analysis

tergite_autocalibration.lib.nodes.characterization.randomized_benchmarking.analysis

Classes:

Name	Description
RandomizedBenchmarkingSSROQubitAnalysis	Analysis that fits an exponential decay function to randomized benchmarking data.

RandomizedBenchmarkingSSROQubitAnalysis

RandomizedBenchmarkingSSROQubitAnalysis(name, redis_fields)

Bases: BaseQubitAnalysis

Analysis that fits an exponential decay function to randomized benchmarking data.

Methods:

Name	Description
plot	Plot the fitted values from the analysis
process_qubit	Setup the qubit data and analyze it.
rotate_to_probability_axis	Rotates the S21 IQ points to the real - normalized axis

plot

plot(primary_axis)

Plot the fitted values from the analysis Args: primary_axis: The axis object from matplotlib to be plotted Returns: None, will just plot the fitted values

process_qubit

process_qubit(dataset, qubit_element) -> QOI

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

rotate_to_probability_axis

rotate_to_probability_axis(complex_measurement_data)

Rotates the S21 IQ points to the real - normalized axis that describes the |0> - |1> axis. !!! It Assumes that complex_measurement_data[-2] corresponds to the |0> and complex_measurement_data[-1] corresponds to the |1>