Merge branch 'main' of https://github.com/Qiskit-Community/qiskit-exp…

…eriments into issue-qiskit-community#1238-draft

update /local/issue-#1238-draft

.mergify.yml

@@ -6,4 +6,4 @@ pull_request_rules:
     actions:
       backport:
         branches:
-          - stable/0.7
+          - stable/0.8

docs/conf.py

@@ -35,10 +35,10 @@
 
 # -- Project information -----------------------------------------------------
 # The short X.Y version
-version = os.getenv("VERSION_STRING", "0.8")
+version = os.getenv("VERSION_STRING", "0.9")
 
 # The full version, including alpha/beta/rc tags
-release = os.getenv("RELEASE_STRING", "0.8.0")
+release = os.getenv("RELEASE_STRING", "0.9.0")
 
 project = "Qiskit Experiments"
 copyright = f"2021-{datetime.date.today().year}, Qiskit Development Team"  # pylint: disable=redefined-builtin

docs/manuals/characterization/stark_experiment.rst

@@ -160,6 +160,11 @@ by a variant of the Hahn-echo pulse sequence [5]_.
         message=".*Due to the deprecation of Qiskit Pulse.*",
         category=DeprecationWarning,
     )
+    warnings.filterwarnings(
+        "ignore",
+        message=".*The entire Qiskit Pulse package is being deprecated.*",
+        category=DeprecationWarning,
+    )
 
     from qiskit_experiments.library import StarkRamseyXY
     from qiskit import schedule, pulse

docs/manuals/measurement/restless_measurements.rst

@@ -77,6 +77,16 @@ they use always starts with the qubits in the ground state.
         message=".*Support for restless.*",
         category=DeprecationWarning,
     )
+    warnings.filterwarnings(
+        "ignore",
+        message=".*The entire Qiskit Pulse package is being deprecated.*",
+        category=DeprecationWarning,
+    )
+    warnings.filterwarnings(
+        "ignore",
+        message=".*QuantumCircuit.duration.*",
+        category=DeprecationWarning,
+    )
 
     # Temporary workaround for missing support in Qiskit and qiskit-ibm-runtime
     from qiskit_experiments.test.patching import patch_sampler_test_support

docs/release_notes.rst

@@ -3,7 +3,7 @@ Release Notes
 =============
 
 .. release-notes::
-   :earliest-version: 0.7.0
+   :earliest-version: 0.8.0
 
 .. _Release Notes_0.7.0:
 
@@ -1344,18 +1344,6 @@ New Features
 - A new experiment :class:`.ZZRamsey` has been added to measure the ZZ
   coefficient between a pair of qubits.
 
-.. releasenotes/notes/mirror-rb-ec4d695a9a923971.yaml @ b'09fe450c18bf2bb706b664416e34cb26b770a9ff'
-
-- Added a new experiment class :class:`.MirrorRB`. This class implements
-  mirror randomized benchmarking, a variant of randomized benchmarking that measures
-  the fidelity of user-defined ensembles of randomized mirror circuits.
-
-.. releasenotes/notes/mirror-rb-ec4d695a9a923971.yaml @ b'09fe450c18bf2bb706b664416e34cb26b770a9ff'
-
-- Added a base class that samples circuit layers for randomized benchmarking experiments,
-  :class:`.BaseSampler`. The edge grab sampler :class:`.EdgeGrabSampler` and a single
-  qubit gate sampler :class:`.SingleQubitSampler` are implemented.
-
 
 .. _Release Notes_0.5.0_Known Issues:
 

docs/tutorials/calibrations.rst

@@ -51,6 +51,11 @@ This automatic updating can also be disabled using the ``auto_update`` flag.
         message=".*Due to the deprecation of Qiskit Pulse.*",
         category=DeprecationWarning,
     )
+    warnings.filterwarnings(
+        "ignore",
+        message=".*The entire Qiskit Pulse package is being deprecated.*",
+        category=DeprecationWarning,
+    )
 
 .. jupyter-execute::
 

docs/tutorials/data_processor.rst

@@ -78,6 +78,11 @@ The code below sets up the Rabi experiment.
         message=".*Due to the deprecation of Qiskit Pulse.*",
         category=DeprecationWarning,
     )
+    warnings.filterwarnings(
+        "ignore",
+        message=".*The entire Qiskit Pulse package is being deprecated.*",
+        category=DeprecationWarning,
+    )
 
 .. jupyter-execute::
 

docs/tutorials/visualization.rst

@@ -48,6 +48,11 @@ First, we display the default figure from a :class:`.Rabi` experiment as a start
         message=".*Due to the deprecation of Qiskit Pulse.*",
         category=DeprecationWarning,
     )
+    warnings.filterwarnings(
+        "ignore",
+        message=".*The entire Qiskit Pulse package is being deprecated.*",
+        category=DeprecationWarning,
+    )
 
 .. jupyter-execute::
 

qiskit_experiments/VERSION.txt

@@ -1 +1 @@
-0.8.0
+0.9.0

qiskit_experiments/library/characterization/drag.py

@@ -77,6 +77,11 @@ class RoughDrag(BaseExperiment, RestlessMixin):
                 message=".*Due to the deprecation of Qiskit Pulse.*",
                 category=DeprecationWarning,
             )
+            warnings.filterwarnings(
+                "ignore",
+                message=".*The entire Qiskit Pulse package is being deprecated.*",
+                category=DeprecationWarning,
+            )
 
             # backend
             from qiskit_experiments.test.pulse_backend import SingleTransmonTestBackend

qiskit_experiments/library/characterization/rabi.py

@@ -69,6 +69,11 @@ class Rabi(BaseExperiment, RestlessMixin):
                 message=".*Due to the deprecation of Qiskit Pulse.*",
                 category=DeprecationWarning,
             )
+            warnings.filterwarnings(
+                "ignore",
+                message=".*The entire Qiskit Pulse package is being deprecated.*",
+                category=DeprecationWarning,
+            )
 
             # backend
             from qiskit_experiments.test.pulse_backend import SingleTransmonTestBackend
@@ -254,6 +259,11 @@ class EFRabi(Rabi):
                 message=".*Due to the deprecation of Qiskit Pulse.*",
                 category=DeprecationWarning,
             )
+            warnings.filterwarnings(
+                "ignore",
+                message=".*The entire Qiskit Pulse package is being deprecated.*",
+                category=DeprecationWarning,
+            )
 
             # backend
             from qiskit_experiments.test import SingleTransmonTestBackend

qiskit_experiments/test/pulse_backend.py

@@ -603,6 +603,7 @@ def __init__(
             }
         )
         self._target = Target(
+            num_qubits=1,
             qubit_properties=[QubitProperties(frequency=qubit_frequency)],
             dt=self.dt,
             granularity=16,

test/base.py

@@ -111,11 +111,11 @@ def setUpClass(cls):
 
             patch_sampler_test_support()
 
-            warnings.filterwarnings("error", category=DeprecationWarning)
             # Tests should not generate any warnings unless testing those
             # warnings. In that case, the test should catch the warning
             # assertWarns or warnings.catch_warnings.
             warnings.filterwarnings("error", module="qiskit_experiments")
+            warnings.filterwarnings("error", module=r"test\.")
             # Ideally, changes introducing pending deprecations should include
             # alternative code paths and not need to generate warnings in the
             # tests but until this exception is necessary until the use of the
@@ -176,6 +176,7 @@ def setUpClass(cls):
                 ".*qiskit.providers.models.backendproperties.BackendProperties.*",
                 ".*qiskit.providers.fake_provider.fake_backend.FakeBackend.*",
                 ".*qiskit.providers.backend.BackendV1.*",
+                ".*The entire Qiskit Pulse package is being deprecated.*",
             ]
             for msg in allow_deprecationwarning_message:
                 warnings.filterwarnings("default", category=DeprecationWarning, message=msg)

test/curve_analysis/test_baseclass.py

@@ -13,6 +13,8 @@
 # pylint: disable=invalid-name
 
 """Test curve fitting base class."""
+import warnings
+
 from test.base import QiskitExperimentsTestCase
 from test.fake_experiment import FakeExperiment
 
@@ -139,22 +141,26 @@ def test_data_extraction(self):
             },
         )
 
-        curve_data = analysis._run_data_processing(raw_data=expdata1.data() + expdata2.data())
-        self.assertListEqual(curve_data.labels, ["s1", "s2"])
-
-        # check data of series1
-        sub1 = curve_data.get_subset_of("s1")
-        self.assertListEqual(sub1.labels, ["s1"])
-        np.testing.assert_array_equal(sub1.x, x)
-        np.testing.assert_array_almost_equal(sub1.y, y1, decimal=3)
-        np.testing.assert_array_equal(sub1.data_allocation, np.full(x.size, 0))
-
-        # check data of series2
-        sub2 = curve_data.get_subset_of("s2")
-        self.assertListEqual(sub2.labels, ["s2"])
-        np.testing.assert_array_equal(sub2.x, x)
-        np.testing.assert_array_almost_equal(sub2.y, y2, decimal=3)
-        np.testing.assert_array_equal(sub2.data_allocation, np.full(x.size, 1))
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", message=".*ScatterTable.labels.*")
+            warnings.filterwarnings("ignore", message=".*ScatterTable.get_subset_of.*")
+            warnings.filterwarnings("ignore", message=".*ScatterTable.data_allocation.*")
+            curve_data = analysis._run_data_processing(raw_data=expdata1.data() + expdata2.data())
+            self.assertListEqual(curve_data.labels, ["s1", "s2"])
+
+            # check data of series1
+            sub1 = curve_data.get_subset_of("s1")
+            self.assertListEqual(sub1.labels, ["s1"])
+            np.testing.assert_array_equal(sub1.x, x)
+            np.testing.assert_array_almost_equal(sub1.y, y1, decimal=3)
+            np.testing.assert_array_equal(sub1.data_allocation, np.full(x.size, 0))
+
+            # check data of series2
+            sub2 = curve_data.get_subset_of("s2")
+            self.assertListEqual(sub2.labels, ["s2"])
+            np.testing.assert_array_equal(sub2.x, x)
+            np.testing.assert_array_almost_equal(sub2.y, y2, decimal=3)
+            np.testing.assert_array_equal(sub2.data_allocation, np.full(x.size, 1))
 
     def test_create_result(self):
         """A testcase for creating analysis result data from fit data."""

test/curve_analysis/test_curve_fitting.py

@@ -11,6 +11,8 @@
 # that they have been altered from the originals.
 
 """Test version string generation."""
+import warnings
+
 from test.base import QiskitExperimentsTestCase
 import numpy as np
 
@@ -88,55 +90,77 @@ def test_process_curve_data(self):
     def test_mean_xy_data(self):
         """Test mean_xy_data function"""
         # pylint: disable=unbalanced-tuple-unpacking
-        x = np.array([1, 1, 1, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5])
-        y = np.array([1, 2, 3, 8, 10, 50, 60, 10, 11, 17, 10, 10, 10, 10])
-        x_mean, y_mean, y_sigma, _ = mean_xy_data(x, y, method="sample")
-
-        expected_x_mean = np.array([1, 2, 3, 4, 5])
-        expected_y_mean = np.array([2, 32, 10.5, 17, 10])
-        expected_y_sigma = np.sqrt(np.array([2 / 9, 542 / 4, 0.25 / 2, 0, 0]))
-        self.assertTrue(np.allclose(expected_x_mean, x_mean), msg=f"{x_mean} != {expected_x_mean}")
-        self.assertTrue(np.allclose(expected_y_mean, y_mean), msg=f"{y_mean} != {expected_y_mean}")
-        self.assertTrue(
-            np.allclose(expected_y_sigma, y_sigma), msg=f"{y_sigma} != {expected_y_sigma}"
-        )
-
-        sigma = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
-        x_mean, y_mean, y_sigma, _ = mean_xy_data(x, y, sigma, method="iwv")
-        expected_y_mean = np.array([1.34693878, 23.31590234, 10.44137931, 17.0, 10.0])
-        expected_y_sigma = np.array([0.85714286, 2.57610543, 5.97927455, 10.0, 6.17470935])
-        self.assertTrue(np.allclose(expected_x_mean, x_mean), msg=f"{x_mean} != {expected_x_mean}")
-        self.assertTrue(np.allclose(expected_y_mean, y_mean), msg=f"{y_mean} != {expected_y_mean}")
-        self.assertTrue(
-            np.allclose(expected_y_sigma, y_sigma), msg=f"{y_sigma} != {expected_y_sigma}"
-        )
-
-        sigma = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
-        shots = np.array([10, 20, 10, 30, 20, 10, 40, 10, 10, 20, 30, 20, 30, 10])
-        x_mean, y_mean, y_sigma, y_shots = mean_xy_data(x, y, sigma, shots, method="shots_weighted")
-        expected_y_mean = np.array([2.0, 33.4, 10.5, 17.0, 10.0])
-        expected_y_sigma = np.array([1.27475488, 3.26190129, 6.02079729, 10.0, 6.46166282])
-        expected_y_shots = np.array([40, 100, 20, 20, 90])
-        self.assertTrue(np.allclose(expected_x_mean, x_mean), msg=f"{x_mean} != {expected_x_mean}")
-        self.assertTrue(np.allclose(expected_y_mean, y_mean), msg=f"{y_mean} != {expected_y_mean}")
-        self.assertTrue(
-            np.allclose(expected_y_sigma, y_sigma), msg=f"{y_sigma} != {expected_y_sigma}"
-        )
-        self.assertTrue(
-            np.allclose(expected_y_shots, y_shots), msg=f"{y_shots} != {expected_y_shots}"
-        )
-
-        x = np.array([1, 1, 1, 1, 2, 2, 2, 2])
-        y = np.array([2, 6, 100, 200, 17, 50, 60, 70])
-        series = np.array([0, 0, 1, 1, 0, 1, 1, 1])
-        series, x_mean, y_mean, y_sigma, _ = multi_mean_xy_data(series, x, y, method="sample")
-        expected_x_mean = np.array([1, 2, 1, 2])
-        expected_y_mean = np.array([4, 17, 150, 60])
-        expected_y_sigma = np.sqrt(np.array([4.0 / 2, 0.0 / 1, 2500.0 / 2, 66.66666667 / 3]))
-        expected_series = np.array([0, 0, 1, 1])
-        self.assertTrue(np.allclose(expected_x_mean, x_mean), msg=f"{x_mean} != {expected_x_mean}")
-        self.assertTrue(np.allclose(expected_y_mean, y_mean), msg=f"{y_mean} != {expected_y_mean}")
-        self.assertTrue(
-            np.allclose(expected_y_sigma, y_sigma), msg=f"{y_sigma} != {expected_y_sigma}"
-        )
-        self.assertTrue(np.allclose(expected_series, series), msg=f"{series} != {expected_series}")
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", message=".*mean_xy_data.*")
+            x = np.array([1, 1, 1, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5])
+            y = np.array([1, 2, 3, 8, 10, 50, 60, 10, 11, 17, 10, 10, 10, 10])
+            x_mean, y_mean, y_sigma, _ = mean_xy_data(x, y, method="sample")
+
+            expected_x_mean = np.array([1, 2, 3, 4, 5])
+            expected_y_mean = np.array([2, 32, 10.5, 17, 10])
+            expected_y_sigma = np.sqrt(np.array([2 / 9, 542 / 4, 0.25 / 2, 0, 0]))
+            self.assertTrue(
+                np.allclose(expected_x_mean, x_mean), msg=f"{x_mean} != {expected_x_mean}"
+            )
+            self.assertTrue(
+                np.allclose(expected_y_mean, y_mean), msg=f"{y_mean} != {expected_y_mean}"
+            )
+            self.assertTrue(
+                np.allclose(expected_y_sigma, y_sigma), msg=f"{y_sigma} != {expected_y_sigma}"
+            )
+
+            sigma = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
+            x_mean, y_mean, y_sigma, _ = mean_xy_data(x, y, sigma, method="iwv")
+            expected_y_mean = np.array([1.34693878, 23.31590234, 10.44137931, 17.0, 10.0])
+            expected_y_sigma = np.array([0.85714286, 2.57610543, 5.97927455, 10.0, 6.17470935])
+            self.assertTrue(
+                np.allclose(expected_x_mean, x_mean), msg=f"{x_mean} != {expected_x_mean}"
+            )
+            self.assertTrue(
+                np.allclose(expected_y_mean, y_mean), msg=f"{y_mean} != {expected_y_mean}"
+            )
+            self.assertTrue(
+                np.allclose(expected_y_sigma, y_sigma), msg=f"{y_sigma} != {expected_y_sigma}"
+            )
+
+            sigma = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
+            shots = np.array([10, 20, 10, 30, 20, 10, 40, 10, 10, 20, 30, 20, 30, 10])
+            x_mean, y_mean, y_sigma, y_shots = mean_xy_data(
+                x, y, sigma, shots, method="shots_weighted"
+            )
+            expected_y_mean = np.array([2.0, 33.4, 10.5, 17.0, 10.0])
+            expected_y_sigma = np.array([1.27475488, 3.26190129, 6.02079729, 10.0, 6.46166282])
+            expected_y_shots = np.array([40, 100, 20, 20, 90])
+            self.assertTrue(
+                np.allclose(expected_x_mean, x_mean), msg=f"{x_mean} != {expected_x_mean}"
+            )
+            self.assertTrue(
+                np.allclose(expected_y_mean, y_mean), msg=f"{y_mean} != {expected_y_mean}"
+            )
+            self.assertTrue(
+                np.allclose(expected_y_sigma, y_sigma), msg=f"{y_sigma} != {expected_y_sigma}"
+            )
+            self.assertTrue(
+                np.allclose(expected_y_shots, y_shots), msg=f"{y_shots} != {expected_y_shots}"
+            )
+
+            x = np.array([1, 1, 1, 1, 2, 2, 2, 2])
+            y = np.array([2, 6, 100, 200, 17, 50, 60, 70])
+            series = np.array([0, 0, 1, 1, 0, 1, 1, 1])
+            series, x_mean, y_mean, y_sigma, _ = multi_mean_xy_data(series, x, y, method="sample")
+            expected_x_mean = np.array([1, 2, 1, 2])
+            expected_y_mean = np.array([4, 17, 150, 60])
+            expected_y_sigma = np.sqrt(np.array([4.0 / 2, 0.0 / 1, 2500.0 / 2, 66.66666667 / 3]))
+            expected_series = np.array([0, 0, 1, 1])
+            self.assertTrue(
+                np.allclose(expected_x_mean, x_mean), msg=f"{x_mean} != {expected_x_mean}"
+            )
+            self.assertTrue(
+                np.allclose(expected_y_mean, y_mean), msg=f"{y_mean} != {expected_y_mean}"
+            )
+            self.assertTrue(
+                np.allclose(expected_y_sigma, y_sigma), msg=f"{y_sigma} != {expected_y_sigma}"
+            )
+            self.assertTrue(
+                np.allclose(expected_series, series), msg=f"{series} != {expected_series}"
+            )

test/library/quantum_volume/qv_ideal_probabilities_qiskit_1_3.json

@@ -0,0 +1 @@
+[[0.016967836206746738, 0.11989893254007651, 0.00394654461853002, 0.13589528784677357, 0.20511482048008703, 0.0816791691977386, 0.14060546401643623, 0.2958919450936116], [0.12056007843940977, 0.10070988761274678, 0.41332860589623954, 0.07543734133716322, 0.021096066850169303, 0.1601488559241172, 0.03780114546996992, 0.0709180184701846], [0.10058394660184918, 0.39126225007098714, 0.09012977139125049, 0.02982028558144569, 0.005523508253283623, 0.040983681969626276, 0.2946239196681466, 0.04707263646341052], [0.3688522014926478, 0.09580420366241688, 0.027757018918742504, 0.15826915582072928, 0.13834528025457885, 0.11281057034461814, 0.04713950685498301, 0.05102206265128345], [0.2238855389562393, 0.0177775881962427, 0.0010361793224677451, 0.49743548639570334, 0.14345035837415962, 0.010983952529157095, 0.030086371572847458, 0.07534452465318305], [0.07191730758022169, 0.08577798556935443, 0.05222185324072163, 0.011505080428938406, 0.1506083560400655, 0.07947214515779061, 0.06954659702813472, 0.4789506749547722], [0.02933798117949475, 0.0905804630987942, 0.13414073913728367, 0.3283400904287246, 0.0039327427715444745, 0.06305126971619468, 0.07960132612718543, 0.27101538754077775], [0.10361935285611489, 0.5602661574454084, 0.0033379483277632236, 0.024104300671844853, 0.11180012107386983, 0.018590174540476845, 0.06496224115302754, 0.11331970393149465], [0.28002251748562146, 0.15659108128101512, 0.06931560231414717, 0.015939512694748054, 0.20272160865971692, 0.06437155774108827, 0.17997014329975955, 0.031067976523903224], [0.2583110429786593, 0.39280733849970395, 0.01004465642965845, 0.10112365839530935, 0.04820792916847759, 0.019172100821762683, 0.07396094843817139, 0.09637232526825651], [0.04205005835070806, 0.5982838080237505, 0.24825488091343656, 0.11141125271210575, 0.0, 0.0, 0.0, 0.0], [0.21631173866223324, 0.07201585886194255, 0.19647582952426423, 0.10741584491273042, 0.054982878753641044, 0.021165794683348307, 0.2526914377956466, 0.07894061680619284], [0.04109901108305593, 0.5346418163425509, 0.0, 0.0, 0.19988603142757153, 0.22437314114682205, 0.0, 0.0], [0.1069722912636189, 0.19610594120528163, 0.13827775538043613, 0.12862292833267164, 0.05652810658633324, 0.0979666878234763, 0.15917488848657904, 0.1163514009216026], [0.1599264302659344, 0.32405634292478286, 0.050423144948197246, 0.03657193295081257, 0.13405197214337658, 0.1872187743627101, 0.0005017400591434017, 0.1072496623450432], [0.057428879095657914, 0.07553302531949552, 0.05869499440092844, 0.031696849711854186, 0.20158700775693938, 0.20118430243224272, 0.02391580078822289, 0.34995914049465865], [0.12733557958689173, 0.08478618874152216, 0.032857670388396855, 0.08627573094283553, 0.18807352626751228, 0.2365561765088683, 0.15519965863714033, 0.08891546892683337], [0.05434853562358783, 0.1282198611011262, 0.11416220587356729, 0.049568640406240184, 0.1511891111128068, 0.14032078289874864, 0.3087935419191839, 0.05339732106473909], [0.06135629616886566, 0.25774569674205117, 0.0464794921453153, 0.15917144757251409, 0.1677568850176423, 0.18787259587234334, 0.05248811617198047, 0.0671294703092877], [0.005879541314725802, 0.025021402090856428, 0.44431583322312285, 0.05198340090748109, 0.04211534913258567, 0.013297849871047806, 0.26003956333041356, 0.15734706012976746]]