iqm.benchmarks.entanglement.graph_states.plot_max_negativities

iqm.benchmarks.entanglement.graph_states.plot_max_negativities#

iqm.benchmarks.entanglement.graph_states.plot_max_negativities(negativities: Dict[str, Dict[str, str | float]], backend_name: str, qubit_names: Dict[int, str], timestamp: str, tomography: Literal['shadow_tomography', 'state_tomography'], num_shots: int, num_bootstraps: int | None = None, num_RM_samples: int | None = None, num_MoMs_samples: int | None = None) → Tuple[str, Figure]#

Plots the maximum negativity for each corresponding pair of qubits.

Parameters:

  • negativities (Dict[str, Dict[str, str | float]]) – A dictionary (str qubit keys) of dictionaries (keys “value”/”uncertainty”) of negativities (float) to plot.

  • backend_name (str) – The name of the backend for the corresponding experiment.

  • qubit_names (Dict[int, str]) – A dictionary of qubit names corresponding to qubit indices.

  • timestamp (str) – The timestamp of the corresponding experiment.

  • tomography (Literal["shadow_tomography", "state_tomography"]) – The type of tomography that was used.

  • num_shots (int) – The number of shots used in the corresponding experiment.

  • num_bootstraps (Optional[int]) – The number of bootstraps used if tomography corresponds to state tomography. * Defaults to None if the tomography type is “shadow_tomography”.

  • num_RM_samples (Optional[int]) – The number of randomized measurement samples used if tomography corresponds to shadow tomography. * Defaults to None if the tomography type is “state_tomography”.

  • num_MoMs_samples (Optional[int]) – The number of Median of Means samples per randomized measurement used if tomography corresponds to shadow tomography. * Defaults to None if the tomography type is “shadow_tomography”.

Returns:

The figure label and the max negativities plot figure.

Return type:

Tuple[str, Figure]