laboneq_applications.contrib.analysis.single_qubit_randomized_benchmarking

This module defines the analysis for single qubit randomized benchmarking experiment.

The experiment is defined in laboneq_applications.experiments.

In this analysis, we first interpret the raw data into qubit populations using principal component analysis or rotation and projection on the measured calibration states. Then we fit a exponential decay to the qubit population and extract the gate fidelity from the fit. Finally, we plot the data and the fit.

analysis_workflow(result, qubits, length_cliffords, variations, options=None)

The Time Rabi analysis Workflow.

The workflow consists of the following steps:

• calculate_qubit_population_rb
• fit_data
• plot_population

Parameters:

Name	Type	Description	Default
result	RunExperimentResults	The experiment results returned by the run_experiment task.	required
qubits	Qubits	The qubits on which to run the analysis. May be either a single qubit or a list of qubits. The UIDs of these qubits must exist in the result.	required
length_cliffords	list	list of numbers of Clifford gates to sweep	required
variations	int	Number of random seeds for RB.	required
options	TuneUpAnalysisWorkflowOptions | None	The options for building the workflow, passed as an instance of [TuneUpAnalysisWorkflowOptions]. See the docstring of this class for more details.	None

Returns:

Name	Type	Description
WorkflowBuilder	None	The builder for the analysis workflow.

calculate_qubit_population_rb(qubits, result, length_cliffords, variations, options=None)

Processes the raw data.

The data is processed in the following way:

• If calibration traces were used in the experiment, the raw data is rotated based on the calibration traces. See calibration_traces_rotation.py/rotate_data_to_cal_trace_results for more details.
• If no calibration traces were used in the experiment, or do_pca = True is passed in options, principal component analysis is performed on the data. See calibration_traces_rotation.py/principal_component_analysis for more details.

Parameters:

Name	Type	Description	Default
qubits	Qubits	The qubits on which the amplitude-Rabi experiments was run. May be either a single qubit or a list of qubits.	required
result	RunExperimentResults	the result of the experiment, returned by the run_experiment task.	required
length_cliffords	list	list of numbers of Clifford gates to sweep	required
variations	int	Number of random seeds for RB.	required
options	TuneupAnalysisOptions | None	The options for processing the raw data. See [TuneupAnalysisOptions], [TuneupExperimentOptions] and [BaseExperimentOptions] for accepted options. Overwrites the options from [TuneupAnalysisOptions], [TuneupExperimentOptions] and [BaseExperimentOptions].	None

Returns:

Type	Description
dict[str, dict[str, ArrayLike]]	dict with qubit UIDs as keys and the dictionary of processed data for each qubit
dict[str, dict[str, ArrayLike]]	as values. See [calibration_traces_rotation.py/calculate_population_1d] for what
dict[str, dict[str, ArrayLike]]	this dictionary looks like.

Raises:

Type	Description
TypeError	If result is not an instance of RunExperimentResults.

exponential_decay_fit(x, data, param_hints=None)

Performs a fit of an exponential-decay model to data.

Parameters:

Name	Type	Description	Default
data	ArrayLike	the data to be fitted	required
x	ArrayLike	the independent variable	required
param_hints	dict | None	dictionary of guesses for the fit parameters. See the lmfit docstring for details on the form of the parameter hints dictionary: https://lmfit.github.io/lmfit-py/model.html#lmfit.model.Model.set_param_hint	None

Returns:

Type	Description
ModelResult	The lmfit result

fit_data(qubits, processed_data_dict, options=None)

Perform a fit of an exponential-decay model to the qubit e-state population.

Parameters:

Name	Type	Description	Default
qubits	Qubits	The qubits on which to run the analysis. May be either a single qubit or a list of qubits. The UIDs of these qubits must exist in processed_data_dict	required
processed_data_dict	dict[str, dict[str, ArrayLike]]	the processed data dictionary returned by process_raw_data	required
options	TuneupAnalysisOptions | None	The options for processing the raw data. See [TuneupAnalysisOptions], [TuneupExperimentOptions] and [BaseExperimentOptions] for accepted options. Overwrites the options from [TuneupAnalysisOptions], [TuneupExperimentOptions] and [BaseExperimentOptions].	None

Returns:

Type	Description
dict[str, ModelResult]	dict with qubit UIDs as keys and the fit results for each qubit as keys.

plot_population(qubits, processed_data_dict, fit_results, options=None)

Create the time-Rabi plots.

Parameters:

Name	Type	Description	Default
qubits	Qubits	The qubits on which to run the analysis. May be either a single qubit or a list of qubits. The UIDs of these qubits must exist in the processed_data_dict and fit_results parameters.	required
processed_data_dict	dict[str, dict[str, ArrayLike]]	The processed data dictionary returned by process_raw_data.	required
fit_results	dict[str, ModelResult] | None	The fit-results dictionary returned by fit_data.	required
options	TuneupAnalysisOptions | None	The options for processing the raw data. See [TuneupAnalysisOptions], [TuneupExperimentOptions] and [BaseExperimentOptions] for accepted options. Overwrites the options from [TuneupAnalysisOptions], [TuneupExperimentOptions] and [BaseExperimentOptions].	None

Returns:

Type	Description
dict[str, Figure]	dict with qubit UIDs as keys and the figures for each qubit as values.