qblox_scheduler.analysis.calibration#

Module containing analysis utilities for calibration procedures.

In particular, manipulation of data and calibration points for qubit readout calibration.

Functions#

rotate_to_calibrated_axis(→ numpy.ndarray)

Rotates, normalizes and offsets complex valued data based on calibration points.

has_calibration_points(, indices_state_1)

Determine if dataset with S21 data has calibration points for 0 and 1 states.

Module Contents#

rotate_to_calibrated_axis(data: numpy.ndarray, ref_val_0: complex, ref_val_1: complex) → numpy.ndarray[source]#

Rotates, normalizes and offsets complex valued data based on calibration points.

Parameters:

  • data – An array of complex valued data points.

  • ref_val_0 – The reference value corresponding to the 0 state.

  • ref_val_1 – The reference value corresponding to the 1 state.

Returns:

: Calibrated array of complex data points.

has_calibration_points(s21: numpy.ndarray, indices_state_0: tuple = (-2,), indices_state_1: tuple = (-1,)) → bool[source]#

Determine if dataset with S21 data has calibration points for 0 and 1 states.

Three pieces of information are used to infer the presence of calibration points:

  • The angle of the calibration points with respect to the average of the datapoints,

  • The distance between the calibration points, and

  • The average distance to the line defined be the calibration points.

The detection is made robust by averaging 3 datapoints for each extremity of the “segment” described by the data on the IQ-plane.

See also

Calibration points autodetection

Parameters:

  • s21 – Array of complex datapoints corresponding to the experiment on the IQ plane.

  • indices_state_0 – Indices in the s21 array that correspond to the ground state.

  • indices_state_1 – Indices in the s21 array that correspond to the first excited state.

Returns:

: The inferred presence of calibration points.