forest.benchmarking.direct_fidelity_estimation.acquire_dfe_data(qc: pyquil.api._quantum_computer.QuantumComputer, expt: forest.benchmarking.observable_estimation.ObservablesExperiment, num_shots: int = 10000, active_reset: bool = False, mitigate_readout_errors: bool = True, show_progress_bar: bool = False) → List[forest.benchmarking.observable_estimation.ExperimentResult]

Acquire data necessary for direct fidelity estimate (DFE).

  • qc – A quantum computer object where the experiment will run.
  • expt – An ObservablesExperiment object describing the experiments to be run.
  • num_shots – The number of shots to be taken in each experiment. If mitigate_readout_errors is set to True then this same number of shots will be used for each round of symmetrized data collection and each calibration of an observable.
  • active_reset – Boolean flag indicating whether experiments should begin with an active reset instruction (this can make the collection of experiments run a lot faster).
  • mitigate_readout_errors – Boolean flag indicating whether bias due to imperfect readout should be corrected
  • show_progress_bar – displays a progress bar via tqdm if true.

results from running the given DFE experiment. These can be passed to estimate_dfe