Add fit model to curve analysis

qiskit_experiments/curve_analysis/curve_analysis.py

@@ -1152,6 +1152,11 @@ def _run_analysis(
             # pylint: disable=assignment-from-none
             quality = self._evaluate_quality(fit_data=fit_result)
 
+            fit_models = {
+                series_def.name: series_def.model_description or "no description"
+                for series_def in self.__series__
+            }
+
             # overview entry
             analysis_results.append(
                 AnalysisResultData(
@@ -1163,6 +1168,7 @@ def _run_analysis(
                         "popt_keys": fit_result.popt_keys,
                         "dof": fit_result.dof,
                         "covariance_mat": fit_result.pcov,
+                        "fit_models": fit_models,
                     },
                 )
             )

qiskit_experiments/curve_analysis/curve_data.py

@@ -43,6 +43,9 @@ class SeriesDef:
     # Whether to plot fit uncertainty for this line.
     plot_fit_uncertainty: bool = False
 
+    # Latex description of this fit model
+    model_description: Optional[str] = None
+
 
 @dataclasses.dataclass(frozen=True)
 class CurveData:

qiskit_experiments/curve_analysis/fit_function.py

@@ -45,7 +45,7 @@ def sin(
     .. math::
         y = {\rm amp} \sin\left(2 \pi {\fm freq} x + {\rm phase}\right) + {\rm baseline}
     """
-    return amp * np.cos(2 * np.pi * freq * x + phase) + baseline
+    return amp * np.sin(2 * np.pi * freq * x + phase) + baseline
 
 
 def exponential_decay(

qiskit_experiments/library/calibration/analysis/drag_analysis.py

@@ -64,6 +64,8 @@ class DragCalAnalysis(curve.CurveAnalysis):
             name="series-0",
             filter_kwargs={"series": 0},
             plot_symbol="o",
+            model_description=r"{\rm amp} \cos\left(2 \pi\cdot {\rm freq}_0\cdot x "
+            r"- 2 \pi \beta\right) + {\rm base}",
         ),
         curve.SeriesDef(
             fit_func=lambda x, amp, freq0, freq1, freq2, beta, base: cos(
@@ -73,6 +75,8 @@ class DragCalAnalysis(curve.CurveAnalysis):
             name="series-1",
             filter_kwargs={"series": 1},
             plot_symbol="^",
+            model_description=r"{\rm amp} \cos\left(2 \pi\cdot {\rm freq}_1\cdot x "
+            r"- 2 \pi \beta\right) + {\rm base}",
         ),
         curve.SeriesDef(
             fit_func=lambda x, amp, freq0, freq1, freq2, beta, base: cos(
@@ -82,6 +86,8 @@ class DragCalAnalysis(curve.CurveAnalysis):
             name="series-2",
             filter_kwargs={"series": 2},
             plot_symbol="v",
+            model_description=r"{\rm amp} \cos\left(2 \pi\cdot {\rm freq}_2\cdot x "
+            r"- 2 \pi \beta\right) + {\rm base}",
         ),
     ]
 

qiskit_experiments/library/calibration/analysis/fine_amplitude_analysis.py

@@ -71,6 +71,8 @@ class FineAmplitudeAnalysis(curve.CurveAnalysis):
                 baseline=base,
             ),
             plot_color="blue",
+            model_description=r"\frac{{\rm amp}}{2}\cos\left(x[{\rm d}\theta + {\rm apg} ] "
+            r"+ {\rm phase\_offset}\right)+{\rm base}",
         )
     ]
 

qiskit_experiments/library/calibration/analysis/oscillation_analysis.py

@@ -59,6 +59,8 @@ class OscillationAnalysis(curve.CurveAnalysis):
                 x, amp=amp, freq=freq, phase=phase, baseline=base
             ),
             plot_color="blue",
+            model_description=r"{\rm amp} \cos\left(2 \pi\cdot {\rm freq}\cdot x "
+            r"+ {\rm phase}\right) + {\rm base}",
         )
     ]
 

qiskit_experiments/library/characterization/resonance_analysis.py

@@ -62,6 +62,7 @@ class ResonanceAnalysis(curve.CurveAnalysis):
                 x, amp=a, sigma=sigma, x0=freq, baseline=b
             ),
             plot_color="blue",
+            model_description=r"a \exp(-(x-f)^2/(2\sigma^2)) + b",
         )
     ]
 

qiskit_experiments/library/randomized_benchmarking/interleaved_rb_analysis.py

@@ -103,6 +103,7 @@ class InterleavedRBAnalysis(RBAnalysis):
             plot_color="red",
             plot_symbol=".",
             plot_fit_uncertainty=True,
+            model_description=r"a \alpha^{x} + b",
         ),
         curve.SeriesDef(
             name="Interleaved",
@@ -113,6 +114,7 @@ class InterleavedRBAnalysis(RBAnalysis):
             plot_color="orange",
             plot_symbol="^",
             plot_fit_uncertainty=True,
+            model_description=r"a (\alpha_c\alpha)^{x} + b",
         ),
     ]
 

qiskit_experiments/library/randomized_benchmarking/rb_analysis.py

@@ -62,6 +62,7 @@ class RBAnalysis(curve.CurveAnalysis):
             ),
             plot_color="blue",
             plot_fit_uncertainty=True,
+            model_description=r"a \alpha^x + b",
         )
     ]
 

releasenotes/notes/add-fit-models-e9b82e9d6c2aef43.yaml

@@ -0,0 +1,8 @@
+---
+other:
+  - |
+    ``model_description`` is added to
+    :py:class:`~qiskit_experiments.curve_analysis.curve_data.SeriesDef`.
+    This field stores the string representation of the fit model of the curve.
+    This information is appended to the analysis result and will be
+    saved in the result database (if possible).

test/curve_analysis/test_curve_fit.py

@@ -113,20 +113,23 @@ def setUp(self):
                         x, amp=p0, lamb=p1, baseline=p4
                     ),
                     filter_kwargs={"type": 1, "valid": True},
+                    model_description=r"p_0 * \exp(p_1 x) + p4",
                 ),
                 SeriesDef(
                     name="curve2",
                     fit_func=lambda x, p0, p1, p2, p3, p4: fit_function.exponential_decay(
                         x, amp=p0, lamb=p2, baseline=p4
                     ),
                     filter_kwargs={"type": 2, "valid": True},
+                    model_description=r"p_0 * \exp(p_2 x) + p4",
                 ),
                 SeriesDef(
                     name="curve3",
                     fit_func=lambda x, p0, p1, p2, p3, p4: fit_function.exponential_decay(
                         x, amp=p0, lamb=p3, baseline=p4
                     ),
                     filter_kwargs={"type": 3, "valid": True},
+                    model_description=r"p_0 * \exp(p_3 x) + p4",
                 ),
             ],
         )
@@ -321,6 +324,7 @@ def test_run_single_curve_analysis(self):
                     fit_func=lambda x, p0, p1, p2, p3: fit_function.exponential_decay(
                         x, amp=p0, lamb=p1, x0=p2, baseline=p3
                     ),
+                    model_description=r"p_0 \exp(p_1 x + p_2) + p_3",
                 )
             ],
         )
@@ -347,6 +351,7 @@ def test_run_single_curve_analysis(self):
         np.testing.assert_array_almost_equal(result.value.value, ref_popt, decimal=self.err_decimal)
         self.assertEqual(result.extra["dof"], 46)
         self.assertListEqual(result.extra["popt_keys"], ["p0", "p1", "p2", "p3"])
+        self.assertDictEqual(result.extra["fit_models"], {"curve1": r"p_0 \exp(p_1 x + p_2) + p_3"})
 
         # special entry formatted for database
         result = results[1]

test/randomized_benchmarking/rb_interleaved_1qubit_output_analysis.json

@@ -1 +1 @@
-[{"name": "@Parameters_InterleavedRBAnalysis", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": {"__type__": "array", "__value__": [0.5116650227730736, 0.9994768276408555, 0.9979725591597532, 0.4884021095472425]}, "stderr": {"__type__": "array", "__value__": [0.006437954091857918, 1.4847358402650982e-05, 6.882915444481367e-05, 0.006675424355241532]}, "unit": null}}}, "extra": {"popt_keys": ["a", "alpha", "alpha_c", "b"], "dof": 16, "covariance_mat": {"__type__": "array", "__value__": [[4.144725288887011e-05, 4.533933132185307e-08, 3.746653829699993e-07, -4.023979858687134e-05], [4.5339331321853074e-08, 2.2044405153677072e-10, 6.432549372321611e-10, -6.928087572143161e-08], [3.746653829699994e-07, 6.432549372321611e-10, 4.737452501588013e-09, -4.2638270013676844e-07], [-4.023979858687135e-05, -6.92808757214316e-08, -4.2638270013676844e-07, 4.456129032255182e-05]]}}}, {"name": "alpha", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.9994768276408555, "stderr": 1.4847358402650982e-05, "unit": null}}}, "extra": {}}, {"name": "alpha_c", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.9979725591597532, "stderr": 6.882915444481367e-05, "unit": null}}}, "extra": {}}, {"name": "EPC", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.0010137204201233763, "stderr": 3.4414577222406835e-05, "unit": null}}}, "extra": {"EPC_systematic_err": 0.0010137204201233763, "EPC_systematic_bounds": [0.0, 0.0020274408402467525]}}]
+[{"name": "@Parameters_InterleavedRBAnalysis", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": {"__type__": "array", "__value__": [0.5034561671663651, 0.9994727949681657, 0.9977551279697571, 0.49581463461118536]}, "stderr": {"__type__": "array", "__value__": [0.010251156479303655, 2.5780778918330006e-05, 0.00012837420188229864, 0.010490177588586015]}, "unit": null}}}, "extra": {"popt_keys": ["a", "alpha", "alpha_c", "b"], "dof": 16, "covariance_mat": {"__type__": "array", "__value__": [[0.00010508620916316932, 1.0217443095675483e-07, 1.0661366451271264e-06, -9.865292651801042e-05], [1.0217443095675484e-07, 6.646485616358089e-10, 1.943773589454093e-09, -1.776204727970427e-07], [1.0661366451271262e-06, 1.9437735894540925e-09, 1.6479935708917166e-08, -1.233244622053204e-06], [-9.865292651801042e-05, -1.776204727970427e-07, -1.233244622053204e-06, 0.0001100438258400723]]}, "fit_models": {"Standard": "a \\alpha^{x} + b", "Interleaved": "a (\\alpha_c\\alpha)^{x} + b"}}}, {"name": "alpha", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.9994727949681657, "stderr": 2.5780778918330006e-05, "unit": null}}}, "extra": {}}, {"name": "alpha_c", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.9977551279697571, "stderr": 0.00012837420188229864, "unit": null}}}, "extra": {}}, {"name": "EPC", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.001122436015121464, "stderr": 6.418710094114932e-05, "unit": null}}}, "extra": {"EPC_systematic_err": 0.001122436015121464, "EPC_systematic_bounds": [0.0, 0.002244872030242928]}}]

test/randomized_benchmarking/rb_interleaved_2qubits_output_analysis.json

@@ -1 +1 @@
-[{"name": "@Parameters_InterleavedRBAnalysis", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": {"__type__": "array", "__value__": [0.7342127078996477, 0.9884063217136179, 0.9927651616883224, 0.2543568492711422]}, "stderr": {"__type__": "array", "__value__": [0.004948839577232964, 0.00020370367690368603, 0.00044396411042263065, 0.004356375518781768]}, "unit": null}}}, "extra": {"popt_keys": ["a", "alpha", "alpha_c", "b"], "dof": 16, "covariance_mat": {"__type__": "array", "__value__": [[2.4491013161187343e-05, 5.236454920804876e-07, 1.2793043417064756e-06, -1.6442242444492406e-05], [5.236454920804876e-07, 4.149518798408131e-08, 4.9409520508775484e-08, -7.419439279427566e-07], [1.2793043417064754e-06, 4.940952050877548e-08, 1.971041313433578e-07, -1.5342339511770156e-06], [-1.6442242444492406e-05, -7.419439279427566e-07, -1.5342339511770158e-06, 1.8978007660641124e-05]]}}}, {"name": "alpha", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.9884063217136179, "stderr": 0.00020370367690368603, "unit": null}}}, "extra": {}}, {"name": "alpha_c", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.9927651616883224, "stderr": 0.00044396411042263065, "unit": null}}}, "extra": {}}, {"name": "EPC", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.005426128733758195, "stderr": 0.000332973082816973, "unit": null}}}, "extra": {"EPC_systematic_err": 0.01196438869581501, "EPC_systematic_bounds": [0, 0.017390517429573205]}}]
+[{"name": "@Parameters_InterleavedRBAnalysis", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": {"__type__": "array", "__value__": [0.7251425386126389, 0.9874511785804855, 0.9927473424015756, 0.2664779856707594]}, "stderr": {"__type__": "array", "__value__": [0.005664202737254954, 0.00020569131958238083, 0.00039769928395310864, 0.005864117641934524]}, "unit": null}}}, "extra": {"popt_keys": ["a", "alpha", "alpha_c", "b"], "dof": 16, "covariance_mat": {"__type__": "array", "__value__": [[3.208319264872652e-05, 1.0448534717564974e-06, 1.627583546356998e-06, -3.285490958063846e-05], [1.0448534717564977e-06, 4.230891895154112e-08, 5.057408974150914e-08, -1.106317359642681e-06], [1.627583546356998e-06, 5.057408974150913e-08, 1.5816472045681535e-07, -1.750695025460556e-06], [-3.285490958063846e-05, -1.106317359642681e-06, -1.750695025460556e-06, 3.438787571844772e-05]]}, "fit_models": {"Standard": "a \\alpha^{x} + b", "Interleaved": "a (\\alpha_c\\alpha)^{x} + b"}}}, {"name": "alpha", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.9874511785804855, "stderr": 0.00020569131958238083, "unit": null}}}, "extra": {}}, {"name": "alpha_c", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.9927473424015756, "stderr": 0.00039769928395310864, "unit": null}}}, "extra": {}}, {"name": "EPC", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.005439493198818313, "stderr": 0.00029827446296483147, "unit": null}}}, "extra": {"EPC_systematic_err": 0.01338373893045347, "EPC_systematic_bounds": [0, 0.018823232129271783]}}]

test/randomized_benchmarking/rb_standard_1qubit_output_analysis.json

@@ -1 +1 @@
-[{"name": "@Parameters_RBAnalysis", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": {"__type__": "array", "__value__": [0.9967817420311399, 0.9997606942463159, 2.813384952009873e-13]}, "stderr": {"__type__": "array", "__value__": [1.5428416345109068, 0.00041321430695411904, 1.5480528890603416]}, "unit": null}}}, "extra": {"popt_keys": ["a", "alpha", "b"], "dof": 7, "covariance_mat": {"__type__": "array", "__value__": [[2.3803603091802867, 0.0006371615470064913, -2.388380804138054], [0.0006371615470064913, 1.7074606347157292e-07, -0.0006393835737758766], [-2.388380804138054, -0.0006393835737758766, 2.3964677473280704]]}}}, {"name": "alpha", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.9997606942463159, "stderr": 0.00041321430695411904, "unit": null}}}, "extra": {}}, {"name": "EPC", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.00011965287684206904, "stderr": 0.00020660715347705952, "unit": null}}}, "extra": {}}, {"name": "EPG_x", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.0005083368294731997, "stderr": null, "unit": null}}}, "extra": {}}]
+[{"name": "@Parameters_RBAnalysis", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": {"__type__": "array", "__value__": [0.35777153783730536, 0.999206126929566, 0.6424033472489555]}, "stderr": {"__type__": "array", "__value__": [0.13354619096959772, 0.0004296014698631278, 0.1381255753346132]}, "unit": null}}}, "extra": {"popt_keys": ["a", "alpha", "b"], "dof": 7, "covariance_mat": {"__type__": "array", "__value__": [[0.017834585122488263, 5.677840091061019e-05, -0.018423379167709843], [5.67784009106102e-05, 1.8455742290855988e-07, -5.902946904590408e-05], [-0.018423379167709843, -5.902946904590408e-05, 0.019078674561517905]]}, "fit_models": {"Series-0": "a \\alpha^x + b"}}}, {"name": "alpha", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.999206126929566, "stderr": 0.0004296014698631278, "unit": null}}}, "extra": {}}, {"name": "EPC", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.00039693653521699623, "stderr": 0.0002148007349315639, "unit": null}}}, "extra": {}}, {"name": "EPG_x", "value": {"__type__": "__object__", "__value__": {"__name__": "FitVal", "__module__": "qiskit_experiments.database_service.db_fitval", "__kwargs__": {"value": 0.001687416879912102, "stderr": null, "unit": null}}}, "extra": {}}]