iqm.benchmarks.entanglement.graph_states.plot_max_negativities_graph

iqm.benchmarks.entanglement.graph_states.plot_max_negativities_graph#

iqm.benchmarks.entanglement.graph_states.plot_max_negativities_graph(negativities: dict[str, dict[str, str | float]], backend_coupling_map: CouplingMap, qubit_names: dict[int, str], timestamp: str, tomography: TomographyType, station: str | None = None, num_shots: int | None = None, 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 in a graph layout of the given backend.

Parameters:

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

  • backend_coupling_map (CouplingMap) – The CouplingMap instance.

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

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

  • tomography (TomographyType) – The type of tomography that was used.

  • station (str | None) – The name of the station to use for the graph layout.

  • num_shots (int | None) – The number of shots used in the corresponding experiment.

  • num_bootstraps (int | None) – The number of bootstraps used if tomography corresponds to state tomography.

  • num_RM_samples (int | None) – The number of randomized measurement samples used if tomography corresponds to shadow tomography.

  • num_MoMs_samples (int | None) – The number of Median of Means samples per randomized measurement used if tomography corresponds to shadow tomography.

Returns:

The figure label and the max negativities plot figure.

Return type:

tuple[str, Figure]

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