analysis
Classes:
| Name | Description |
|---|---|
OptimalRO2not2AmplitudeQubitAnalysis |
|
OptimalROAmplitudeQubitAnalysis |
|
ROThreeStateAmplitudeQubitAnalysis |
|
Bases: OptimalROAmplitudeQubitAnalysis
Methods:
| Name | Description |
|---|---|
IQ |
Extracts I/Q components from the dataset at a given index. |
process_qubit |
Setup the qubit data and analyze it. |
run_initial_fitting |
Classify all iq points for all amplitudes and store them in |
Setup the qubit data and analyze it. Args: dataset: xarray dataset with the qubit data qubit_element: name of the qubit element Returns: QOI: Quantity of interest as QOI wrapped object
Bases: BaseQubitAnalysis
Methods:
| Name | Description |
|---|---|
IQ |
Extracts I/Q components from the dataset at a given index. |
analyse_qubit |
Run the actual analysis function |
plotter |
Plot the fitted values from the analysis |
process_qubit |
Setup the qubit data and analyze it. |
run_initial_fitting |
Classify all iq points for all amplitudes and store them in |
abstractmethod
Run the actual analysis function
Returns:
| Type | Description |
|---|---|
QOI
|
The quantity of interest as QOI wrapped object |
abstractmethod
Plot the fitted values from the analysis
Returns:
| Name | Type | Description |
|---|---|---|
None |
None
|
This will just plot the fitted values |
Setup the qubit data and analyze it. Args: dataset: xarray dataset with the qubit data qubit_element: name of the qubit element Returns: QOI: Quantity of interest as QOI wrapped object
Bases: OptimalROAmplitudeQubitAnalysis
Methods:
| Name | Description |
|---|---|
IQ |
Extracts I/Q components from the dataset at a given index. |
analyse_qubit |
classify the three states for each RO amplitude |
process_qubit |
Setup the qubit data and analyze it. |
run_initial_fitting |
Classify all iq points for all amplitudes and store them in |
three_state_classification |
Classify all iq points for all amplitudes and store them in |
classify the three states for each RO amplitude and return the RO amplitude that gives the maximum three state classification fidelity as well as the defining parameters for the optimal three state boundary returns
optimal_amplitude: float amplitude of the RO pulse that gives optimal fidelity centroid_I: float I coordinate of the centroid defined by the class boundaries centroid_Q: float Q coordinate of the centroid defined by the class boundaries omega_01: float \in [0,360) degrees defining angle for the |0> - |1> boundary omega_12: float \in [0,360) degrees defining angle for the |1> - |2> boundary omega_20 \in [0,360) degrees, defining angle for the |2> - |0> boundary inv_cm_str: str string encoding of the confusion matrix
Setup the qubit data and analyze it. Args: dataset: xarray dataset with the qubit data qubit_element: name of the qubit element Returns: QOI: Quantity of interest as QOI wrapped object
Classify all iq points for all amplitudes and store them in corresponding dataArrays.