Update tutorial to show example code

docs/manuals/characterization/stark_experiment.rst

@@ -211,6 +211,111 @@ In Qiskit Experiments, the experiment option ``stark_amp`` usually refers to
 the height of this GaussianSquare flat-top.
 
 
+Workflow
+--------
+
+In this example, you'll learn how to measure a spectrum of qubit relaxation property
+with fixed frequency transmons.
+As you already know, we give an offset to the qubit frequency with a Stark tone,
+and the workflow starts from characterizing the amount of the Stark shift against
+the Stark amplitude :math:`\bar{\Omega}` that you can experimentally control.
+
+.. jupyter-input::
+
+    from qiskit_experiments.library.driven_freq_tuning import StarkRamseyXYAmpScan
+
+    exp = StarkRamseyXYAmpScan((0,), backend=backend)
+    exp_data = exp.run().block_for_results()
+    coefficients = exp_data.analysis_results("stark_coefficients").value
+
+You first need to run the :class:`.StarkRamseyXYAmpScan` experiment that scans :math:`\bar{\Omega}`
+and estimates the amount of the resultant frequency shift.
+This experiment fits the frequency shift to a polynomial model which is a function of :math:`\bar{\Omega}`.
+You can obtain the :class:`.StarkCoefficients` object that contains
+all polynomial coefficients to map and reverse-map the :math:`\bar{\Omega}` to corresponding frequency value.
+
+
+This object may be necessary for the following spectroscopy experiment.
+Since Stark coefficients are stable for a relatively long time,
+you may want to save the coefficient values and load them later when you run the experiment.
+If you have an access to the Experiment service, you can just save the experiment result.
+
+.. jupyter-input::
+
+    exp_data.save()
+
+.. jupyter-output::
+
+    You can view the experiment online at https://quantum.ibm.com/experiments/23095777-be28-4036-9c98-89d3a915b820
+
+
+Otherwise, you can dump the coefficient object into a file with JSON format.
+
+.. jupyter-input::
+
+    import json
+    from qiskit_experiments.framework import ExperimentEncoder
+
+    with open("coefficients.json", "w") as fp:
+        json.dump(ret_coeffs, fp, cls=ExperimentEncoder)
+
+The saved object can be retrieved either from the service or file, as follows.
+
+.. jupyter-input::
+
+    # When you have access to Experiment service
+    from qiskit_experiments.library.driven_freq_tuning import retrieve_coefficients_from_backend
+
+    coefficients = retrieve_coefficients_from_backend(backend, (0,))
+
+    # Alternatively you can load from file
+    from qiskit_experiments.framework import ExperimentDecoder
+
+    with open("coefficients.json", "r") as fp:
+        coefficients = json.load(fp, cls=ExperimentDecoder)
+
+Now you can measure the spectrum of qubit relaxation property.
+The :class:`.StarkP1Spectroscopy` experiment also scans :math:`\bar{\Omega}`,
+but instead of measuring the frequency shift, it measures the excited state population P1
+after certain delay, :code:`t1_delay` in the experiment options, following the state population.
+You can scan the :math:`\bar{\Omega}` values either in the "frequency" or "amplitude" domain,
+but the :code:`stark_coefficients` options must be set when you prefer the frequency sweep.
+
+.. jupyter-input::
+
+    from qiskit_experiments.library.driven_freq_tuning import StarkP1Spectroscopy
+
+    exp = StarkP1Spectroscopy((0,), backend=backend)
+
+    exp.set_experiment_options(
+        t1_delay=20e-6,
+        min_xval=-20e6,
+        max_xval=20e6,
+        xval_type="frequency",
+        spacing="linear",
+        stark_coefficients=coefficients,
+    )
+
+    exp_data = exp.run().block_for_results()
+
+You may find notches in the P1 spectrum, which may indicate the existence of TLS
+in the vicinity of your qubit drive frequency.
+
+.. jupyter-input::
+
+    exp_data.figure(0)
+
+.. image:: ./stark_experiment_example.png
+
+Note that this experiment doesn't yield any analysis result because a landscape of P1 spectrum
+is hardly predicted due to random occurrence of the TLS or frequency collision.
+If you have own protocol to extract meaningful quantities from the data,
+you can write a custom analysis class and give it to the experiment instance before execution.
+See :class:`.StarkP1SpectAnalysis` for more details.
+
+This protocol can be parallelized among many qubits unless crosstalk matters.
+
+
 References
 ----------
 

qiskit_experiments/library/driven_freq_tuning/p1_spect_analysis.py

@@ -35,8 +35,10 @@ class StarkP1SpectAnalysis(BaseAnalysis):
         lossy TLS notches, and hence this analysis doesn't provide any
         generic mathematical model to fit the measurement data.
         A developer may subclass this to conduct own analysis.
+        The :meth:`StarkP1SpectAnalysis._run_spect_analysis` is a hook method where
+        you can define a custom analysis protocol.
 
-        This analysis just visualizes the measured P1 values against Stark tone amplitudes.
+        By default, this analysis just visualizes the measured P1 values against Stark tone amplitudes.
         The tone amplitudes can be converted into the amount of Stark shift
         when the calibrated coefficients are provided in the analysis option,
         or the calibration experiment results are available in the result database.