample and resource complexity than [[Tomography]]  
* This method is a certification technique which has lower sample and resource complexity than [[Tomography]]


==Related Papers==
==Related Papers==

Revision as of 17:45, 16 September 2019

Functionality

Randomized benchmarking is the certification technique which falls under the Fidelity Estimation functionality. Fidelity is a measure of the "closeness" of two quantum states. It expresses the probability that one state will pass a test to identify as the other. The randomised benchmarking protocols are used to determine the error probability per gate in a computational context and also gives an overall average fidelity for the noise in the gate. The figure of merit in these protocols is the average gate fidelity and the average error rate. The computationally relevant errors are yielded in these protocols without relying on accurate quantum state preparation and measurement.

Protocols

Properties

  • The noise model is assumed to be IID.
  • The figure of merit is average error rate, average fidelity of a noise quantum circuit.
  • This method is a certification technique which has lower sample and resource complexity than Tomography

Related Papers

  • E.Knill et al (2007) arXiv:0707.0963: gate and time-independent noise model
  • E. Mageson et al (2011) arXiv:1009.3639: multi-parameter model
  • Magesan et al. PRL (2012): Interleaved Randomized Benchmarking
  • Harper et al (2016) arXiv:1608.02943v2: Interleaved Randomised Benchmarking to estimate fidelity of T gates
  • Wallman, Granade, Harper, F., NJP 2015: Purity benchmarking
*contributed by Rhea Parekh