forest.benchmarking.tomography.estimate_variance(results: List[forest.benchmarking.observable_estimation.ExperimentResult], qubits: List[int], tomo_estimator: Callable, functional: Callable, target_state=None, n_resamples: int = 40, project_to_physical: bool = False) → Tuple[float, float]

Use a simple bootstrap-like method to return an error bar on some functional of the quantum state.

  • results – Measured results from a state tomography experiment
  • qubits – Qubits that were tomographized.
  • tomo_estimator – takes in results, qubits and returns a corresponding estimate of the state rho, e.g. linear_inv_state_estimate
  • functional – Which functional to find variance, e.g. dm.purity.
  • target_state – A density matrix of the state with respect to which the distance functional is measured. Not applicable if functional is dm.purity.
  • n_resamples – The number of times to re-sample.
  • project_to_physical – Whether to project the estimated state to a physical one with project_state_matrix_to_physical().