- class LinearIQDiscriminator(cal_results, qubit_mask, expected_states=None, standardize=False, schedules=None, discriminator_parameters=None)¶
Linear discriminant analysis discriminator for IQ data.
qubit_mask (List[int]) – determines which qubit’s level 1 data to use in the discrimination process.
expected_states (List[str]) – a list that should have the same length as schedules. All results in cal_results are used if schedules is None. expected_states must have the corresponding length.
standardize (bool) – if true the discriminator will standardize the xdata using the internal method _scale_data.
schedules (Union[List[str], List[Schedule]]) – The schedules or a subset of schedules in cal_results used to train the discriminator. The user may also pass the name of the schedules instead of the schedules. If schedules is None, then all the schedules in cal_results are used.
discriminator_parameters (dict) – parameters for Sklearn’s LDA.
ImportError – If scikit-learn is not installed
- type result
Applies the discriminator to x_data.
Fits the discriminator using self._xdata and self._ydata.
Takes IQ data obtained from get_memory(), applies the qubit mask and formats the data as a list of lists.
Retrieves feature data (xdata) for the discriminator.
Retrieves the expected states (ydata) for the discriminator.
Identify if a name corresponds to a calibration name identified by the regex pattern self._cal_pattern.
Creates a plot of the data used to fit the discriminator.
Add the relevant IQ data from the Qiskit Result, or list thereof, to the given axes as a scatter plot.
Returns the expected states used to train the discriminator.
True if the discriminator has been fitted to calibration data.
Returns the schedules with which the discriminator was fitted.