diff --git a/docs/apidocs/index.rst b/docs/apidocs/index.rst index b06897e4f8..b23965c685 100644 --- a/docs/apidocs/index.rst +++ b/docs/apidocs/index.rst @@ -30,3 +30,4 @@ Experiment Modules mod_randomized_benchmarking mod_tomography mod_quantum_volume + mod_hamiltonian diff --git a/docs/apidocs/mod_hamiltonian.rst b/docs/apidocs/mod_hamiltonian.rst new file mode 100644 index 0000000000..9fbf5e00cd --- /dev/null +++ b/docs/apidocs/mod_hamiltonian.rst @@ -0,0 +1,6 @@ +.. _qiskit-experiments-hamiltonian: + +.. automodule:: qiskit_experiments.library.hamiltonian + :no-members: + :no-inherited-members: + :no-special-members: diff --git a/docs/conf.py b/docs/conf.py index 9fd5223c7b..44ad860300 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -43,9 +43,9 @@ author = 'Qiskit Development Team' # The short X.Y version -version = '0.2' +version = '0.3' # The full version, including alpha/beta/rc tags -release = '0.2.0' +release = '0.3.0' rst_prolog = """ .. raw:: html diff --git a/docs/tutorials/calibrating_armonk.ipynb b/docs/tutorials/calibrating_armonk.ipynb index 2858e89e1e..0bdf760be7 100644 --- a/docs/tutorials/calibrating_armonk.ipynb +++ b/docs/tutorials/calibrating_armonk.ipynb @@ -9,16 +9,15 @@ "\n", "In this tutorial we demonstrate how to calibrate single-qubit gates on `ibmq_armonk` using the calibration framework in qiskit-experiments. We will run experiments to find the qubit frequency, calibrate the amplitude of DRAG pulses and chose the value of the DRAG parameter that minimizes leakage. The calibration framework requires the user to\n", "\n", - "* setup an instance of `Calibrations` or `BackendCalibrations`,\n", - "* run calibration experiments which can be found either in `qiskit_experiments.library.calibration` or `qiskit_experiments.library.characterization`, and\n", - "* update the values of the parameters stored in the instance of `Calibrations` (or `BackendCalibrations`) using `Update` classes. \n", + "* setup an instance of `Calibrations`,\n", + "* run calibration experiments which can be found in `qiskit_experiments.library.calibration`. \n", "\n", - "You will see that the `Update` classes are not meant to be instantiated but provide an `update` class method to extract calibrated parameter values and add them to the calibrations." + "Note that the values of the parameters stored in the instance of the `Calibrations` class will automatically be updated by the calibration experiments. This automatic updating can also be disabled using the `auto_update` flag." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "e3836dba", "metadata": {}, "outputs": [], @@ -36,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "87e1101f", "metadata": {}, "outputs": [], @@ -48,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "e3f6f6c1", "metadata": {}, "outputs": [], @@ -61,12 +60,12 @@ "id": "5325fe3d", "metadata": {}, "source": [ - "The two functions below show how to setup an instance of `BackendCalibrations`. To do this the user defines the template schedules to calibrate. These template schedules are fully parameterized, even the channel indices on which the pulses are played. Furthermore, the name of the parameter in the channel index must follow the convention laid out in the documentation of the calibration module. Note that the parameters in the channel indices are automatically mapped to the channel index when `get_schedule` is called. " + "The two functions below show how to setup an instance of `Calibrations`. To do this the user defines the template schedules to calibrate. These template schedules are fully parameterized, even the channel indices on which the pulses are played. Furthermore, the name of the parameter in the channel index must follow the convention laid out in the documentation of the calibration module. Note that the parameters in the channel indices are automatically mapped to the channel index when `get_schedule` is called. " ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "ca0c1462", "metadata": {}, "outputs": [], @@ -116,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "a2f9d7e5", "metadata": {}, "outputs": [], @@ -135,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "48895a9f", "metadata": {}, "outputs": [], @@ -145,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "bd83e088", "metadata": {}, "outputs": [], @@ -166,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "908ff764", "metadata": {}, "outputs": [], @@ -184,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "fa22b8a4", "metadata": {}, "outputs": [ @@ -222,112 +221,112 @@ " \n", " \n", " 0\n", - " duration\n", + " β\n", " ()\n", " x\n", - " 3.200000e+02\n", + " 0.000000e+00\n", " default\n", " True\n", - " 2021-12-07 14:11:16.088611+0100\n", + " 2021-12-12 16:45:32.534775+0100\n", " None\n", " \n", " \n", " 1\n", - " β\n", + " duration\n", " ()\n", - " x\n", - " 0.000000e+00\n", + " sx\n", + " 3.200000e+02\n", " default\n", " True\n", - " 2021-12-07 14:11:16.088696+0100\n", + " 2021-12-12 16:45:32.534783+0100\n", " None\n", " \n", " \n", " 2\n", - " meas_freq\n", - " (0,)\n", - " None\n", - " 6.993371e+09\n", + " σ\n", + " ()\n", + " x\n", + " 8.000000e+01\n", " default\n", " True\n", - " 2021-12-07 14:11:16.298667+0100\n", + " 2021-12-12 16:45:32.534757+0100\n", " None\n", " \n", " \n", " 3\n", - " β\n", + " duration\n", " ()\n", - " sx\n", - " 0.000000e+00\n", + " x\n", + " 3.200000e+02\n", " default\n", " True\n", - " 2021-12-07 14:11:16.088738+0100\n", + " 2021-12-12 16:45:32.534723+0100\n", " None\n", " \n", " \n", " 4\n", - " duration\n", + " σ\n", " ()\n", " sx\n", - " 3.200000e+02\n", + " 8.000000e+01\n", " default\n", " True\n", - " 2021-12-07 14:11:16.088757+0100\n", + " 2021-12-12 16:45:32.534790+0100\n", " None\n", " \n", " \n", " 5\n", - " σ\n", + " amp\n", " ()\n", - " x\n", - " 8.000000e+01\n", + " sx\n", + " 2.500000e-01\n", " default\n", " True\n", - " 2021-12-07 14:11:16.088718+0100\n", + " 2021-12-12 16:45:32.534805+0100\n", " None\n", " \n", " \n", " 6\n", - " amp\n", - " ()\n", - " x\n", - " 5.000000e-01\n", + " drive_freq\n", + " (0,)\n", + " None\n", + " 4.971613e+09\n", " default\n", " True\n", - " 2021-12-07 14:11:16.088669+0100\n", + " 2021-12-12 16:45:32.769415+0100\n", " None\n", " \n", " \n", " 7\n", - " drive_freq\n", - " (0,)\n", - " None\n", - " 4.971650e+09\n", + " amp\n", + " ()\n", + " x\n", + " 5.000000e-01\n", " default\n", " True\n", - " 2021-12-07 14:11:16.298640+0100\n", + " 2021-12-12 16:45:32.534767+0100\n", " None\n", " \n", " \n", " 8\n", - " σ\n", - " ()\n", - " sx\n", - " 8.000000e+01\n", + " meas_freq\n", + " (0,)\n", + " None\n", + " 6.993371e+09\n", " default\n", " True\n", - " 2021-12-07 14:11:16.088794+0100\n", + " 2021-12-12 16:45:32.769441+0100\n", " None\n", " \n", " \n", " 9\n", - " amp\n", + " β\n", " ()\n", " sx\n", - " 2.500000e-01\n", + " 0.000000e+00\n", " default\n", " True\n", - " 2021-12-07 14:11:16.088776+0100\n", + " 2021-12-12 16:45:32.534798+0100\n", " None\n", " \n", " \n", @@ -336,31 +335,31 @@ ], "text/plain": [ " parameter qubits schedule value group valid \\\n", - "0 duration () x 3.200000e+02 default True \n", - "1 β () x 0.000000e+00 default True \n", - "2 meas_freq (0,) None 6.993371e+09 default True \n", - "3 β () sx 0.000000e+00 default True \n", - "4 duration () sx 3.200000e+02 default True \n", - "5 σ () x 8.000000e+01 default True \n", - "6 amp () x 5.000000e-01 default True \n", - "7 drive_freq (0,) None 4.971650e+09 default True \n", - "8 σ () sx 8.000000e+01 default True \n", - "9 amp () sx 2.500000e-01 default True \n", + "0 β () x 0.000000e+00 default True \n", + "1 duration () sx 3.200000e+02 default True \n", + "2 σ () x 8.000000e+01 default True \n", + "3 duration () x 3.200000e+02 default True \n", + "4 σ () sx 8.000000e+01 default True \n", + "5 amp () sx 2.500000e-01 default True \n", + "6 drive_freq (0,) None 4.971613e+09 default True \n", + "7 amp () x 5.000000e-01 default True \n", + "8 meas_freq (0,) None 6.993371e+09 default True \n", + "9 β () sx 0.000000e+00 default True \n", "\n", " date_time exp_id \n", - "0 2021-12-07 14:11:16.088611+0100 None \n", - "1 2021-12-07 14:11:16.088696+0100 None \n", - "2 2021-12-07 14:11:16.298667+0100 None \n", - "3 2021-12-07 14:11:16.088738+0100 None \n", - "4 2021-12-07 14:11:16.088757+0100 None \n", - "5 2021-12-07 14:11:16.088718+0100 None \n", - "6 2021-12-07 14:11:16.088669+0100 None \n", - "7 2021-12-07 14:11:16.298640+0100 None \n", - "8 2021-12-07 14:11:16.088794+0100 None \n", - "9 2021-12-07 14:11:16.088776+0100 None " + "0 2021-12-12 16:45:32.534775+0100 None \n", + "1 2021-12-12 16:45:32.534783+0100 None \n", + "2 2021-12-12 16:45:32.534757+0100 None \n", + "3 2021-12-12 16:45:32.534723+0100 None \n", + "4 2021-12-12 16:45:32.534790+0100 None \n", + "5 2021-12-12 16:45:32.534805+0100 None \n", + "6 2021-12-12 16:45:32.769415+0100 None \n", + "7 2021-12-12 16:45:32.534767+0100 None \n", + "8 2021-12-12 16:45:32.769441+0100 None \n", + "9 2021-12-12 16:45:32.534798+0100 None " ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -371,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "502aef29", "metadata": {}, "outputs": [], @@ -384,18 +383,18 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "91184061", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -413,7 +412,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -445,7 +444,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -471,8 +470,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: f01\n", - "- value: 4971710631.072543 ± 46113.624847722356 Hz\n", - "- χ²: 1.1424156746459029\n", + "- value: 4971670289.422816 ± 23140.38461583078 Hz\n", + "- χ²: 1.3182518484011887\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -531,51 +530,38 @@ " \n", " \n", " 0\n", - " meas_lo_freq\n", + " drive_freq\n", " (0,)\n", " None\n", - " 6.993371e+09\n", + " 4.971670e+09\n", " default\n", " True\n", - " 2021-11-03 16:40:00.882285+0100\n", - " None\n", + " 2021-12-12 16:49:50.675000+0100\n", + " 70119ceb-0b48-4a24-b39a-af583e50e219\n", " \n", " \n", " 1\n", - " qubit_lo_freq\n", + " meas_freq\n", " (0,)\n", " None\n", - " 4.971589e+09\n", + " 6.993371e+09\n", " default\n", " True\n", - " 2021-11-03 16:40:00.882258+0100\n", - " None\n", - " \n", - " \n", - " 2\n", - " qubit_lo_freq\n", - " (0,)\n", + " 2021-12-12 16:45:32.769441+0100\n", " None\n", - " 4.971711e+09\n", - " default\n", - " True\n", - " 2021-11-03 16:41:19.204000+0100\n", - " 426026dd-b836-4756-803f-ad9422826895\n", " \n", " \n", "\n", "" ], "text/plain": [ - " parameter qubits schedule value group valid \\\n", - "0 meas_lo_freq (0,) None 6.993371e+09 default True \n", - "1 qubit_lo_freq (0,) None 4.971589e+09 default True \n", - "2 qubit_lo_freq (0,) None 4.971711e+09 default True \n", + " parameter qubits schedule value group valid \\\n", + "0 drive_freq (0,) None 4.971670e+09 default True \n", + "1 meas_freq (0,) None 6.993371e+09 default True \n", "\n", " date_time exp_id \n", - "0 2021-11-03 16:40:00.882285+0100 None \n", - "1 2021-11-03 16:40:00.882258+0100 None \n", - "2 2021-11-03 16:41:19.204000+0100 426026dd-b836-4756-803f-ad9422826895 " + "0 2021-12-12 16:49:50.675000+0100 70119ceb-0b48-4a24-b39a-af583e50e219 \n", + "1 2021-12-12 16:45:32.769441+0100 None " ] }, "execution_count": 16, @@ -641,7 +627,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -681,7 +667,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -707,9 +693,9 @@ "text": [ "DbAnalysisResultV1\n", "- name: rabi_rate\n", - "- value: 0.5835711622496985 ± 0.0028894642130689666\n", - "- χ²: 2.0874659580448207\n", - "- quality: good\n", + "- value: 0.5842264468922855 ± 0.001401840343655317\n", + "- χ²: 3.933170680803399\n", + "- quality: bad\n", "- device_components: ['Q0']\n", "- verified: False\n" ] @@ -760,45 +746,45 @@ " \n", " 0\n", " amp\n", - " ()\n", + " (0,)\n", " x\n", - " 0.500000+0.000000j\n", + " 0.855833+0.000000j\n", " default\n", " True\n", - " 2021-11-03 16:40:00.882460+0100\n", - " None\n", + " 2021-12-12 16:58:31.216000+0100\n", + " 2ebdbde5-f504-41d6-8bb3-4fd7286a15fa\n", " \n", " \n", " 1\n", " amp\n", - " (0,)\n", - " x\n", - " 0.856794+0.000000j\n", + " ()\n", + " sx\n", + " 0.250000+0.000000j\n", " default\n", " True\n", - " 2021-11-03 16:42:27.534000+0100\n", - " bc1dac04-bbfc-4282-97ae-90272616e093\n", + " 2021-12-12 16:45:32.534805+0100\n", + " None\n", " \n", " \n", " 2\n", " amp\n", " (0,)\n", " sx\n", - " 0.428397+0.000000j\n", + " 0.427916+0.000000j\n", " default\n", " True\n", - " 2021-11-03 16:42:27.534000+0100\n", - " bc1dac04-bbfc-4282-97ae-90272616e093\n", + " 2021-12-12 16:58:31.216000+0100\n", + " 2ebdbde5-f504-41d6-8bb3-4fd7286a15fa\n", " \n", " \n", " 3\n", " amp\n", " ()\n", - " sx\n", - " 0.250000+0.000000j\n", + " x\n", + " 0.500000+0.000000j\n", " default\n", " True\n", - " 2021-11-03 16:40:00.882499+0100\n", + " 2021-12-12 16:45:32.534767+0100\n", " None\n", " \n", " \n", @@ -807,16 +793,16 @@ ], "text/plain": [ " parameter qubits schedule value group valid \\\n", - "0 amp () x 0.500000+0.000000j default True \n", - "1 amp (0,) x 0.856794+0.000000j default True \n", - "2 amp (0,) sx 0.428397+0.000000j default True \n", - "3 amp () sx 0.250000+0.000000j default True \n", + "0 amp (0,) x 0.855833+0.000000j default True \n", + "1 amp () sx 0.250000+0.000000j default True \n", + "2 amp (0,) sx 0.427916+0.000000j default True \n", + "3 amp () x 0.500000+0.000000j default True \n", "\n", " date_time exp_id \n", - "0 2021-11-03 16:40:00.882460+0100 None \n", - "1 2021-11-03 16:42:27.534000+0100 bc1dac04-bbfc-4282-97ae-90272616e093 \n", - "2 2021-11-03 16:42:27.534000+0100 bc1dac04-bbfc-4282-97ae-90272616e093 \n", - "3 2021-11-03 16:40:00.882499+0100 None " + "0 2021-12-12 16:58:31.216000+0100 2ebdbde5-f504-41d6-8bb3-4fd7286a15fa \n", + "1 2021-12-12 16:45:32.534805+0100 None \n", + "2 2021-12-12 16:58:31.216000+0100 2ebdbde5-f504-41d6-8bb3-4fd7286a15fa \n", + "3 2021-12-12 16:45:32.534767+0100 None " ] }, "execution_count": 23, @@ -845,7 +831,7 @@ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=(0.42839677+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=(0.42791627+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())" ] }, "execution_count": 24, @@ -866,7 +852,7 @@ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=(0.85679354+0j), sigma=80, beta=0), DriveChannel(0)), name=\"x\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=(0.85583253+0j), sigma=80, beta=0), DriveChannel(0)), name=\"x\", transform=AlignLeft())" ] }, "execution_count": 25, @@ -887,7 +873,7 @@ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=0.85679354j, sigma=80, beta=0), DriveChannel(0)), name=\"y\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=0.85583253j, sigma=80, beta=0), DriveChannel(0)), name=\"y\", transform=AlignLeft())" ] }, "execution_count": 26, @@ -919,7 +905,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/calibrations.py:981: UserWarning: Schedules are only saved in text format. They cannot be re-loaded.\n", + "/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/calibrations.py:1333: UserWarning: Schedules are only saved in text format. They cannot be re-loaded.\n", " warnings.warn(\"Schedules are only saved in text format. They cannot be re-loaded.\")\n" ] } @@ -938,7 +924,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 28, "id": "24256b82", "metadata": {}, "outputs": [], @@ -988,89 +974,63 @@ " \n", " 0\n", " amp\n", - " ()\n", + " (0,)\n", " x\n", - " 0.500000+0.000000j\n", + " 0.855833+0.000000j\n", " default\n", " True\n", - " 2021-11-03 16:42:30.370765+0100\n", - " None\n", + " 2021-12-12 16:58:31.216000+0100\n", + " 2ebdbde5-f504-41d6-8bb3-4fd7286a15fa\n", " \n", " \n", " 1\n", " amp\n", " ()\n", - " x\n", - " 0.500000+0.000000j\n", + " sx\n", + " 0.250000+0.000000j\n", " default\n", " True\n", - " 2021-11-03 16:40:00.882460+0100\n", - " \n", + " 2021-12-12 16:58:48.227382+0100\n", + " None\n", " \n", " \n", " 2\n", " amp\n", " (0,)\n", " sx\n", - " 0.428397+0.000000j\n", + " 0.427916+0.000000j\n", " default\n", " True\n", - " 2021-11-03 16:42:27.534000+0100\n", - " bc1dac04-bbfc-4282-97ae-90272616e093\n", + " 2021-12-12 16:58:31.216000+0100\n", + " 2ebdbde5-f504-41d6-8bb3-4fd7286a15fa\n", " \n", " \n", " 3\n", " amp\n", - " (0,)\n", - " x\n", - " 0.856794+0.000000j\n", - " default\n", - " True\n", - " 2021-11-03 16:42:27.534000+0100\n", - " bc1dac04-bbfc-4282-97ae-90272616e093\n", - " \n", - " \n", - " 4\n", - " amp\n", " ()\n", - " sx\n", - " 0.250000+0.000000j\n", + " x\n", + " 0.500000+0.000000j\n", " default\n", " True\n", - " 2021-11-03 16:42:30.370841+0100\n", + " 2021-12-12 16:58:48.227345+0100\n", " None\n", " \n", - " \n", - " 5\n", - " amp\n", - " ()\n", - " sx\n", - " 0.250000+0.000000j\n", - " default\n", - " True\n", - " 2021-11-03 16:40:00.882499+0100\n", - " \n", - " \n", " \n", "\n", "" ], "text/plain": [ " parameter qubits schedule value group valid \\\n", - "0 amp () x 0.500000+0.000000j default True \n", - "1 amp () x 0.500000+0.000000j default True \n", - "2 amp (0,) sx 0.428397+0.000000j default True \n", - "3 amp (0,) x 0.856794+0.000000j default True \n", - "4 amp () sx 0.250000+0.000000j default True \n", - "5 amp () sx 0.250000+0.000000j default True \n", + "0 amp (0,) x 0.855833+0.000000j default True \n", + "1 amp () sx 0.250000+0.000000j default True \n", + "2 amp (0,) sx 0.427916+0.000000j default True \n", + "3 amp () x 0.500000+0.000000j default True \n", "\n", " date_time exp_id \n", - "0 2021-11-03 16:42:30.370765+0100 None \n", - "1 2021-11-03 16:40:00.882460+0100 \n", - "2 2021-11-03 16:42:27.534000+0100 bc1dac04-bbfc-4282-97ae-90272616e093 \n", - "3 2021-11-03 16:42:27.534000+0100 bc1dac04-bbfc-4282-97ae-90272616e093 \n", - "4 2021-11-03 16:42:30.370841+0100 None \n", - "5 2021-11-03 16:40:00.882499+0100 " + "0 2021-12-12 16:58:31.216000+0100 2ebdbde5-f504-41d6-8bb3-4fd7286a15fa \n", + "1 2021-12-12 16:58:48.227382+0100 None \n", + "2 2021-12-12 16:58:31.216000+0100 2ebdbde5-f504-41d6-8bb3-4fd7286a15fa \n", + "3 2021-12-12 16:58:48.227345+0100 None " ] }, "execution_count": 29, @@ -1133,7 +1093,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -1169,7 +1129,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1195,8 +1155,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: beta\n", - "- value: -1.1055031138199274 ± 0.018683794096014686\n", - "- χ²: 1.1196703236999246\n", + "- value: -1.1559985430408373 ± 0.009022771608798948\n", + "- χ²: 1.0864875414479318\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -1248,24 +1208,24 @@ " \n", " 0\n", " β\n", - " (0,)\n", + " ()\n", " x\n", - " -1.105503\n", + " 0.000000\n", " default\n", " True\n", - " 2021-11-03 16:43:59.621000+0100\n", - " fa8c73a5-3a96-4581-b8e2-1e6384976dc2\n", + " 2021-12-12 16:58:48.227353+0100\n", + " None\n", " \n", " \n", " 1\n", " β\n", - " ()\n", - " sx\n", - " 0.000000\n", + " (0,)\n", + " x\n", + " -1.155999\n", " default\n", " True\n", - " 2021-11-03 16:42:30.370857+0100\n", - " None\n", + " 2021-12-12 17:08:01.247000+0100\n", + " 3dda7894-1c3a-445a-8e99-fadccdc76a3c\n", " \n", " \n", " 2\n", @@ -1275,49 +1235,23 @@ " 0.000000\n", " default\n", " True\n", - " 2021-11-03 16:40:00.882507+0100\n", - " \n", - " \n", - " \n", - " 3\n", - " β\n", - " ()\n", - " x\n", - " 0.000000\n", - " default\n", - " True\n", - " 2021-11-03 16:42:30.370808+0100\n", + " 2021-12-12 16:58:48.227375+0100\n", " None\n", " \n", - " \n", - " 4\n", - " β\n", - " ()\n", - " x\n", - " 0.000000\n", - " default\n", - " True\n", - " 2021-11-03 16:40:00.882483+0100\n", - " \n", - " \n", " \n", "\n", "" ], "text/plain": [ " parameter qubits schedule value group valid \\\n", - "0 β (0,) x -1.105503 default True \n", - "1 β () sx 0.000000 default True \n", + "0 β () x 0.000000 default True \n", + "1 β (0,) x -1.155999 default True \n", "2 β () sx 0.000000 default True \n", - "3 β () x 0.000000 default True \n", - "4 β () x 0.000000 default True \n", "\n", " date_time exp_id \n", - "0 2021-11-03 16:43:59.621000+0100 fa8c73a5-3a96-4581-b8e2-1e6384976dc2 \n", - "1 2021-11-03 16:42:30.370857+0100 None \n", - "2 2021-11-03 16:40:00.882507+0100 \n", - "3 2021-11-03 16:42:30.370808+0100 None \n", - "4 2021-11-03 16:40:00.882483+0100 " + "0 2021-12-12 16:58:48.227353+0100 None \n", + "1 2021-12-12 17:08:01.247000+0100 3dda7894-1c3a-445a-8e99-fadccdc76a3c \n", + "2 2021-12-12 16:58:48.227375+0100 None " ] }, "execution_count": 36, @@ -1370,7 +1304,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", 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" ] @@ -1402,7 +1336,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1428,9 +1362,9 @@ "text": [ "DbAnalysisResultV1\n", "- name: d_theta\n", - "- value: -0.08009669103196095 ± 0.0010877479733272931\n", - "- χ²: 25.48854792104786\n", - "- quality: bad\n", + "- value: -0.1259812027299088 ± 0.0015459904802351424\n", + "- χ²: 1.1671390569895568\n", + "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" ] @@ -1458,8 +1392,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The ideal angle is 3.14 rad. We measured a deviation of -0.080 rad.\n", - "Thus, scale the 0.8792+0.0000j pulse amplitude by 1.026 to obtain 0.90221+0.00000j.\n" + "The ideal angle is 3.14 rad. We measured a deviation of -0.126 rad.\n", + "Thus, scale the 0.8916+0.0000j pulse amplitude by 1.042 to obtain 0.92883+0.00000j.\n" ] } ], @@ -1521,102 +1455,63 @@ " \n", " 0\n", " amp\n", - " ()\n", + " (0,)\n", " x\n", - " 0.500000+0.000000j\n", + " 0.891586+0.000000j\n", " default\n", " True\n", - " 2021-11-03 16:42:30.370765+0100\n", - " None\n", + " 2021-12-12 17:13:11.518000+0100\n", + " 11278d3c-b5e7-4e51-b00e-b2fcaabbb3e9\n", " \n", " \n", " 1\n", " amp\n", " ()\n", - " x\n", - " 0.500000+0.000000j\n", + " sx\n", + " 0.250000+0.000000j\n", " default\n", " True\n", - " 2021-11-03 16:40:00.882460+0100\n", - " \n", + " 2021-12-12 16:58:48.227382+0100\n", + " None\n", " \n", " \n", " 2\n", " amp\n", " (0,)\n", " sx\n", - " 0.428397+0.000000j\n", + " 0.427916+0.000000j\n", " default\n", " True\n", - " 2021-11-03 16:42:27.534000+0100\n", - " bc1dac04-bbfc-4282-97ae-90272616e093\n", + " 2021-12-12 16:58:31.216000+0100\n", + " 2ebdbde5-f504-41d6-8bb3-4fd7286a15fa\n", " \n", " \n", " 3\n", " amp\n", - " (0,)\n", - " x\n", - " 0.856794+0.000000j\n", - " default\n", - " True\n", - " 2021-11-03 16:42:27.534000+0100\n", - " bc1dac04-bbfc-4282-97ae-90272616e093\n", - " \n", - " \n", - " 4\n", - " amp\n", - " (0,)\n", - " x\n", - " 0.879209+0.000000j\n", - " default\n", - " True\n", - " 2021-11-03 16:44:31.671000+0100\n", - " b4966de8-a1ce-4822-b70d-775adffd4f45\n", - " \n", - " \n", - " 5\n", - " amp\n", " ()\n", - " sx\n", - " 0.250000+0.000000j\n", + " x\n", + " 0.500000+0.000000j\n", " default\n", " True\n", - " 2021-11-03 16:42:30.370841+0100\n", + " 2021-12-12 16:58:48.227345+0100\n", " None\n", " \n", - " \n", - " 6\n", - " amp\n", - " ()\n", - " sx\n", - " 0.250000+0.000000j\n", - " default\n", - " True\n", - " 2021-11-03 16:40:00.882499+0100\n", - " \n", - " \n", " \n", "\n", "" ], "text/plain": [ " parameter qubits schedule value group valid \\\n", - "0 amp () x 0.500000+0.000000j default True \n", - "1 amp () x 0.500000+0.000000j default True \n", - "2 amp (0,) sx 0.428397+0.000000j default True \n", - "3 amp (0,) x 0.856794+0.000000j default True \n", - "4 amp (0,) x 0.879209+0.000000j default True \n", - "5 amp () sx 0.250000+0.000000j default True \n", - "6 amp () sx 0.250000+0.000000j default True \n", + "0 amp (0,) x 0.891586+0.000000j default True \n", + "1 amp () sx 0.250000+0.000000j default True \n", + "2 amp (0,) sx 0.427916+0.000000j default True \n", + "3 amp () x 0.500000+0.000000j default True \n", "\n", " date_time exp_id \n", - "0 2021-11-03 16:42:30.370765+0100 None \n", - "1 2021-11-03 16:40:00.882460+0100 \n", - "2 2021-11-03 16:42:27.534000+0100 bc1dac04-bbfc-4282-97ae-90272616e093 \n", - "3 2021-11-03 16:42:27.534000+0100 bc1dac04-bbfc-4282-97ae-90272616e093 \n", - "4 2021-11-03 16:44:31.671000+0100 b4966de8-a1ce-4822-b70d-775adffd4f45 \n", - "5 2021-11-03 16:42:30.370841+0100 None \n", - "6 2021-11-03 16:40:00.882499+0100 " + "0 2021-12-12 17:13:11.518000+0100 11278d3c-b5e7-4e51-b00e-b2fcaabbb3e9 \n", + "1 2021-12-12 16:58:48.227382+0100 None \n", + "2 2021-12-12 16:58:31.216000+0100 2ebdbde5-f504-41d6-8bb3-4fd7286a15fa \n", + "3 2021-12-12 16:58:48.227345+0100 None " ] }, "execution_count": 44, @@ -1654,7 +1549,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1688,9 +1583,9 @@ "text": [ "DbAnalysisResultV1\n", "- name: d_theta\n", - "- value: -0.05526699149655558 ± 0.0009785317206102975\n", - "- χ²: 5.662785981062492\n", - "- quality: bad\n", + "- value: -0.02297341948181875 ± 0.0006927301059360831\n", + "- χ²: 1.93685218471896\n", + "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" ] @@ -1738,7 +1633,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1770,7 +1665,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1796,8 +1691,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: @Parameters_FineAmplitudeAnalysis\n", - "- value: [0.79618143 0.02732817 0.47355097] ± [0.01300009 0.00098153 0.00362947]\n", - "- χ²: 1.144205682189689\n", + "- value: [0.79868138 0.03532474 0.48706798] ± [0.00668965 0.00057385 0.00179089]\n", + "- χ²: 1.1894079114882583\n", "- quality: good\n", "- extra: <4 items>\n", "- device_components: ['Q0']\n", @@ -1821,8 +1716,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: d_theta\n", - "- value: 0.027328167719946905 ± 0.0009815320929519311\n", - "- χ²: 1.144205682189689\n", + "- value: 0.035324738012950226 ± 0.0005738510570579542\n", + "- χ²: 1.1894079114882583\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -1882,128 +1777,63 @@ " \n", " 0\n", " amp\n", - " ()\n", + " (0,)\n", " x\n", - " 0.500000+0.000000j\n", + " 0.898154+0.000000j\n", " default\n", " True\n", - " 2021-11-03 16:42:30.370765+0100\n", - " None\n", + " 2021-12-12 17:16:31.432000+0100\n", + " 3cfb9f75-1ba8-4de5-9f68-68f9871be450\n", " \n", " \n", " 1\n", " amp\n", " ()\n", - " x\n", - " 0.500000+0.000000j\n", + " sx\n", + " 0.250000+0.000000j\n", " default\n", " True\n", - " 2021-11-03 16:40:00.882460+0100\n", - " \n", + " 2021-12-12 16:58:48.227382+0100\n", + " None\n", " \n", " \n", " 2\n", " amp\n", " (0,)\n", " sx\n", - " 0.428397+0.000000j\n", + " 0.418505+0.000000j\n", " default\n", " True\n", - " 2021-11-03 16:42:27.534000+0100\n", - " bc1dac04-bbfc-4282-97ae-90272616e093\n", + " 2021-12-12 17:19:01.453000+0100\n", + " 26b8c108-38dc-4d45-921a-e586d21dc261\n", " \n", " \n", " 3\n", " amp\n", - " (0,)\n", - " sx\n", - " 0.421071+0.000000j\n", - " default\n", - " True\n", - " 2021-11-03 16:45:38.341000+0100\n", - " aee3d73b-423a-45c9-918a-8fff8c537b19\n", - " \n", - " \n", - " 4\n", - " amp\n", - " (0,)\n", - " x\n", - " 0.856794+0.000000j\n", - " default\n", - " True\n", - " 2021-11-03 16:42:27.534000+0100\n", - " bc1dac04-bbfc-4282-97ae-90272616e093\n", - " \n", - " \n", - " 5\n", - " amp\n", - " (0,)\n", - " x\n", - " 0.879209+0.000000j\n", - " default\n", - " True\n", - " 2021-11-03 16:44:31.671000+0100\n", - " b4966de8-a1ce-4822-b70d-775adffd4f45\n", - " \n", - " \n", - " 6\n", - " amp\n", - " (0,)\n", - " x\n", - " 0.894954+0.000000j\n", - " default\n", - " True\n", - " 2021-11-03 16:45:02.477000+0100\n", - " cb306dbb-ca97-42ce-9b7e-b5bcf4fe65dd\n", - " \n", - " \n", - " 7\n", - " amp\n", " ()\n", - " sx\n", - " 0.250000+0.000000j\n", + " x\n", + " 0.500000+0.000000j\n", " default\n", " True\n", - " 2021-11-03 16:42:30.370841+0100\n", + " 2021-12-12 16:58:48.227345+0100\n", " None\n", " \n", - " \n", - " 8\n", - " amp\n", - " ()\n", - " sx\n", - " 0.250000+0.000000j\n", - " default\n", - " True\n", - " 2021-11-03 16:40:00.882499+0100\n", - " \n", - " \n", " \n", "\n", "" ], "text/plain": [ " parameter qubits schedule value group valid \\\n", - "0 amp () x 0.500000+0.000000j default True \n", - "1 amp () x 0.500000+0.000000j default True \n", - "2 amp (0,) sx 0.428397+0.000000j default True \n", - "3 amp (0,) sx 0.421071+0.000000j default True \n", - "4 amp (0,) x 0.856794+0.000000j default True \n", - "5 amp (0,) x 0.879209+0.000000j default True \n", - "6 amp (0,) x 0.894954+0.000000j default True \n", - "7 amp () sx 0.250000+0.000000j default True \n", - "8 amp () sx 0.250000+0.000000j default True \n", + "0 amp (0,) x 0.898154+0.000000j default True \n", + "1 amp () sx 0.250000+0.000000j default True \n", + "2 amp (0,) sx 0.418505+0.000000j default True \n", + "3 amp () x 0.500000+0.000000j default True \n", "\n", " date_time exp_id \n", - "0 2021-11-03 16:42:30.370765+0100 None \n", - "1 2021-11-03 16:40:00.882460+0100 \n", - "2 2021-11-03 16:42:27.534000+0100 bc1dac04-bbfc-4282-97ae-90272616e093 \n", - "3 2021-11-03 16:45:38.341000+0100 aee3d73b-423a-45c9-918a-8fff8c537b19 \n", - "4 2021-11-03 16:42:27.534000+0100 bc1dac04-bbfc-4282-97ae-90272616e093 \n", - "5 2021-11-03 16:44:31.671000+0100 b4966de8-a1ce-4822-b70d-775adffd4f45 \n", - "6 2021-11-03 16:45:02.477000+0100 cb306dbb-ca97-42ce-9b7e-b5bcf4fe65dd \n", - "7 2021-11-03 16:42:30.370841+0100 None \n", - "8 2021-11-03 16:40:00.882499+0100 " + "0 2021-12-12 17:16:31.432000+0100 3cfb9f75-1ba8-4de5-9f68-68f9871be450 \n", + "1 2021-12-12 16:58:48.227382+0100 None \n", + "2 2021-12-12 17:19:01.453000+0100 26b8c108-38dc-4d45-921a-e586d21dc261 \n", + "3 2021-12-12 16:58:48.227345+0100 None " ] }, "execution_count": 55, @@ -2024,7 +1854,7 @@ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=(0.421071121202659+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=(0.4185047565+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())" ] }, "execution_count": 56, @@ -2045,7 +1875,7 @@ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=(0.894953534996499+0j), sigma=80, beta=-1.10550311381993), DriveChannel(0)), name=\"x\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=(0.8981539795+0j), sigma=80, beta=-1.155998543), DriveChannel(0)), name=\"x\", transform=AlignLeft())" ] }, "execution_count": 57, @@ -2066,7 +1896,7 @@ { "data": { "text/plain": [ - "ScheduleBlock(Play(Drag(duration=320, amp=0.894953534996499j, sigma=80, beta=-1.10550311381993), DriveChannel(0)), name=\"y\", transform=AlignLeft())" + "ScheduleBlock(Play(Drag(duration=320, amp=0.8981539795j, sigma=80, beta=-1.155998543), DriveChannel(0)), name=\"y\", transform=AlignLeft())" ] }, "execution_count": 58, diff --git a/docs/tutorials/experiment_cloud_service.ipynb b/docs/tutorials/experiment_cloud_service.ipynb index b636cdfbc8..b27adedc7a 100644 --- a/docs/tutorials/experiment_cloud_service.ipynb +++ b/docs/tutorials/experiment_cloud_service.ipynb @@ -61,23 +61,23 @@ "name": "stdout", "output_type": "stream", "text": [ - " ┌───┐ ░ ┌──────────────┐ ░ ┌─┐\n", - "q_0: ┤ X ├─░─┤ Delay(0[us]) ├─░─┤M├\n", - " └───┘ ░ └──────────────┘ ░ └╥┘\n", - "c: 1/════════════════════════════╩═\n", - " 0 \n" + " ┌───┐ ░ ┌─────────────────┐ ░ ┌─┐\n", + " q: ┤ X ├─░─┤ Delay(1e-06[s]) ├─░─┤M├\n", + " └───┘ ░ └─────────────────┘ ░ └╥┘\n", + "c: 1/═══════════════════════════════╩═\n", + " 0 \n" ] } ], "source": [ "from qiskit_experiments.library.characterization import T1\n", + "import numpy as np\n", "\n", - "t1_delays = list(range(0, 800, 50))\n", + "t1_delays = np.arange(1e-6, 600e-6, 50e-6)\n", "\n", "# Create an experiment for qubit 0,\n", - "# setting the unit to microseconds,\n", - "# with the specified time intervals\n", - "exp = T1(qubit=0, delays=t1_delays, unit=\"us\")\n", + "# with the specified time intervals in seconds\n", + "exp = T1(qubit=0, delays=t1_delays)\n", "print(exp.circuits()[0])" ] }, @@ -96,7 +96,15 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can view the experiment online at https://quantum-computing.ibm.com/experiments/d4539703-1f55-4aa5-befe-d8e8e5f274a3\n" + ] + } + ], "source": [ "# Run the experiment circuits with 1000 shots each,\n", "# and analyze the result\n", @@ -122,18 +130,13 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "scrolled": true, - "slideshow": { - "slide_type": "slide" - } - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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" ] }, "metadata": {}, @@ -143,12 +146,19 @@ "name": "stdout", "output_type": "stream", "text": [ + "DbAnalysisResultV1\n", + "- name: @Parameters_T1Analysis\n", + "- value: [7.94606369e-01 1.08432993e-01 1.64684523e-04] ± [1.15917392e-02 9.80484687e-03 7.24146088e-06]\n", + "- χ²: 0.6312147837244724\n", + "- quality: bad\n", + "- extra: <4 items>\n", + "- device_components: ['Q0']\n", + "- verified: False\n", "DbAnalysisResultV1\n", "- name: T1\n", - "- value: 0.00014003438826912983 ± 4.398101339932208e-06 s\n", - "- χ²: 1.9282291758563228\n", + "- value: 0.00016468452295042568 ± 7.241460875012179e-06 s\n", + "- χ²: 0.6312147837244724\n", "- quality: bad\n", - "- extra: <8 items>\n", "- device_components: ['Q0']\n", "- verified: False\n" ] @@ -250,7 +260,7 @@ "source": [ "from qiskit_experiments.database_service import DbExperimentDataV1 as DbExperimentData\n", "\n", - "load_exp = DbExperimentData.load(\"9eb0b0f4-be97-4c57-9665-8c9ff09442e8\", provider.service(\"experiment\"))" + "load_exp = DbExperimentData.load(\"d4539703-1f55-4aa5-befe-d8e8e5f274a3\", provider.service(\"experiment\"))" ] }, { @@ -268,16 +278,16 @@ { "data": { "image/svg+xml": [ - 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" \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -423,28 +500,32 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -452,53 +533,113 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -508,12 +649,34 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -522,119 +685,84 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "" @@ -705,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": { "scrolled": false, "slideshow": { @@ -717,23 +845,29 @@ "name": "stderr", "output_type": "stream", "text": [ - "Not all post-processing has finished. Consider calling save() again after all post-processing is done to save any newly generated data.\n", - "Analysis result cannot be saved because no experiment service is available.\n" + "Not all post-processing has finished. Consider calling save() again after all post-processing is done to save any newly generated data.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can view the experiment online at https://quantum-computing.ibm.com/experiments/2cbd7e7b-e347-4f17-a438-b16c6d53287f\n" ] }, { "data": { "text/plain": [ - "ExperimentData(T1, c22c3f3b-4fa0-4410-a753-0859c0549935, backend=ibmq_armonk, job_ids=['6106b1e569320609fc186865'])" + "" ] }, - "execution_count": 12, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "exp = T1(qubit=0, delays=t1_delays, unit=\"us\")\n", + "exp = T1(qubit=0, delays=t1_delays)\n", "\n", "t1_expdata = exp.run(backend=backend, shots=1000)\n", "t1_expdata.auto_save = True\n", @@ -756,7 +890,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -772,10 +906,10 @@ { "data": { "text/plain": [ - "'6ae505d7-a2ba-4f21-b962-42cfe6b5fdd1'" + "'9e81892a-f13e-468b-95fc-f829a2afce1f'" ] }, - "execution_count": 13, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -812,9 +946,17 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can view the experiment online at https://quantum-computing.ibm.com/experiments/f7fcf941-067a-4f0e-8dfb-f7c0ae862cc8\n" + ] + } + ], "source": [ "from qiskit_experiments.library import randomized_benchmarking as rb\n", "\n", @@ -829,7 +971,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 11, "metadata": { "slideshow": { "slide_type": "slide" @@ -838,7 +980,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -851,28 +993,40 @@ "output_type": "stream", "text": [ "DbAnalysisResultV1\n", - "- name: RBAnalysis\n", - "- value: [0.62327871 0.99941831 0.31212028] ± [0.56502796 0.00062857 0.56631132]\n", - "- χ²: 0.13454473118612684\n", - "- extra: <10 items>\n", + "- name: @Parameters_RBAnalysis\n", + "- value: [9.16364654e-01 9.99774688e-01 3.01828641e-19] ± [1.26073871e+00 3.32474852e-04 1.26197705e+00]\n", + "- χ²: 13.101370110046265\n", + "- extra: <4 items>\n", "- device_components: ['Q0']\n", "- verified: False\n", "DbAnalysisResultV1\n", "- name: alpha\n", - "- value: 0.9994183055710573 ± 0.000628570304337799\n", - "- χ²: 0.13454473118612684\n", + "- value: 0.9997746880536807 ± 0.00033247485242169305\n", + "- χ²: 13.101370110046265\n", "- device_components: ['Q0']\n", "- verified: False\n", "DbAnalysisResultV1\n", "- name: EPC\n", - "- value: 0.0002908472144713681 ± 0.0003144680764970782\n", - "- χ²: 0.13454473118612684\n", + "- value: 0.0001126559731596255 ± 0.00016623742621084652\n", + "- χ²: 13.101370110046265\n", + "- device_components: ['Q0']\n", + "- verified: False\n", + "DbAnalysisResultV1\n", + "- name: EPG_rz\n", + "- value: 0.0 ± 0.0\n", + "- χ²: 13.101370110046265\n", "- device_components: ['Q0']\n", "- verified: False\n", "DbAnalysisResultV1\n", - "- name: EPG\n", - "- value: {0: {'rz': 0.0, 'sx': 0.00015093763767253147, 'x': 0.00015093763767253147}}\n", - "- χ²: 0.13454473118612684\n", + "- name: EPG_sx\n", + "- value: 7.057902264347598e-05 ± 0.00010414782935747094\n", + "- χ²: 13.101370110046265\n", + "- device_components: ['Q0']\n", + "- verified: False\n", + "DbAnalysisResultV1\n", + "- name: EPG_x\n", + "- value: 7.057902264347598e-05 ± 0.00010414782935747094\n", + "- χ²: 13.101370110046265\n", "- device_components: ['Q0']\n", "- verified: False\n" ] @@ -913,24 +1067,25 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "You can view the experiment online at https://quantum-computing.ibm.com/experiments/14da0e00-3500-4113-9561-79077457bc3e\n", "DbAnalysisResultV1\n", "- name: state\n", - "- value: DensityMatrix([[0.5078125 +0.j , 0.44042969-0.00878906j],\n", - " [0.44042969+0.00878906j, 0.4921875 +0.j ]],\n", + "- value: DensityMatrix([[0.50195313+0.j , 0.40527344-0.00976562j],\n", + " [0.40527344+0.00976562j, 0.49804688+0.j ]],\n", " dims=(2,))\n", "- extra: <4 items>\n", "- device_components: ['Q0']\n", "- verified: False\n", "DbAnalysisResultV1\n", "- name: state_fidelity\n", - "- value: 0.9404296875000002\n", + "- value: 0.9052734375000002\n", "- device_components: ['Q0']\n", "- verified: False\n", "DbAnalysisResultV1\n", @@ -978,7 +1133,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1002,9 +1157,9 @@ ], "metadata": { "kernelspec": { - "display_name": "experiments-venv", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "experiments-venv" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1016,7 +1171,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/docs/tutorials/fine_calibrations.ipynb b/docs/tutorials/fine_calibrations.ipynb index 2e08426903..6b6cba4903 100644 --- a/docs/tutorials/fine_calibrations.ipynb +++ b/docs/tutorials/fine_calibrations.ipynb @@ -73,7 +73,7 @@ { "data": { "text/plain": [ - "Drag(duration=320, amp=(0.9051783795364403+0j), sigma=80, beta=-1.0111553175219203, name='Xp_d0')" + "Drag(duration=320, amp=(0.9017311554901297+0j), sigma=80, beta=-0.9639957979207219, name='Xp_d0')" ] }, "execution_count": 4, @@ -124,7 +124,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The reported amplitude of the X pulse is 0.9052+0.0000j.\n" + "The reported amplitude of the X pulse is 0.9017+0.0000j.\n" ] } ], @@ -186,7 +186,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", 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" ] @@ -218,7 +218,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -244,8 +244,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: d_theta\n", - "- value: 0.04826614947456462 ± 0.0009994728679398562\n", - "- χ²: 11.706758284290771\n", + "- value: 0.057015162697548215 ± 0.001116534957642551\n", + "- χ²: 10.326624587562133\n", "- quality: bad\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -266,9 +266,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "The ideal angle is 3.14 rad. We measured a deviation of 0.048 rad.\n", - "Thus, scale the 0.9233+0.0000j pulse amplitude by 0.985 to obtain 0.90931+0.00000j.\n", - "Amplitude reported by the backend 0.9052+0.0000j.\n" + "The ideal angle is 3.14 rad. We measured a deviation of 0.057 rad.\n", + "Thus, scale the 0.9198+0.0000j pulse amplitude by 0.982 to obtain 0.90337+0.00000j.\n", + "Amplitude reported by the backend 0.9017+0.0000j.\n" ] } ], @@ -333,7 +333,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -359,8 +359,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: d_theta\n", - "- value: -0.030517244232514656 ± 0.0009727068477499323\n", - "- χ²: 9.078404254770456\n", + "- value: -0.04063457298918617 ± 0.0008703716859582015\n", + "- χ²: 9.476715071582424\n", "- quality: bad\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -381,9 +381,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "The ideal angle is 3.14 rad. We measured a deviation of -0.031 rad.\n", - "Thus, scale the 0.8871+0.0000j pulse amplitude by 1.010 to obtain 0.89578+0.00000j.\n", - "Amplitude reported by the backend 0.9052+0.0000j.\n" + "The ideal angle is 3.14 rad. We measured a deviation of -0.041 rad.\n", + "Thus, scale the 0.8837+0.0000j pulse amplitude by 1.013 to obtain 0.89528+0.00000j.\n", + "Amplitude reported by the backend 0.9017+0.0000j.\n" ] } ], @@ -413,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "id": "coordinated-education", "metadata": {}, "outputs": [], @@ -424,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 21, "id": "boring-shaft", "metadata": {}, "outputs": [], @@ -435,18 +435,18 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 22, "id": "interior-schedule", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "
" ] }, - "execution_count": 7, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -457,7 +457,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 23, "id": "normal-content", "metadata": {}, "outputs": [], @@ -467,18 +467,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "id": "naval-franklin", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "execution_count": 9, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -489,7 +489,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 25, "id": "indonesian-tribe", "metadata": {}, "outputs": [ @@ -499,9 +499,9 @@ "text": [ "DbAnalysisResultV1\n", "- name: d_theta\n", - "- value: 0.1209986777958791 ± 0.0007349561528222693\n", - "- χ²: 46.57422337790615\n", - "- quality: bad\n", + "- value: 0.11453288392453519 ± 0.0004978518661405299\n", + "- χ²: 0.9575406908660448\n", + "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" ] @@ -513,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 26, "id": "swiss-prayer", "metadata": {}, "outputs": [ @@ -521,9 +521,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "The ideal angle is 1.57 rad. We measured a deviation of 0.121 rad.\n", - "Thus, scale the 0.4526+0.0000j pulse amplitude by 0.928 to obtain 0.42022+0.00000j.\n", - "Amplitude reported by the backend 0.4065-0.1037j.\n" + "The ideal angle is 1.57 rad. We measured a deviation of 0.115 rad.\n", + "Thus, scale the 0.4509+0.0000j pulse amplitude by 0.932 to obtain 0.42023+0.00000j.\n", + "Amplitude reported by the backend 0.4062-0.1000j.\n" ] } ], @@ -549,7 +549,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 27, "id": "extended-wedding", "metadata": {}, "outputs": [], @@ -564,7 +564,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 28, "id": "artistic-stand", "metadata": {}, "outputs": [], @@ -574,18 +574,18 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 29, "id": "excessive-transformation", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "execution_count": 14, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -596,7 +596,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, "id": "liquid-details", "metadata": {}, "outputs": [ @@ -606,9 +606,9 @@ "text": [ "DbAnalysisResultV1\n", "- name: d_theta\n", - "- value: 0.0013818683670870186 ± 0.0006246304059929329\n", - "- χ²: 6.112465315478119\n", - "- quality: bad\n", + "- value: 0.0035757081871945428 ± 0.0003946947710710705\n", + "- χ²: 1.2646181222690043\n", + "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" ] @@ -620,7 +620,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 31, "id": "driving-density", "metadata": {}, "outputs": [ @@ -628,9 +628,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "The ideal angle is 1.57 rad. We measured a deviation of 0.001 rad.\n", - "Thus, scale the 0.4202+0.0000j pulse amplitude by 0.999 to obtain 0.41985+0.00000j.\n", - "Amplitude reported by the backend 0.4065-0.1037j.\n" + "The ideal angle is 1.57 rad. We measured a deviation of 0.004 rad.\n", + "Thus, scale the 0.4202+0.0000j pulse amplitude by 0.998 to obtain 0.41927+0.00000j.\n", + "Amplitude reported by the backend 0.4062-0.1000j.\n" ] } ], @@ -652,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 32, "id": "billion-calibration", "metadata": {}, "outputs": [], @@ -662,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 33, "id": "aging-volunteer", "metadata": {}, "outputs": [ @@ -670,7 +670,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The reported beta of the X pulse is -1.0112.\n" + "The reported beta of the X pulse is -0.9640.\n" ] } ], @@ -681,7 +681,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 34, "id": "asian-anniversary", "metadata": {}, "outputs": [], @@ -697,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 35, "id": "continuing-arkansas", "metadata": {}, "outputs": [], @@ -708,18 +708,18 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 36, "id": "independent-extraction", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "
" ] }, - "execution_count": 22, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -730,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 37, "id": "excited-eleven", "metadata": {}, "outputs": [], @@ -740,18 +740,18 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 38, "id": "driven-fiber", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "execution_count": 25, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -762,7 +762,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 39, "id": "pacific-bible", "metadata": {}, "outputs": [ @@ -772,8 +772,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: @Parameters_FineDragAnalysis\n", - "- value: [-0.01021558 0.48055694] ± [0.00121587 0.00672626]\n", - "- χ²: 0.8762246416493179\n", + "- value: [-0.01416042 0.48915581] ± [0.00062282 0.0034207 ]\n", + "- χ²: 1.0082917682360732\n", "- quality: good\n", "- extra: <4 items>\n", "- device_components: ['Q0']\n", @@ -787,17 +787,17 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 40, "id": "geographic-terminology", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "-0.010215579158030891" + "-0.014160419499688572" ] }, - "execution_count": 27, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -808,7 +808,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 41, "id": "cutting-firewall", "metadata": {}, "outputs": [ @@ -816,8 +816,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Adjust β=-1.264 by ddelta=0.147 to get -1.117 as new β.\n", - "The backend reports β=-1.011\n" + "Adjust β=-1.205 by ddelta=0.203 to get -1.002 as new β.\n", + "The backend reports β=-0.964\n" ] } ], @@ -842,7 +842,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 42, "id": "adc1e987", "metadata": {}, "outputs": [], @@ -852,7 +852,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 43, "id": "7b15ae90", "metadata": {}, "outputs": [], @@ -863,18 +863,18 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 44, "id": "46785cdb", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] }, - "execution_count": 34, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -885,7 +885,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 45, "id": "adf28640", "metadata": {}, "outputs": [], @@ -895,18 +895,18 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 46, "id": "b1a1e889", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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EZWUl06ZNY/HixQCsX7+eZcuWdXpWRUVF7LXXXvlX0oZSSQ1///11O7eaZdi1fCPwI8iyZTppRb9+sNVWet8pp0BVFbzzjv5EgWXLtDlvwADo2ze5P2wCPxsbN+pOoFcvsCfMsmLRrM6s1OrlEohIh99x0aJFHH300YwePZrHHnuMOXPm8Le//Q0oLFDJqUm/vl7Xpbw8GexmJUb56qtlzJ8/n5/85CeUlJRQUlLClClT+PjjjykpKeG5554D4IorruDkk0/mzDPPZMyYMXz/+9/nhhtu4KabburS/ZALJSWdU4eISNr/a1tbO83NycFXRUUFZWVa2AB8/LE3/2+AYcOGdRpUlZSUbLYvV1pb9SCsuDj5PADGj9+LhoYNfPONlqK33XYbFRUV3HTTTYwdO5YDDjiABx98kFdeeYU333yTZctye3apDB48mFWrVqFsMdJKKVauXMngwZkzm++111588cUXHd9bWpLBh9W2NGqWwLf8+On87BWWfyzBo48+yi9/+UsmT57Ms88+y7x58zj33HN9C+j7/HPd/1ZXJxWVI4/U27D78aOQeMeQgt1/b00JLi+HM86Am2+GBx7QPvGwYwXspfZ/YRP42267LaWlpbz11ltss802ADQ0NPDRRx+x7bbbdkzHSY1R6tVLa5O5RCHPnj2b5uZmpk2bRnFxMQBPPfVUp2PGjh3LSy+95KjsljaZKX1naoS8NR3Pnv2sd2/te62oGMaH9rmgaJ/w888/z7/+9S9GjBgB6ABHqw4WxcXFnYSG11i3Sp0AYH2fMyf7/3v06NEdcQgWqd+9xtLgLWuRxeefz6NHj5707NkPyPw/Bzp83rk8u1QmTJhAfX09s2bN6vDjz5o1i4aGhk5+/VTmzZvHkCFDOr43NOhn0rt356yN69frAUtTk25TuQRFvv766+y11178/Oc/79j3lRUMBPTt25chQ4Z0BLiCHqS88847ncqUL9Z0vP33T/a/1rj5+ef1IK0kpJI1pMUydIVd4NvZbz8t8MMiKLNhtVG7OR/CJ/ArKys544wz+PWvf82gQYMYOnQoU6ZM6dBGMqXRtYRLLgJ/1KhRtLe3M336dE444QTeeustpk+f3umYSy+9lL333puzzz6b8847j549e/Laa69x+OGHs5Vl6knB0iZzzVpmCXz7odY4obm5lLFjd8YuV7bYYgt69OjRaT73sccey9SpUxk/fjx77bUXX375JVdeeSXHHHNMh4ZeX1/Pl4kRX3t7O4sXL2bevHn0798/Y12cYAVspwp86xkNG5b9//2LX/yCffbZhxtvvJFJkyZRU1PDv/71r6z3Xrt2bSdt0xokWK4CoEvt2M6mTfDqq0/S1LSc9vYJ9OrVi5kzZ3LzzVdx/PFnA3okcPTRRzNt2jSmTJnCySefTF1dHb/97W8ZPnw448aNo7S0dLM59+me3b/+9S8uu+wyXnzxRYYNG8bo0aM58sgjOeecc7jzzjsBOOecczjmmGM6XCv33XcfpaWljB07lqKiIp588kluu+02/vCHP3Rc1/p3pA5c/vrX6VRXj2Xo0AE88sgNtLS08OWXX7JixQrKy8vT/k+233577r33XmbOnMl2223HI488wiuvvEJVVVXHMRdccAE33ngj22+/PWPGjOH2229n2bJlrgh8uznfYpttYPvttfb/1lu6Lw4jxqQfQTIJfMsdG5X5+Nk0fMu/HwZuvvlmDjroIL7//e9z0EEHsfPOO3PAAQcAmQW+pcnkIvB32WUXZsyYwdSpU9lxxx25++67N8vMt9tuu/HCCy/w6aefsvfee7PXXnvxyCOPbGbSzpfWVm1aFensmiguTs46SJ2Pn44rrriCiy++mCuvvJIdd9yR//f//h+HH344d999d8cxs2fPZuzYsYwdO5ampiauvvpqxo4d61oUfDaBv+222f/fe++9N3/961/585//zC677MI///lPrrnmmqz3PuGEExgyZEiXn1xpboaSklL+7/9uZ8KECeyyyy78+c9/5oorpvDLX97SYQE4+OCDefjhh/nPf/7D2LFjOeKIIygtLeWZZ57ZzCTeFevXr+ezzz6jxZZF5uGHH2bXXXfliCOO4IgjjmDXXXflgQce6HTe9ddfz/jx49ljjz145JFH+Nvf/saFF17Y8bt1udRX9cgjv8tVV/2YE04YS2lpGb/97W958MEHeb2L1KHnnHMOP/jBDzjllFPYY489WLhwIRdffHGnYy6++GJ+8pOfcOaZZ7LXXnvR3t7Oj370o5z/D11hafipQt3S8kNt1k8XyReXT1yj9EeN0hGh8+Z13r90qd4/aFDh9/CjniedpMt7332d9y9apPcPHux5EXKK0s/GV1/p6ONEoHgHqZH6QbJhw4asx6xdq8tqC4LvwJqFsHixB4VzEaue1kyDdes6/97crPe/914AhcuDBQt0eW3B9mrDhg2d6pFh8kOo+PprXd6VK5P7APX3vz+m5s7Vv9lnHCiV2zvrFrm2+yVLdN/Uu3dyZoHFzJn6t7Fjnd3bROkbMlJfrzXj0lIYPbrzb1tsobWxVauiMR/fMumnavjDhun6LV8ejdS0mTT8oqKklh+FjHvpzPkWlk8/NQFPGFEq+d6kavglJdqC0damP2Enkw+/pES/X21t4cpXkYlMcS4izlxfQWMZHvbZB1JCJjjwQF2XuXN1HxxGjMCPGB9+qDu0HXfcvPEUF+u89JBMAhNmLJN+qg+/uDg5+2DhQl+LlBdvvfUaBxxQydChm0fB77WXto1HoTOzJ9xJpaJCd86NjeEXMC0tuozFxZubkEWS7SYkWVq7JJPAF0kOJqMwuLf+1+m8T5arPgptJJM5H7Sw/8539N9h7bdM0F7EyOS/txg2TK9jvmRJ0hceRtau1RHsFRXaMpHKyJHaArBgweaWjDChFIwaNZ6HHprHjjtuPuq3go7D3plZ870h2QHbsfz49fXJ9eXDimVN6dUr/bKrZWW6ri0tm1sAwkR7e2brEehBQGOjrkuaCRihIp2GrxJTKazMv1FYRMceoZ8OKxYzrAs1GYEfMXIR+BD+wD17wF66TjlskfqZaG2FHj16MXLkdtjywXTQv78OPgy7wG9t1UK/uFibitMRFc3YEhyZhLmlZYa9HnZhn+6ZWFp/2DV8y30isvmAGKKj4Tc0wAcf6Pcn07Rny8IaVoFvTPoRIy4CP9OUPIuoCPyuTJUQHf+kZabvKuDf+i3sy4DaNfx0RGXgkmkqm0VUBL594JJucN+zp96/cWO44yqWLtWD4uHDM79blsC3zcAMFUbgR4jWVj3CBJ1SNx1RE/iZko5FTeCnM7mC7pRF9HFh7swyTZuyY/0Wdh9+Ng3felZhH7hk8t9bREXgZwrYs4hKcKvlnrNnCkwl7Bq+MelHiC++0A1i6611Gt10REXg2wP2amvh8cf1qHjwYJg0KToCP9fOrKlJPzsHU6J9xYnAD7OgVCo+Gr4lyLsaTNqPCyvZrGCgn1VTkx6shbWNWFp7VzmTwu7DNxp+hMhmzofoCHxLw3/1VRg6FC68EK6+Wm+HDtXL5EL4BX42DR+iYdaPmsD/5ptvmDhxIjvuuCO77LILjz32GAAtLUJ7uy5rpvSmUfHhZ9PwLRN5S0sy0VAYyTYohmj48S2BbzR8gy+8/77eZjLng16LHcIv8C0N/x//6GzGa2jQ2zvu0B3zunX606+fzwXMkVy1Fwh3ZxY1gV9SUsL06dPZbbfdWL58OePGjeOoo45i0yYdFdZV9H1cTPrWFMNNm/QnrDMOnLSRMEfqWyb9rjR848M3uMY33+htV4ttWRq+FWASRhoakg0ik8+uqSnpKw6zlp+Lhu8kxW5QeCnwJ0+ezDHHHJNfwTIwZMgQdkuYuqzlcXUOe92ldSX8rOQ7ra3hjqtIDdprbdUJXZqb9VbPENG/hdmsn4uGn7qyZBjJRcO3BgPLl4ez/zUCP0JYS6enm7duUV6uteFNm5LLUYYNK0d+uohdO9ZUpDAL/Lh0ZrkI/OLiZJY6uwl58uTJiMhmH2ulvhkzZvDggw8CMHHixE6rnLnBnDlzaGtrY/jw4WzalF3g25PvFKLlv/rqqxx33HEMGzYMEeHee+/d7Ji2tjauvPJKRo4cSc+ePRk5ciRXXHHFZssE33777R3HjBs3jpqa12ht1W2gpERb7N5/Xw/6W1rgP/95lYMPPo799x/GHnsI9923+b3DQi4avj0gNKzuiVw0/F699FLf9uWAw4QR+BEiF4EP4ffjW+b8bCNgS/sKq8BXKrfOLAqR+pbg62pZT5HMWv6hhx7KsmXLOn2sVdj69u1LP498MmvXruW0007rWMmtpUWPIjOZwS3c8OPX19ez8847M2PGjM2WGbb4wx/+wG233cb//u//8umnnzJjxgxuu+02brzxxo5jHn30US644AJ++9vfMnfuXPbZZx+OOea7LF++mLIyba1bsUK/b5YwbGioZ9ttd+aii2bQo0evUM+cyGVQbH+3wlqXXDR8CLcf3wj8COFU4H/7rbflyRcrYC/bmtFWBxFWgW8lqykpSZ9QxMItjdJLctHwIfnMUuvRo0cPBg8e3OljLYVrmfQnT57MK6+8wm233dZhBViYIQfpSSedxIABAzotWTt//nzKy8t55JFHANi0aRPHH388v/nNbzrWZm9rK8qpHm48j6OOOoobbriBSZMmUZQhW9Gbb77Jsccey7HHHsuIESM47rjjOO6443j77bc7jpk6dSqTJ0/mrLPOYvTo0fzpT3+iunoIjz/+Z8rKtKBJ1Xr33fcozjvvBg45RN87rO9Ve7vzdyusAj8XDR/C7cc3Aj8itLcnF2QYNKjrY6Oi4WfDsgCEVeDnot1bZBKUYaC9PZkJLdsgrBAtbMaMGUyYMIGf/OQnHVaA4cOHpz12+vTpnHLKKVx77bWAFu4nn3wykyZN4oc//CFKKSZPnszBBx/Mqaee2nFeW5vW8FPrccMNN3Ra42DMmEoOOKCSrbZK7nvNypvqIvvttx8vv/wyn376KQCffPIJL730EkcddRQAzc3NzJkzh8MPP7zTeQcccDgffPAmSmV3fenruF50V7AL+2z1CFNQaCpK5a7hh3lqnonSjwi1tbpT7teva9MYhF/gWxr+pEnwxBPpI3PLy+FHP4K77gq/wM/2PCDc5kq7OT/fTvmZZ56h0pbQff/992dmysLgffv2paysjPLycgZnUZOGDBnCxRdfzK233sqiRYuYPn06GzZs4LbbbgPgjTfe4NFHH2WXXXbh3//+NwD33fcA7e1j0qZw/elPf8oPfvCDju+rV+sOuX//ZHsZZv3hIr/+9a+pq6tjxx13pLi4mNbWVi6//HLOPffcRDlW09bWRnWKFKmqqmbNmhcQyc2n3dZGzoMDP8nFnG8RZg1//Xrd3nv3Tr/WhJ0wm/SNwI8IuZrzIToC/+qr9YyDm2/WHXRjo25MbW1w0UVwySVa4C9cGM7OzInAD3NnlqvJ1X5MqsA/4IADOvzoQEafthNGjBhBv379uOmmm7jzzjt59dVX6Z1Yu3e//fajPUUSNjfrTJTpBi79+/enf//+Hd8HDNDH9e3b9ayXQnn00Ue5//77efjhh9lpp52YN28eF1xwASNHjuSMM87IeJ4V69Gjhw7cy0Xot7Tk9i76SVysYLlq92AEvsEF4iLwm5th0SLdiY0cCdddp4W7PdPeiScm590PGKCjXVesyO4785u4aC+55NG3yCTwy8vL2c4Dybnrrrty++23c8011zBhwoQuj+0q8PCGG27ghhtu6LTPEqKW+33mzJnsn2kZtDy59NJLueSSS/jhD38IwJgxY1i0aBE33ngjZ5xxBgMHDqS4uJgVloM4wYoVKxgwYDADBiRXk8vGpk3hE/hO2kiYrWC5+u8h3D58I/AjQlwE/qJFuqPdeutkJHVVFZx1VvrjR47UAn/BgvAJfCfaS5j9k040/EK1sLKyMtocTFVQSrHTTjtxxRVXZD22q4FLqkm/uRk++0xblnbcUe/zwqTf2NhIcYp/obi4uMM6UVZWxrhx43j++ec58cQTAW3NmjXreQ466H/o1Uu/9ytWpNfy7bGCGzdqk3OYyEfDD6PAd6LhGx++oWDiIvDtOfRzYeRImD1bC/wsCp7vGJO+c0aMGME777zDwoULqayspH///hkj3G+77TZeffVVdthhh82EZjqs/206DT/VpK+UToSkFGyzTeYlgbuivr6eLxMvdHt7O4sXL2bevHn079+frbbaCoBjjz2W3//+94wcOZKddtqJuXPnMnXqVE477bSO61x00UWceuqp7Lnnnuy7777cdtsdrFq1lB/84KcUF+tU0/fccyt3330r//ynDv7buLGexYu/ZOBAUKqd5csX89578xg9OnnvMJBPnEsYB8X5aPhhFPgmSj8iOBH4gwbpxrN2bfiyuy1apLfbbJPb8WFeRMcIfOdccskllJWVseOOOzJo0CAWL16c9rhPPvmESy+9lPPOO48vvviCxhxyruaSS8Ciq5wCuTJ79mzGjh3L2LFjaWpq4uqrr2bs2LFcddVVHcf86U9/YtKkSZx77rmMHj2aiy++mLPOOovf/e53HcecdNJJTJ8+neuvv57ddtuNN954nenTn2bkyK07ytrauppFiz5j+HBd7tWrZ/OjH43liCPGsnFjE3feeTVHHtn53mEgLm6vfHz4xqRvyBsnAr+oSGsFixbppB25atN+YE0tzKXhQHgFvlL5dWZh1F7yFfhWIGW6DHN27L9vv/32zJo1q8vjN23axCmnnMLxxx/PTTfdxJ///Gc++OAD9t577y7PcxKLAPq5NTfrT7ZEPemYOHEiKkv2qN69ezN9+vRO+QTSce6559oi93Wgqr1M11xzDddccw0AdXVw/PHJe9fVafdERQWMHu28Hl4Sl6A9Jxp+v3762dXV6TTiYVr9z2j4EcGJwIfwmvWtAKQBA3I7PqwC3550JxdzcJgDkpwI/OJiXV+lvMsa+Jvf/Ib169fz5z//mfLyckaNGsWMGTMyWgMsujLppyOsq+al5tDPRlg1Y/ug2MlgMmz1AGcavkh4/fhG4EeEXJPuWIRd4A8cmNvxYRX4Tsz50LlTDtuiGk46ZftxXnTMzz33HLfeeisPPvggffv2BeDyyy/npZde4vTTT+/yXKf1sJ5d2AS+03pY71bY0jZb73pxcdeZKC2KiujIOxC2ujjR8CG8fnxj0o8I+Wr4YUuvay0okavAtxqYVf+w4MRUCbozs+ZTt7XlroV6jV0Lc6IZb9qkz7NWAnSLww8/nJYUm+6pp57aKZteJpxq+GFNd+y0HqmDybDkq3Di8oJkXEVzc/i0fEvDdyrww+bHNxp+RIibST9Xgd+7t+4EGhrCtdqcUw0fwmmytDK0FRXlpoVBeKOp42LSd1oPe0rkML1bTgfFEM56tLc773+NSd+QN83NOrVuUZFOBZoLcRH4Ikl/f5iWm3SqvUA4O7N86hFWge/UUhFWk75TgW8/NkzvVj6D4jAG7tXW6vL065e7RSusJn0j8COAJSQHDcp9vnBcBL792DAJ/Lh0Zk6FpP3YMNWjvV1/RFTOloq4mPTtx4ZJ4BcymAxTPSz/fa4zi8AIfEMBODUnQTgFflOTNs2XloJtnZWshFHDj4tJ32mAmP3YMAlKqyzFxSpnH7a9HrnkqvcDpeIj8Asx6Yfp3XLqvwfjwzcUQD4Cf+hQvV26NDydmT1gz0lgkSXwc80p7gdx8U/GReBb/9Pi4tynQLiRfMdt2tuTMRVOsv+F+d2K+qA4Hw3f+PANeZOPwO/VS/v7W1uTU/qCJh9zvv34sGj4TpPuWIRRe3GarMZ+bJg65XwEPoTPj5+Pdm8/PkzPxGj4RuADICLnisgCEdkoInNEpMslqkTkFBGZJyKNIrJcRB4UkZAtpeId+Qh8CJ9Z3+mUPIuwafhWlrlck+5YhFFQxkXDT8Yi5Cfww1KXOAn8uGj4TpLuWGyxhbYgrV4dnncLApiHLyInATOAc4HXE9uZIrKjUmqzVFoisi/wAHAJ8G+gGrgdeAg4xKdiB0q+An/LLeHDD7XA331398vllHw1fD99+EOGDGH8+PFdHtPcrEfuZWXJkXwuNDXpZ9mzp7POww02btxIzzQhxitW6OmOW2yhrUK5oBRYSe+23trFQhbAhg06mrq8vJVBg3Lv1mpr9blVVdCnj4cFzJFc3pF0z7K+XrePysrcs1h6ifWOiICTtXzsbauqKv076wVDumjITpPugB6ADRqkn+XKlUnlK2iCSP9xEXCvUuquxPfzReRI4GfAZWmOnwB8q5Salvi+QET+BPzJ+6KGg7ho+FEw6T/55JNZj3nmGfjud+Gww+C553K/9pw5MH487LqrXgHQT2pqapg4ceJm+8eMgY8+guefh912y/16Awfq5/HRR/4PXtLx61/DTTfBmWd+zV135bgyE3DzzXDppfDjH8O0admP95qHHtJl+eEP4f/+L/0x6Z7lf/4Dxx8Pe+8NObzCnvPppzqv/3bbOXvXFy/Wg8hhw+DWW9O/s36Tj4YPWhlYuVIPYMIi8H016YtIGTAOSO0mnwP2yXDaG8AQETlWNAOBHwJPe1fScNHdBX7YTPpr1+ptrjkRLKy0yGGJqYD8/JP248MShWy1kX79nNlPrWcSlviQfN1eYYtzsTJ8OhV01vNYuTI8Kajz0fAhnH58vzX8gUAxsCJl/wrg0HQnKKVmicgP0Sb8XugyPw+kTawtImcDZwNUV1dTU1PjSsEt6uvrXb9mNr76anegD99++x41NRtyPq++fgiwA7NnL6Om5jNH9/Sinu+/vx2wJWvXfkFNTe6jkMWL+wC7s2DBBmpq3nO1TPnU8623hgGj2LhxCTU1X+R83qZNRcABrFzZzssvv+prCtR09WxtFVavPpCiIsXHH7/Cp5/mfr0ePXYFqnj22fepra11taz58OmnY4AB9Oy5wdHzXLJkADCGzz9fQ03Nh14VL2dmzx4BjKCubiE1NQvTHpPuWS5e3AvYi2++aaSm5h2PS5mdl16qBkZTXLyCmpr5js7t1Ws/mppKWLlyo+99bToWL54A9ODrr2dRV7fJwZk7AEN45ZXP6N07s9T3VaYopXz7AEMBBRyQsv8q4LMM5+wILAEuBXYBjgA+AO7Pdr9x48Ypt3n55Zddv2Y2tt5aKVDqyy+dnffUU/q8I45wfk8v6vnDH+ryPPigs/M+/1yft802rhcpr3pec40uzxVXOL9fZaU+d9065+cWQrp6fvutLsvgwc6v96Mf6XPvvbfwsrnBnnvq8tx66xxH573+uj5v7709KphDzj1Xl+dPf8p8TLpnuXq1Pq+qyruyOWH6dF2e8893fu422+hzH3jgLfcL5pDWVqWKi3V5Nm1ydu5ll+nzrr226+O86GuB2SqNTPQ7Sn810IYOvLNTDWQyDl4GvKOU+qNS6gOl1LPoQL9TRWRL74oaDpTK36RvmcJDoIABhUfph8VcaZUjn+Aou8kyaPI159vPibpJ33qGlpsmaJwuH23Rr58OkKutDUeEu9VGnLq9INnP1dY6mDriEWvW6PUm+vd3NtsAwmnS91XgK6WagTnAYSk/HQa8meG0cvQgwY71PfZ5BBoadORuz57OstNBsrGFRVDm68Pv109Pf1u/PhxTXPL14UO4/PhWRxQHgW/9P/v1czahPmxtJN9BcXFxsi5hGOC70UbWrXMoYT0gX/89GIFvMRWYLCJnishoEZmBNvXfASAi94vI/bbjnwS+JyI/E5FtEtP0/hd4T6WZxhc3rI7MmtfpBKuxhU17cdqZ2RcNCkNdrDLko+Fb2ksYBL4lrJ1MLbQIk8BvbNQD47IyKC93tpB6VZXe1taGIyNlvho+JNtVGIJb3Wgj69YFr+HnG6EP4Uyv6/u0PKXUoyIyALgCGAJ8BByllFqUOGSrlOPvFZHewM+BW4D1wEvAr/0rdXDka86HZGe2bp02S+W6qIhX5CvwrXNWr9YaUNDTwAoxV4ZJw4+LSb+QQXFpqZ5/v2GD/vTr53rxHJGvhg/hms1SiIafFPjR1vDDmF43iHn4KKVuRyfPSffbxDT7utW8ezuFCPziYt2BrVunzeH5ND63aGxMuibKy52fHyY/fiHai/Hhu48l8K3/rVP699fCfs2a4AW+Gxp+mNpI1H34bmn4SjkfjHpB7H3gUacQgQ/hMYXbO7J8XvwwaS/5avi1tfD11/rvl14K3tcaFx++1UbyFfhhCdxrbNRZD/MdFIfJpO+GFSzqGn55ubYeNTcH/25ZGIEfcuIi8AsxVdrPC1p7aWvTFhPIXRtUCq68Uq9g+MQTel9Njf5+5ZXBJRgpxIffv79OH7punRZSQWJp+OvX60HMXXc5G0yFJXCv0EFxmAS+Oyb98Gj4+Qh8+3lhGBiDEfihJy4CvxD/PYRHw1+3Tgvofv1yX+Dkqqtg6lQtGK0VxNra9PepU/XvQVBIZ1ZUlDRzrkhNo+UjSsEDD+i/58zRy0FfeKGzwVRY2kihg+KwtBFrUCySn4skTAI/n6Vx7VhtK8g2YscI/JDjlsAPi/YSdQ3fqeZSW6vztTc2pv+9sVH/blkN/EKpeGgvV10Fr7yi/7bmnzc0OBtMhcWkX4j/HsLTRuwWsHwChcMUtFdoGwnTVEkwAj/0GA1fE5agPadJdx5/PHunV1wMjz1WWLmc0tioP/nkd7AIWuBbg6lMiWZyHUyFZVDsltsraA2/EHM+JOuxfn1p4FMlC9XwrZlSQfe/Fkbghxwj8DVhMVc67cyWL8+s3Vs0NvovNO31yDd62HongzJXujWYClsbKVTDD7qNFBKwB3qqZFUVtLdLoM+ktVXHh4gUNgMEjIZvyJFCBX7YzJVxMenn2ikPHpw94rq8PH+TYb5YHVAhUzWD7szcGkyFpY3ExYdfqIYPyf4uyOmrq1dr19fAgbnH66QSlsGkhRH4Iaa9PRmBnG8nEJYXrlDtJSydmVPtZdIkHcTUFW1tcOKJhZXLKdb7YJkc8yFoge/WYCosJv24+PALyVNhEYZ8FYUqW2BM+gYH1NZqYdC3L/Tokd81wiLw3dJewtKZ5Srwq6rgkksyC6bycv273wlf3NDw7Wlpg8CtwVRc2oi15kTQC+jERcOPgxUsFSPwQ4wbI8ywdGaFmvTtDSdbJ+8l+ayUN2UKXHSRDpCrqEjuLy3V+6dMcbeMueCmhh/Uu1VVBb/4Rebfcx1MhcWkX6iGX1wcDo2yUB8+hGPNiTi0kVSMwA8xRuAnKSnRHXd7u/9T2Ozko72IwHXX6Tni06bB2LF6/y236P1BpNyMg4YP8NOfJv+2BlMVFXpwletgKiwm/UI1fPu5QdbFaPhJwtBG7BiBH2LcFPhBdgBKFa69QDg6s3w0fIuqKjjrLDj0UP29vt69cjnFDe0lDNqkpQHuuqseTA0dqrfLluU+mArLinlutpEgY13cEPhh8OEbDd/gK/ZVwPIlDJ1ZQwNs2qRNrPnkCLcIgx/fzc4sSHNlXPyT1v9w8GA9mBoyRG+dxESUlOg4GaV0et6gcFPDD4PAL2TgEjcN3wh8Q1bc0PBLS6F3by3sN2xwp1xOKdScbxGGSP1CNHyLMAj8uGn4hbQRCN4StnGjHhiXluafCAnC0UbcNOmHYVBcSBvp21dbmerqoKXFnXIVghH4IcYNgQ/BByW5ISQhHCb9uHVmhdTD6sw2bAguKrzQlfIsgja92rX7QmI6wtBG3Azai7pJv6goeX6QsUcWRuCHmLh0ZnHR8Ftbtck330VBLOLinywu1kIfguvM3NLwg3YXueG/h3CZ9KPuw3djUAzhCtwzAj/EuKUZx0XgB6292E18RQW0nDCY9N3qzIL248dlUOyG/95+flACP5/lo9PRvz8UFSlqa5MrTPqNGyZ9CP7dsmMEfoiJywvntoYflMB3Q3OBzgI/l+VbvcANDd9+flAC3xo0FSrwg3Z7uaXhB91G1q93vnx0OoqLobJS+4mCerfcbiNG4Bu6xG2BH7S5MuomfbcsLhUVerbCpk3BTM1ra0tGoxea4S/owaRbcS5Bt5G4aPhuDYoBevfWUW5BCfy4WMHsGIEfYqzGc/fdcNdd+b8wQXfKcTHpu9mZBemjtLSwvn3zW6/cTtAaftzcXlH34bsRsFdbq/u7Hj20hr9okQsFc0h7e/Kdjvqg2I4R+CFEKbj88qT2d+ONcOGFOqnIlVc6NwMH/cIZDX9zghy8uKW52K8R1LvllhUsaJO+Wxp+0G2kkEGxUrp/GzpU93clJVrgH3tsfv1eIWzYoO/Xu3dhrgkIflBsp8CqGLzgqqt0tjA7DQ16O3Wq3l53Xe7XC7pTjsu0PDc1/CA7Abd8k/ZrBFEPL1wTQbu9Cm0j1gI669bpWSWFCiunFNJGrrpK928bN+rv5eVa4Le05NfvFUKcBsV2jIYfMmpr4eaboakp/e+Njfp3J9OggtZevAjaCyLYzY0MYhZBCko3O7MgA5KsRFJ9+hTumgi6U3ZLwy8uDrYu+bYRq99rbEzu69Urmakmn36vENwcFAf9btkxAj9kPP549s6ruBgeeyz3awb9wrkl8MvKdBay1tZgsga64Z+0CIOG76b2EuTAxY1OOSyDYjfdRUGY9fN9t9L1e716dc7m5LTfKwQvBsVhMOkbgR8yli/vPMpNR2OjPi5XghT4bi2cYxGkWd+Y9DcnSA3fTYEftEnfLQ0fgp2al++gOF2/lyrwnfZ7heDFu2U0fMNmDB6cfYGZ8nJ9XK4E+cJZOaQrK/WSpYUShs7MmPSTxEXDt2IA1q3TsQF+0901/HT9nuXDT3531u8VQlziXFIxAj9kTJqUvcNpa4MTT8z9mnYtzG/ft1vmfIsgo5CNhr85cdHwS0q00FfK/zTBzc16YGxPVVwIURT46fo9uw8fnPd7hWCC9gy+UFUFl1yiV81KR3m5/t1JVHKPHjrZS2ur7lj8xE2tGII16btZF+v5GQ0/f9wU+BBcx2x/rwpZOMciDALfaRux+j27lm836efT7xWCV4PioDJrWhiBH0KmTIFx4/TfpaW6E6io0Cbxiy7SvzslqM7MaPjpiZuGbwR+/rjpv4dg3V6FtJEpU3T/1rOnlY1SC3yR/Pu9fHHz3erVS9eppSV7fJbXmHn4IUQEdtoJ3noLTjoJtt9e+65OPDH/EW7//vDNN7pBjhjhZmm7xm2BH5SG77bZNQyC0o2BS0WFHpQ2Nen5027EaeSK2wI/KEHppv8egtXwC5nJIqLn2V90kY7ab2rSAn/IEP/m31u42Uas6yxdqvvfigp3rpkPRuCHFOuFO+44d/xWQU078krD97tTtgsXN8yucdHwRfR1Vq7UdRkypPBr5orR8NMTlMC3p6Mt5JlUVcFZZ8F//6t9+FZyJT9xs41Y11m6VP9/hg9355r54MikLyJlInK1iHwqIo0i0pbyac1+FUMuxK0zc0t7Ccqk72bSHYiPhm+/jt/vVlzaiNsaflBtxFqjoU8fdzL8lZe3UVyss4y2tGQ/3k3i8m6l4vSx/BE4D5gJ/BPY5HqJDID7I8y4dMpBmfTdTLoDSbfAhg06+rjQTHFO8EJ7Af8HL3Ex6Xul4ftdD7cHxSLahblmjX7Wha6I6IS4DIpTcSrwJwFXK6V+50VhDEniMsL0qlMOSsN3qwOwYgHWr9cft66bjY0btb+9tNQ9X2Jc3q24aPhBDcDcHhSDrksQAj8ug+JUnEbpVwKzvCiIoTNedWZB+r7dIGgN361OGYLpBNyORbCuZb+2X3g1mIy628ueRMjPaWBuD4ohmHerpUUH6BYVafeEG4RFw3cq8J8EDvCiIIYk1nx5EXciwiF4Lcyt+bNx0fAhWIHvRT3iouEHNSh265lY1pu2Nn/zbsSljViJl6yVB90gLBq+U5P+n4D7RaQdeBrYrIkrpb52o2DdGS9euKAEvlUXtzrl8nKdSGjTJm2a7tXLnetmIy4avtumSggm+U57e+d24gZBD4rdfCZVVTrYbd0697TUbLjtw4fgrWBuEVUNfxYwCrgGeBv4Is3HUCBxeuG86szs1/aDuGgvcdHwN2zQ5urevd1b8z0ok75pI5mJSxsJi8B32lT+H1CwV0hEzgUuBYYAHwO/VEq91sXxZcAVwKnAUGAFcLNS6n8LLUsY8aIDCKIzU8p9k751reXL9bWHDnXvul1hNPzMBKHhezkoDsqk72YbCeLd8ipoD6LfRiJp0ldK3VvoDUXkJGAGcC7wemI7U0R2VEotznDaI8CWwNloK0I14JMx13/iouE3NuoAmJ493c3AZv1f/FzkxGgvmYmL2bVfPx03Y62Y59dUSbfdXhDMWg1xayNR73/TkZcxTEQE2BHoj/bjf6JUzvGgFwH3KqXuSnw/X0SOBH4GXJbmXocDhwDbKqWsUK2F+ZQ7Knhp4rMWcHArQrsrvOjI7NcznZlzvNTw/ezMvGgjxcVaUNbW6o9b8+K7orlZD4xLStxNuWraSP7E2aTvOCRMRM4ElgEfADWJ7VIROSOHc8uAccBzKT89B+yT4bTjgXeBi0TkWxH5QkT+V0QqnZY9KnjRmfXqpT/NzTqYxw+8qIf9ekGYK6Nu0jcaftf43TF7MU3Suh4EYwWLehsxJv0EIvIj4E7gReBBYDkwGPgRcKeINCql/q+LSwwEitE+eDsrgEMznLMNsB86q9//AP3QswWGohMBpZbxbLTpn+rqampqanKoWe7U19e7fs1U5szZCtiGurrF1NS4N+mhomICTU09+O9/Z1Fd3XWSRDfq+cEHfYGxFBWtp6ZmbkHXstPQMAoYxjvvfMHw4UsKulau9Vy1an+gmI8/fo2FC9uyHp8LS5cOAnbi889XUlPziSvXzIRVz/nzRwPVLF8+n5qa1GaYH2vXlgL7smJFMzU1b7pyzWy89dYQYAc2blxGTc1nHfsLfW9LS3cH+vDCC++xdOmGgsuZjcWLy4E96dGjkZqad3I6J5c6rlu3NTCSefMWUlOzsNBi5sSSJXsC5Xz55Ts0Nxe+LFx9fT2LFs0DdmPBgnXU1Mwr+Jq58MEH2wLDWbv2K2pqvnHlmm1tIHIg69cLL75Y08ld5IdM6UAplfMHeB94IMNvDwDzspw/FB30d0DK/quAzzKc8xzQBPS17Ts8cZ3qru43btw45TYvv/yy69dM5ZJLlAKlfv97d687Zoy+7ty52Y91o57/+Y++3zHHFHypTlxxhb7utdcWfq1c6rlxo75fSYlS7e2F39Pi2Wf1dQ85xL1rZsKq53e/q+/51FPuXXvTJn3N4mJ3/z9d8Yc/6HtefHHn/YW+t0ce6f7/pytmzdL323PP3M/JpY7/+7/6uuedl3/ZnDJggL7nihXuXO/ll19W772nr7nLLu5cMxdOP13f869/dfe6/frp665e3Xm/FzIFmK3SyESnJv0d0Jp9Oh5M/N4Vq4E2dNCdnWq0tSAdy4AlSin7mknzE9utstwvksTNXOlm9DH4bx6z+ya9MLtG3VxZVuZ/ope4tRG36+F30J5bK+WlYtxF7uJU4Neho+XTsWXi94wopZqBOcBhKT8dBmSyBb4BDE3x2W+f2C7qsrQRxasXzu+peXEJ2vPCfw/x8eGD/3WJS6fsdZyLXz78DRu00O/dW2f6c4s4tZEoCvyZwA0isr99p4hMAK5P/J6NqcBkETlTREaLyAy0qf+OxLXuF5H7bcc/DKwB7hGRnURkX/S0vseVUisdlj8SmM6sa/zWXryIPob4aPj260X93fJ7xby4WcHcHhT37q2zjdbX+7dErtdtJMjAPafT8n4F7A3UiMgStLl9MFq7/zLxe5copR4VkQHoRDpDgI+Ao5RSlra+Vcrx9SJyKDpQ712gFvg38BuHZY8MRuB3jd/ai1cC3+rk16/X2pFbaZQzYU+E5NW7ZTR8Z8RlJotXbaSoSLeTtWt1ex80yN3rpyMu71Y6nCbeWS4iu6Ez7u2Pnoe/EHgFPbc+p9BMpdTtwO0ZfpuYZt9n6EC9bkFcXri4aC9ePY/iYp3nfMMGLfTdvn4qdXXaz15Rof3ubhKXZ+K3hu+V28tvK5gXWfYsqqp0n1Vb64/Aj5NFLxXHiXcSQv3WxMfgAV77kPw2V0Zde/GqHtY1N2zQHb/XAt+rjsx+TaMZO8OPeviRaMvLd8vPZ7Jxo/6UluqFutwkDBq+x0ZEg1Pa2rQAEHF/lSu/X7i4aC9eC3z7PbwkLvWw38e8W+np1UuvKtncrAWY18RF4HuVCAnCIfCzavgi8jXwfaXU+yKygK4Xz1FKqW1dK103xIulcS3iooX17q3N4Q0NOpDHzajgdHgpKP0UMHHR8JVyf2lcC7/jQ7xye4Gui7XIlNfLSMdF4MelHpnIxaT/CrDB9neuOfMNeeBVhCjEx4cvoq+5Zo0/gTxx0YzjUo+6Oh3kWFnp/mDP707ZKysY+LuqZFzeLS/rEQkNXyn1E9vfkz0tjSE2Dcd+H6/qsmaNP4E8XmmTEB/txc/OzK824ofvOy7t3WtLhf0eXuJV/JT9mkFq+I6MxiJylYikHSuKyBARucqdYnVf4tIBbNoETU3urwJm4acpPC7PxNQjOz16aPN3a6s/i0z58Uz8cE94aakIYlDsZT2iFLR3NZkz7Q1N/G4oAC87gMpK7ftubNTBPF5i7wC80JLiImCMhu8cL5+H/bpePxMvA3QhPm3ET8047iZ9pwK/q667Cr2inaEAvHzhLN+3/T5e4Ven7If2EheBb+qRG1Yb8frdsq7ft683SZfi8kziMihOdRcFQS5R+hOBg227zhGRY1IO6wUcDXzsWsm6KX4ISivYrTp1CSMX8dKnB/51Al5mp7NfM+qdmdHwneOlGRyM2ysfvKyHNVXScne6Pc8/F3KJ0j8QnQYXdIT+T9Ic0wx8AvzCpXJ1W+LSmfmlhXldj6Ym7f4oK4OePd2/flw6s759tQVpwwZtqrav9+02po3kRlzerbjUQ0QPjJct0/cJQuBnNSQppa5VShUppYrQJv29re+2T0+l1O5KqVneFznexKUT8Fp7CaIeUY9F8FLDLyrSQh+8N4XHxXrkVz28fh4tLXpxm6IinSPDbeLSRiD4wD1HnqOEcH/Hq8IY4iPw4+LDj8vzsN/Dq7r4ZdaPyzOJSz1MLELuBB245ziXvoWIbAFsZuRUSi0uqETdnLh0AnHTwqL+PMA/7SXqz8Qvd1FcfPhe18NaIreuTk+XLMlbamXHr/YeCYEvIkXode/PAfplOMxD7138iYuAiUun7GdEuJdL5La2CnV13k0Bg/hp+MZ6lBte1yN1idyBA725D3g/KA46+Y7T7uWXwHnALWh//g3oAcAC4CvgLDcLFyZqa+Guu3TAxV13effAvMz0BPERlHGxVJSW6vwI7e1ag/GK+no9tq+q8m5QERcBE5d3Ky7Pw35tL+vi9Ywc+3WjIvB/AkwB/pD4/i+l1NXAaGAJsJWLZQsFSsGVV+pc1BdeCEuX6u3QoXq/2/Mp4/LCxTFozyv8qEtdXVLge0XcNPyoC8q4WCrs1/bymdTXa5dBebmePucFURP42wCzlVJtQCt6/j1KqRZgOvD/XC1dCLjqKpg6VS8xaaXabGjQ36dO1b+7hdeZtyA+nUBc6mG/tpd1qasr7XQvL4iboIz6YNLKrGmtKukVcRH4calHVzgV+OtJBuotBXaw/VYCeGSIDobaWrj5Zp2KNh2Njfp3tzpqL5fGtYiLudKaArZ+vTaHe0VcOgFLw/fKVQTG7OoUr+vhV2bNuLSRuNSjK5yKlbnAjom/nwWuFZGTReRE4EbgPTcLFzSPP755ApGmps5xjsXF8Nhj7twvTi+c13UpLtZWEKW00PeKuDwTP0z6fkQg19drS1h5uU6G5AV+x7l4NSgGfwWlH/Xw8t3ycuEci6gJ/OmApe9eDSwHHgIeBUqBn7tWshCwfPnm2v3TT4/s9L2xUR/nBnERLvbrR70ucemU/fThx0ULM+6i3IhLnIvXAdMQvMB3NC1PKfW87e/lIrInsC1QDsxP+PJjw+DBWpOwL5PZ1FTa6Zjycn2cG8RFSLa24vkUMNB1WbTIdGa5UF8fDx++H22kvFzPnti4UX+8SKkM/tTFmPRzJy716IqCPMVK86VS6oO4CXuASZO0+dBOY2PnMVJbG5x4ojv38+OF69NHC2IriYUXWCZ2rzJvWZhOIHf8NOlH/XmIeF8XpTrH7HhFXJ6JH/Xw06Qf2sQ7InKAkwsqpV7NvzjhoqoKLrlER+Nbpn27wC8vh4sucq/B+tFwrJzn69Z5l8TCj3qA0V6cYAS+M6qqYOVKfb8hQ9y/fl2dDjatqNDWBK+IyzPxuh61tfDyy/rvr7/W372oj70eSnmzPkdX5GLSr0GvkpcNSRwXq0x7U6bo7U036VXTmppKqajQmv1FFyV/dwM/RpjW9det0y9dlAW+6cxyx5qW54d/0kvtJS6DSdNGnOFVPZTSU6tvvjlp8Zw5U+dZueQS3b+7KZR79NCKYmOjDkD1YrGhrshF4B/keSlCjAhcdx2cdhpsvz1s2lTCtGnajO+2Kc7PTmDBAu983353Zl7Vo7lZN8ziYj2n2SviouH37q3/V42NySWF3SYu71Zc6gHRDmy151mxaG7W26lT9fa669y9Z1WVbiO1tSEU+EqpV/woSNgZmQjOb2go4YwzvPFNx2XU70egm/36XtejXz9vTW/+BO15L/At3/fq1bou1dXu38O0EWd4baloa0vG7ERN4Ft5VuzC3o6VZ+Xii92tW1UVLFmi77+Vz7lpPQypihclJXo0ppSwYYM394hLZ+bHiN9+/aibXf2IRfBDw7dfP+rPJC5txOt6WH1hnz6b5yxxEy/qkS7PSipu5lmxCDJS3+lqeS9lOUQppQ4poDyhpqpKB9vU1nrTUOPWmZl65IafPvyo1yUuz8TUwxmps4vcWCI3XZ6VVNzMs2IRpMB3quEXoYPz7J+BwL7A9onvscXrxUFMJ+CMONbD7cWYQPskN24sprjYe5+haSO5ERcfvl/1sJbIBffqYuVZ6Qo386xYREbgK6UmKqUOSvnsgk63W4teLje2+NUJDBjgzfUtTGeWG37Vo6xMdyxtbTpy123s9fB6GlBc3i2v3Sxx8eH79TzA/UyO6fKspOJmnhWLyAj8TCilvgJ+D/zRjeuFFa8flF/T8vzqzKLuw/erHuDtu+VnpxwXge/XYDLqPvwov1tWnpVMWn55uf7d7WcUeYGfYBXarB9bvDRXtrRo/5S1KIyXxKUTiEs97PcwAr9r4jIDxK9nYl9VMps2mw9+DVzAm/53yhSdT8U+hbSiQqdTdjvPikWQ2fZcEfgiMgC4CPjKjeuFFS87AXtH5pfZNeqmcK9933ERlHGph1L+JqeC6Av84uLOQt9tgjDpuykorTwrzzyjvw8eDNOmwbJler8XfXGUovQXsHnWvTLAmnH7P24UKqx4+aD86sjs94h6Z2b5vhsb9QJHbifHibJ/0k7UO2WL+nodoV1RoTOWeUlc2gho7Xv9ej3AdzvTol8WF/C2jVjWj9Gj4ayz3L++HT9WlcyE08kNr7C5wN8ILAIeS/jyY4uXD8rqIL1MfWrhZ8Iar+nXL5m1ygj89MRFw7eu6UcbiVt8yKJF8Xm3vBhMxqWNZMPp8riTPSpHJPDjhYu6wG9v978zW7pU12X4cHevHXX/pEVc3i0/B8W9e+upYA0NOr7G7QVu4iJg4mI9isvzyEZBPnwRGeRWQaJAXEz6dp9ee7u717ZWAaus9HYVMAu/4iq8xsvOLAh30ddfax/oXXe592z8rIcX874tlIqPgImLwI+ThbUrHAt8ETlQRF4RkSZguYg0iUiN02V0o0hcTPrJNMHuB/L42QHY7+NFAGJcOjO/6qEU3H23/nvZMrj6arjwQr3y2JVXFh5Y6WcbAe865qYmnQypRw/o1cvda6cjbgI/6vXwOti4KxwJfBE5EXgJ2AI95/4XwM3ooL2XRGSS6yUMEV6a9OPSmfkt8L30tRqB74yrrkoKfKX0p6FBL04ydar+vRDi0kb8dHnZ7+PloNjPXBVRbiPgfaKtrnCq4U8B/gvspJS6Sil1m1LqSmAn4Bkgp4UEReRcEVkgIhtFZI6I7J/jefuJSKuIfOSw3K4Ql5Gy/T5R78y8qkdbm14YRCTpAvGSqHdmtbV6ZbGmpvS/WyuPFSJ04iLw49LW7deMy6A46u9WNpwK/JHAn5VSnTy/ie+3AyOyXUBETgJmoNPwjgXeBGaKSJcLBYpIFXA/8KLDMrtG374gotiwQU8PcpOgOjO3R/1x6cys/0ufPt4shZxK1DV8P1Ye87uNeGU9iksbUcrEueRLVAT+F0CmQL1BwJc5XOMi4F6l1F1KqflKqfOBZcDPspz3V+A+YFauhXWboiKorNSS3m1BabSX/PBa4PtVj6hrL36sPBaUFmYGxemxAnQrKvwP0HXb9x3UM/E7255TgX85cK2I7GHfKSJ7AdcAl3V1soiUAeOA51J+eg7Yp4vzzkXHCVzvsLyu07u3FvhGUKYnKB9+1DvlqGsvfqw8FrdBcdR9+H63kZ49dZBjc3P2waVT4tL/ZsNp4p1LgZ7AWyLyDbACLYiHJ/7+lYj8KnGsUkodmHL+QKA4caydFcCh6W4oImOAq4G9lVJtkiXXoYicDZwNUF1dTU1NTW41y5Hy8t2AXrzwwhyWLKlz7brffrsnUM6XX75DS4vLb3Ma6uu3BYYze/ZXjBr1TZrf6/P6382bNxLYmtraBdTULCq4nNn49tsBwBi+/HINNTUfOj4/Uz1nz64CdgVqqal5v9BiZkUpKCo6kLo64YUXXqGkxB0VprlZaGo6kOLidt5991XP0jZvs43OO64j9cfw6acD+MlPPmSnndZ0HCOij8u3SX799a5AFYsWzaOmZl3aY/J9b9NRWzsc2JYPPlhMTc3XrlwT4N13hwGjaGxcQk3NF47Pd1rHBQv6ALuzePEGamrec3y/THz5ZSUwnrKyempqZrt2XYt09ayomEBTUw/++99ZbLHFJtfutWrVfkAJn3zyOt9847K/Ng3NzTsAQ5g161MOPNC9dzYbTgV+G/Bp4mOxIPFxHRHpATwKXKKUyukeSqk7gTsBxo8fryZOnOhqmfr102rGyJHjcPPSGzfq7ZFH7kl1ddfHusEbb8Df/w79+2/LxInbbvZ7TU0N+fzvLB/t7ruPZOLEkQWWMjuW37ioaEBe5c1Uz5Ur9Xabbaryum4+9O8Pq1fDLrscyBZbuHPNTxMttaKilS+/nMikSd5pMTU1Ohrf0r7uuWdMx2/l5XoxkkMOyf/6Vs6Igw/ejd12y1SG/N7bdHz+ud5WVm7FxIldhhg54pVX9HbnnYcxceIwx+c7reOQIXrb2trH1XfZMqtvuWWlJ20kXT2HDNFtZIcdJrDrru7cp61NzyYRgaOP3s+XmJ0nntD5+6urv0Nl5XLf+hinmfYmFni/1ehBQ6pIqwbSefeGAKOBe0TknsS+IkBEpBU4SimV6h7wFMuH76Ypxu9EHOBdQNKahEJnzK7OsQT+2rUULPCV0tPgbrpJf+/Ro5ULLyzjF7/QS35OmeL+wiDWymI33pjMTV5Rof92Y+WxuJn0o24+DqKNeFEXy9XRt68/AboQHZN+QSilmkVkDnAYYI/XPQz4R5pTlgBjUvadmzj++8BCD4rZJX36tADuPij7oiD2ZRq9xKsXzuqUBwxw97qZ8Mo/6XfQHrjrx7/qKq1tNzfr7716tbJqlf576lS9vS6nSbS5Y608tnGjnoI3cSKccgqceKI7QsFE6eeHva23t7sn1PyuB3gT6+J3MChEJ0ofERkiIjeLyLsi8lVie5OI5BqOMxWYLCJnishoEZkBDAXuSFz/fhG5H0Ap1aKU+sj+AVYCmxLffU5bkNTw3Xzh/O7IwLsXztLw/RL4cdHCwL3OzJoTbw9sKi9v6fjbjTnxXTF0qN7usoteecwNYb9pky53aakeGPuBV1H6fueqKC3VmTXb23VuCbeI+qDYwu8pefZ7hVrgi8j2wDx0hr164J3E9gJgnoiMynYNpdSjwC+BKxLX2g9tmrcivLZKfEKJF1H6QQgXrzozv0365eW6Q9u4MRkH4QZRFvjp5sT36tU5EKnQOfFd4UUKVLsW5lXgYSpeW8H8fLesAfiaNV0f54Qg+y0vNHwj8DfnD8AGYHul1EFKqZOVUgcB2wPrE79nRSl1u1JqhFKqh1JqnFLqVdtvE7uKFVBKXaOU2tlhuV3DC4EfJw3fb5O+iDd1ibLATzcnPlXgFzonviu86JTjpIX5bQWz3yvqAt/LwWQc3q1sOBX4BwFXKqUW2ncmtPNrEr/Hmt693ffhx0XgNzfrZBzFxf6ko7Ww/m9edGZRDEhKNyfebtLX3wubE98VXrxbQbQR+6qSVhCiGwQh8L1sI1EcFNsxPvzMlAGZJp/XJX6PNZaGHxftZd0697JW2evhl9kVvNFeouyfnDRpcwGVquG3telgOi+Ii8AvLtaplcG9VSWV8t8KZr9X1AfFcfPhhz3T3jzgfBHpdJ7obDjnJn6PNV768P3szKzgp7Y2rZW7QRAdGcDAgXprtBdNVZWeemfX8u0Cv7xc/+5VR+2F2TUIgQ/uD16sdTgqKvTyuH5htcm4+L7jUg83Fa5ccDotbwrwFDBfRB5F58AfDJwIjAKOdrd44SMuJn3QHX5Dg66LpckUQhCmSvv9jMBPYs15v+EGHZ1dXt7i6pz4rkjNee6GtSdIgb9okXvBraaNFIbXAaF+UVamB30NDdDYmGXVKRdxquHPBo5FB+5dDtyGjravB47xOwlOEMTFpG+/n1uNx+8IfQu3O7P2dv+nToG7At+aE7/nnvr78OGtTJsGy5bp/V66XHr10trrpk2Zl8t1ShCdMnjXRvwW+F748KPs9rITdP9bV+dfOpysAl9EikXkGhGpRee8/w/wDTp//nCgt1JqT6XUs94WNRz06tVGcbGOcraSmhRKXDqzoEz6bgt8v1cBs/CiM7PmXY8Y0eranPhc8OrdisugOOptJIjsoOBt0F5Q71Z9vX+dTC4a/k+Bq4C5wM1ogX8c8Dul1BKllPcrvYQIEffNSkH7J425sjNBaC7gbWdmWab8wiuBH/VBcdBtxK13q7ERWlr0CnY9e7pzzVzo3VtnCqyr0/d3g6AFfqg0fOAs4C6l1MFKqV8rpU4EzgN+nFjuttvhduCI0V4Kw22BH1QHYE/lai0UUyhWXSorXeodcyQug2K30+uaNlIYRUXuKypBW1jDJvC3oXPee9Ar2BUDW7teogjgtqAM+oWLiw9/9Wp3rhdUZ1ZSoud+K+XONDAr+2BpKfTs6dIIIkfipuEbK1hngpiSZ+G2JSwohcuaRfPttyXcdZc/c/JzEfiV6CA9O9ZErt7uFicaxEV7MT789AQl8MHdziyIdLQWXlnBot5G4hK0F2QbcfPdamnRkfLFxdpd4AdKwZVX6uXJAZYtK+XCC/UaFFde6e00vVxtCcNEZBvb92Lb/nX2A5VSX7tRsDDj9gtXX+/vC2cRl84sbtrLggX63dp228KuFYZO2QyKOxPUoNha+rWuTgcbF7oqZ9wGxX4mC7NWsrSSYzU1ldDQoP/2aiVLi1yn5T0OfGH7fJrY/++U/V+4XL5Q4mYnEMQLZ+GVf9LvTtneAbjh+w6qUwZ3B5NBmSrt93Tj3Wpr0y4OEX9TNkN8BsVFRe5aJoMKbAV36+H3wCXdSpaNjSW2v71dyTIXDf8n3tw6urj5wsWlU4bgBGVZmbaO1NVpwVDo/9JaN37QoMLL5hSvtBe/cbONrF+vzZz9+m2+CqDXuG0KD0rgW/dcvVqXobq6sGuF4d2K4qA43UqWTU2dp+VZK1medZb7988q8JVS97l/22jjhRbmt1YM7gp8pYLvzOrqdBkKbbxW8J+VstdP4iLw3Xy3gmwj1jvgVkBo0G3EXoZCCMO7FcU2km4lS7uGr797t5Kl00x7Brwx6Qcp8N0wHzU26sxqPXroTGt+42ZnZgR+4cRlUGxZeeIg8N20VgRpmfQqsNUP0q1k2dTUWeB7uZKlEfh5EBeTvhemsQED/I9FsO4LRuDbCcNgMuoafmWldhk1Nm6umTklqOWjLdxsI2Fwe0Wx/023kmWqwPdyJUsj8PMgLtqLvQMoNNgtSM0F3F0xz+rMghT4UQxIshMXgS/inlnfXo8gB8Vu9FvW/8LEuTgj3UqWjY1JH77XK1kagZ8HcenMSkv1i9XeXnhdgorQt/BCwzedWf54oYUF9W65ZdYPelDshYYfxKDYCx++n+/WlCl6xUpreeSNG0soL9cpir1eydII/DyIixYGyQZrNeB8CXIqm/2+hXZmLS06pqGoKPpZxMIyA6TQRCJhaSNG4Ccxg+L8sVayXLZMC/32duHGG/1ZydII/DxwszMLi/ZSqMCPS2dmfx5+TwGz7msvRyEEKSit4E0rk1khBN1G3BoUB91G3AraUyo+bq+gB8VW//u97/mjYBiBnwdurvcddGdmBH5nguzIID4C337fQjvmsLQRo+FrNmzQA7mKimBm5NhN+lG3HrmdgjobRuDniVujzKBfOLc7s6B9+IXWI0hTJXjTmQX1TOIi8OOi4bsVtBd0Gykr04ONtjY966EQ4tJGcsUI/DyJS2fmloYfFx9+kFPyQAfuuGUKD3ow6dagOCxtJC6D4qhbwcA9S1iQJn1wfyG2bBiBnydumWKC7szipr1EXeCDO51ZU1OwiZAgPoPiuATt2X34hViPgpyDbxEXC6vR8COCGy+cUsG/cG778KOuvcRF4AetudjvbQbFmqAFvjX1q7m5MOtR0CZ9cOfd2rhRf8rKNs9+5xd+C/xcl8c1pODGg6qr036oiorCl6vMl7iY9CsrdV6Bpib9yVerDZP2UkhnFvRA0n7vuAyKo67hW/deskS/W5WV+V0jLib9IFcqtTjvPNhuuzlMmjTOl/sZDT9P3BhhBq25QHw6MxF3tPy4afhBvltuWMEaGnQ8Q3l5MlGJ38TFpG+/txttJOqD4jBYwbbZBkaPrvPtf2kEfp640ZkFrbmAOxp+e3s4Go8R+ElWrNDbQpdBLQQ35n2HYeBiT9tcSArquAj8MFnBot7/+o0R+HnihrkyTJ3ZqlX5B/Js2KA7wt69g3NNgBH4dlau1Nsttii8PPli3dsqSz6EoY2UlurFbtra8l9ZUqng3V7gziAsDCZ9NyysQU/JCwIj8PMkLiZ9K3nGpk1QX5/fNcKguYA7C+iESXuJuoZvCXyrLPkQhjYChQfubdgAra26vQXlmgBj0rcTBquk3xiBnydxeuEK9eOHReAbDT9JGDR8a7ARdQ0f4tdGCnm3zKA4uhiBnyduaC9hMSkV6scPS6dcqMBvbNQR/j16aE0sKOLSmVn3dkPDD3pQXGjgXtgEflxM+oW4VJcv11sj8A1ZGTxYb+NgrixU4MelM7Nr90FN0wF3ApLCoOH366f93xs26PnO+RCWQXGhJv24tJFNm/R04pKSYFaTtIjLoNhvjMDPE3tAUr6Ru2HTXrp7ZxYGUyW4Ex8Shs5MpPDAvbANiqOu4RcatBe2QXHU24jfGIGfJ2VlumNua8u/8YRFe3FLww+6HoUuoBMG/z3Ex4cPhZv1wyLwjUlfE6c2YgS+wRGFmvXD0pkVqr2EYbqR/f7dvTNratJm17IyPZ0sSAqNdQlbG4mLFSzfdyssVrDKSigu1nE3mzbldw0j8A2OcEt7CdqkH7fOLOoCv7JS+0gbG/Pzfdu1+yDNrlB4pH5YBL7R8DVhEfgihcW6tLbqZykSfF38xAj8ArA6Myva0ylh6cziZtLv7p2ZNQAN2pwP8RkUx0Xg26Pb29qcnx+WQTEUZglbvVonQxowQA+uuwuBCHwROVdEFojIRhGZIyL7d3HsCSLynIisEpE6EXlbRI7zs7yZKKQzUyo8JqVCg/bCYtK3OrN167p3Z2Zp00G/V2BM+hZhEfglJdrNo1R+WQPDMiiGwgIQw9L3+o3vAl9ETgJmADcAY4E3gZkislWGUw4EXgKOThz/NPCvrgYJflGID3/NGr0oSN++wa1XbhGXCGRrqpB9hTUnxEXgh1HDz9ekH5bA1rho+PYy5PNuhUngF6JwWedYfXh3IQgN/yLgXqXUXUqp+Uqp84FlwM/SHayUukAp9Xul1DtKqS+VUtcCc4Dj/Styegp54ZYt09shQ9wrT77ExaQPhZn14yLww6ThF9JGNm7Uq+WVlOS/lKtb9O2ry1FXl1+QWBgFftTbiNV3Ll3q/Fyj4fuAiJQB44DnUn56DtjHwaV6AwWkJXGHQnz4YRL4/frpzmzDBuedWUuLPq+oKNhEHBZudGZh0F4sgZ+PRhmWKXn2MuQj8K12NXhw8MGHIoVp+XER+GHS8K2+0+pLndBdBb7f4QoDgWIgtfmvAA7N5QIich6wJfBAht/PBs4GqK6upqamJt+ypqW+vr7jmt9+WwmM56uv6qipmePoOi+9VA2MpqhoBTU1810tYz706TOBtWt78MQTbzJoUHOnenZFbW0psC+VlS28+uobnpczGyJjgAHU1HxIc3P2Hs1ez6VL9wHK+PzzN1mzptnTcmajuXkbYCtmzfqabbdd7OjcDz4YDVSzdu18amp0U8v1ebrN2rVlwD58+20zNTVvOjr3o4/6ALtTWbmBmpr3cjrHy3r26jUeqGTmzHfZbruGnM9raRHq6w+kqEgxd+4rBQ9eCq1jS4t+P15/fT4VFc5GYosW7QFUsGDBu9TU5P4/yIds9dywYQiwA3PnLqem5lNH13733W2B4TQ0fEVNzTcFlbNQfG2bSinfPsBQQAEHpOy/Cvgsh/P/B2gEjs3lfuPGjVNu8/LLL3f8/c03SoFSQ4Y4v86NN+pzL77YvbIVws476/LMnau/2+vZFZ98os/bfnvPiuaIU0/V5bnnntyOt+rZ1qZUcbE+d9Mmz4qXM9On67Kce67zcw85RJ/77LPJfbk+T7dpaVFKRH9aWpyd+9hjuh7HH5/7OV7W86CDdHmef97ZeUuX6vMGDXKnHIXW8fzzdXmmTnV+7hZb6HOXLi2oCDmRrZ7//a8uy+GHO7/2j3/srJ/wEi/eWWC2SiMT/fbhrwbagFRDSjXQpWFcRCahtfrTlFJPelM8ZxSSXjdMJn1ImugWLIC77tLlu+uu7MFvYYmitsjXXLl+vY7s79NHJ6wJmqFD9Tbq/smSEv1MlHJuCrfaiPW/CJp8TfphMudD/kF77e3JuhgffjTxVeArpZrRAXeHpfx0GDpaPy0i8gO0sJ+slHrcuxI6o6xMC7p80uuGVeCfdBJceKFuRBdeqDvbK6/UHXY6wtqZOX0eYQpGAhg2TG/z6czC5MOH/AP3rLobge8u+bYRa7pr3756UaSgMT585wQRpT8VmCwiZ4rIaBGZgTb13wEgIveLyP3WwSLyQ+Ah4DfAqyIyOPEJhU6Zb2cWNoH/9dd629KiI6NBbzduhKlT4aqr0p8Xl84sTMFIkL+G39YWruBDyH9qXtgEfr6zWUwb8YZBg3R63TVrnAcbG4HvE0qpR4FfAlcA84D9gKOUUosSh2yV+Fj8FB1cOB09fc/6/NOXAmch30j9MAn82lqYNy/z742NcPPN6RN1hGlKHuS/gE7YNHy79uLEXbRmjT4+TBnE8o3UtwR+GNoI5K/hhyUxlUW+CWvCJvCLi/Prf9vaknUJixXMLwLpEpRStwO3Z/htYlffw0Y+yXeUCpfAf/xxPa2uK4qL4bHH4KyzOu8PW2cWF5N+jx66LmvWaM041wQhYdRc4mLS744afm2t7h+WL0/2WWFpI6D7z6VLddm23jq3c6xBcf/+4XBN+ElIdIDokk9nVlenteZevXSQWNAsXw7NWWahNTamH0VHuTOzEzaBD1rQrVmjO7RcBX7Y/PcQH5N+3Hz4XQXtKaXdeDffnFyVzhKOixfr34POjQD5+fHDOCj2C7N4ToHkY1Kya/dhaDSDB0PPnl0fU16eXuiE1aQfdXMl5OfHD2Nnlo9Jv6lJu5BKS8MjKOMm8LtqI1ddpWN3rGyHSiWVgo8/zhzT4zdWGzECPzeMwC+QfEz6YTLnA0yalN1P3NYGJ564+X6rLmFpPPbOLNPMgnSEVcMHZwI/zBp+Pm1k6NBwDIohPib93r11fIcVlJtKba3W7Bsb05/f2po5psdv8pmaZwS+IW8K6czCIvCrquDcczP/Xl4Ol1ySPnXu4kQSuFz9Z15TXq6tFc3NydkGuRBGgZ/P1LwwLZxjkY9JP2zmfOgcEOpkMBk2gS+SVFTSacaPP67N+F1hxfQETT4mfcsaawS+wTGFmvTDwh/+kPy7vFxvKyq08LzoIpgyZfNzWlt1xyySFE5hIB+zfhgFfiEafpg6s3xM+mGL0AfdFior9Xu/YUPu51nvVlgEPsBWiXlQi9NkbV6+PLN2b5EppsdvCvHhd7eV8sAI/IKJg0kfdBIhaz35667TwmbaNF3W665Lb1ZdulS7AgYPDkd2OgtLaDsxvYZt7joU5sMPq4afq2YcRg0fnJv1lUoK1S239KZM+WBZ5NIJ/MGDk4P+TGSK6fEb48N3hhH4BWJ1rKtWaT93LoRR4EOyM/vud3XZzjqr6xXwrM5iq60yHxMEw4frbbrOLBNWB240fPfp2VPPRmlpyd3vG1aB7zRwb80arQ336ROO1SQtrDa7aNHmv02alL0vyxTT4zfGh+8MI/ALpLTUeXrdsAv8XLWXbxKLTIVN4I8YobcLF+Z2fEuLzqUfliV+LeKi4YNzs35YBb7TNmIJ1LDEuFh0ZdKvqtIxO5m0/F69Msf0+E11tbY+rlqlXS25YAS+oSCcBu6FXeDnqr1YnYWlUYcFq3NNp72kwx5UlS0BkZ9UV+vyrFypByXZUCqcGj44byNhFfiWhr9okV5c6rrrul5kKooCH3TMzkUXaetMRUXn3zLF9ARBSYkeTCqV+7tlBL6hIJz68cMq8J36vsNq0s9Fw6+tTa4KeMcdel+Y/PegO7Pqat2Z5RIgVVenp1mVl2/eSQdNvoPisAl8K/Duwgv15+qru15kKqoCX0QPZpYu1bE8F12k9w8dCtdfH56pkuAscK+9PZxTV/3CCHwXcNKZWQlFrGVDw0RcTPpdafhK6Y556NDkqoDWDIX6emfTrfzAiVk/rNo9dF5KOhfCGKUPyTUnWluTCWm6WmQqCgK/q3e+qkrH8pxyiv4exnfLiR9/7Vrtfu3XT6ev7m4Yge8CTqbmWccMHhwu8zE4F/hhNel3peGnZhCDZPKRJUvCk0HMwonAD6v/HpwNiuvr9bS3Hj2SM0fCQG0tvPZa5t/TLTIVVoHft69OwNPQkNkdYSeMQa0WTjT87mzOByPwXcGJST+s5nzIX+CHTcMfOFAHFq1f37nzzZZBrK0tPBnELOKi4TsR+GHMsgf5JaQJq8AX6XpqXiphnLZq4WRqnhH4hoLJpzMLs8DPJWivvl4L0B49wtcJiCS1fLtZP0oZxCziouE7MemHNWBv+fLs666nJqQJq8CH7H58O1HQ8J20ESPwDXnjxKQfZoHvJGjP8t8PHx4uLcwinR8/ShnELLqjhh9WgT94sLYcdYU9IU19vfYZ9+gRzkFYV3PxUwnj4lIWTkz63TmtLhiB7wrd0aQfVnO+RTo/fpQyiFnERcOPg8B3usiUJUi32ip88TrgTMM3Jv14EMLXMHrEzaS/alX2aPWwRuhbpDPpRymDmIW1RsGSJdmPDbOG78SkH9Y2UlUFl16a2aKVushUmM354EzgW8eE7ZmACdpzghH4LmDvzLIJlLB2ZqDNlQMH6pXmVq/uOjl+2DV8q5O1a/jpMog1NZV0/B2mDGIWcdHw+/TRpu2GhuyrGIZVwwedcGbkSP13jx5a+GdaZCpOAv/TT/X2O9/xrjz5Ylnkli/P3v8agW8omNJSPae+vT17et0wC3yA0aP1dvHirjO3hHVKnkU6DR82zyC2apV2yorAxReHJ4OYxYAB+v2qrdU5HLoizAlFRHK3hIVZ4IvA0Ufrv489Fq69NvMiU3ER+I2Nui6lpcnBTpgoK0v2v9nckUbgG1wh184s7ALfGsEvWtS1szvsJv10Gj5snkGsrU0L/GOPzbwqYJAUFeVusgx7Z5arWT/MAh9g++31tqpKJ3HKtMhU2AX+sGH6/Vq2TFv1MvHFF3q73XZa6IeRXP343XlpXDAC3zVyidRvbdUjULu2EzaSGn7XAj/sJv3qam1yXbNGR0unYmUQq6/X9dxpJ58L6IBczPrNzTp/QHGxXswpjOQyKFYqOgL/88+7Pi7sAr+kRAt9pbqOEbHM+Tvs4E+58iGXQXGY15rwCyPwXSKXSP0VK/RLN2iQbmxhJBeB397eeVpeGCkqym3a0bffag1/1CgfCpUnuQh8+7SpMEaEQ24Cv65O+/jLy7XfP4zEReBDbmb9MPvvLXKZi19bqxeh6tNHu/S6IyHtGqJHLp1Z2M35kJtJf9UqnYCkf//wLdJiJ5Mf344l8K1OPIzkIvDDHLBnYbWRrkz69hz6YXOvWAwfrq1Hy5bpAUo6mpv170VFyZkWYSSXQXEUBH4uJv2wu7z8wAh8l8jFpB8Fgb/VVjpafe3aHhlTzIbdf2+RyY9voRR8+60e2IRZw7cERi4CP8ydmTUYyWVQHFZzPmi3ifW+WP7tVL75Rr9fw4aF1+8NuWn4n32mt1E36UehjXiNEfgukYtJPwoCv6go2bCtkX0qYfffW2TT8FevhoaGEvr2DWdCEQtL+HXlZ/3kE73dZhvvy5MvVhvpqh5h999bWBYhSximEgVzPmQX+O3t0RL4UR8Ue40R+C4RF5M+JP348+en/z3sU/Issmn4lg921Kjwmo8hN5P+nDl6u8ce3pcnX3beWW/nzs18TNQEfiY/flQEfrYFdL79Vk/Lq64O18qFqeSi4Vu/GYFvKBjrhevKFxY1gZ9Jw4+KST+bhm+ZY8NszofcBP7s2Xo7frz35cmX0aN1zMfChZnnSxuB7y/ZNPwoROhDbj7899/X2zDH63iNEfguMXq0DuT57LPM60tHReBbwTnZNPywC/xcNfywdwDZBP66dXrw0qMH7Lijb8VyTHExjBun/3733fTHGIHvL3aBny6dtmXOD3PAHiT71OXLM693MGuW3k6Y4E+ZwogR+C7Ro0dSu7JerFSsgL6wC/xsGn5UTPpDh+rpjytWpM9SFxUNv29fHUhZX58+Kvy99/R2t93CHSAGSZfDO++k/z2KAj+doIyKwO/bV09Ta2hIr6hEIUIf9DS7fv30tLt02U7XrtWDl549dTvprhiB7yL77qu3b76Z/veoaPijRkFRkeKrr9Kv/x0Vk35xcXJQks5kGRUNX6RrLT8K5nyLPffU22waftjbyMCB2qe9YUP6uJ2oCHzoempeVAQ+dG3Wf/ttvR03Tqfi7a4Yge8i++yjt2+8sflvTU3JFzHsaR179IAhQ5pob9982tGmTboexcXh75Qhsx9fKfjyS/132DV86HpqXpQEvl3DT9WMlYrGtDzQg7BMZv22tui4vaBrP35UfPjQdeCeZXXde2//yhNGjMB3EUvgv/22Ni3ZefllnVp39921eTbsbL11I7C5Wd+aUmWZy8NOJj/+0qU6+rhv3+ZQrY6Xibho+CNGaO149erNB2Hr1+uBcWUl9O4dSPEckUngL1um2/qgQZ1XZgwrmQT+hg36fevRIxqWim231dt01iPjv9cYge8igwZpbbGpKRkRavHUU3p7zDH+lysfhg/XAj81cC8q5nyLTBq+1UlvuWWWJehCgiXwv/668/41a2DBAi1YomB2FUlq+akd81tv6a3VcYedTAI/SuZ8yDw1z+7yKi72t0z5YK1i+J//dN7f1pY06RuBb3AVy49vN+srlRT4xx7rf5nywdLwUwV+lEyVkFnDt1wVURH4++2nt//8Z+f9VsDe2LHRsLhA0o+fGrj36KN6+/3v+1uefImLwM+k4UfJnA9w6KHaejp7ts4fYDF/vg523Wqr8LuKvMYIfJdJF7j34YdaMx48WJv0o0Amk37UBH52Db/R1/Lky1FH6SjkefPgo4+S+y1zvjXdLQqk0/A3bYJ//Uv/fdJJ/pcpHyxBmJptz3rXrHcv7GQT+FGwHIG2ch1xhP77iSeS+43/PokR+C5jD9yzgpIs7f7oo8O7klkqW22VFPj2ea1hXyUvlbho+D16wA9+oP9+6KHk/ij57y0sgT97tja3Ajz3nPbh77JLdATMdtvp7VdfaZ+9Rdw0/Kg8D4Djj9fbf/87uc9yFXV3cz4Yge863/mOnq6zZEmyAUXNfw9QWdnK4ME6HsHeEVidWVQ0/C231IOspUv1CmYWURP4AD/6kd4+9FByEBZFgb/FFloYNjQkhYplzo+Kdg86a+CWW+oAXbsFKWoCf+hQ3UaWLevcRqKSdMeOpVS9/DIdi3+ZgL0kRuC7TFFR8sV6802dQvStt/Tcz0MPDbZsTrEautUpv/UWvPCC/ttKzhN2Skv1lDalktaJtjatlQEMGxYdgb/ffnqg9c038NprepnZxYt1VHvYcwmkYp+e19SUDLSKksCH9H78qAn8khI9cFEKnn5a72tri06eCjsDB8L++2uLy8yZOpnQ/PnaQjZ2bNClC55ABL6InCsiC0Rko4jMEZH9sxx/YOK4jSLytYj81K+y5oM9cG/mTN2QDjpId8xRwr6Iztq1ujNubYULL0yaM6OA5Uv90590+Rcv1prM0KHQq1dboGVzQlFRZy3fWjBn992jEUVtx56A5+mndRbB8eOjE6FvkZpx79prdXspLYWRI4MtmxN+muhRTz0VPvhAu8Cam/VgOQpTJO1873t6+5//JANDd9+9eyfcsfBd4IvIScAM4AZgLPAmMFNE0hqJRWQk8HTiuLHAjcCfROR//Cmxc+x+/Cia8y0sgf/JJ3D66VpQ7rkn/P73wZbLKT/7mRaWM2bAgQcmrRRR0lwsfvxjvf373+H11/XfUTLnW9g1/Cia8y2sd+jjj+Hss+Gaa/S7dtttOmVtVPjNb+Dkk/XA65hjoKZG74+SOd/CEvhPPw2vvKL/NuZ8TRATeS4C7lVK3ZX4fr6IHAn8DLgszfE/BZYqpc5PfJ8vInsBlwD/8Ly0ebDnntpM9sEHyWxu1hzRKGE19gce0FHUVVVa0ERtpHzyyVqbP+UU7WaxZlBEIcNeKjvuqE2Tc+dqoQLRFPjjxuk5+R98oAeUkAxKjBJWpP7dd2sNv2dPeOSRpNCJCiLwt79pd8Sbb+pBMkRnSp6dbbaBMWP07Kg//1nvMwJf46uGLyJlwDjguZSfngP2yXDahDTHPwuMF5FQLhVSXq475fZ2PWLeaadomfcsLA3fyqd/333R8UumcuCBOhmSfeAVRYEPSbP++vV6G0WB37u3fr9aWrQPf8KE6ASC2rE0fKVgwAB46aXoCXuLnj11dPvIkclMoVHU8CEZrW8F7hmBr/HbpD8QKAZSl5tYAWTKMD84w/ElieuFkn1sw5comvOhs//u4oujkzQoEwMHwpNPwvTpOgDuhBOCLlF+nHyy1shAm42j5ve2sPz4EE1zPuj4kB120DEtb7wRfcEyaBD89796FT2AnXcOtjz5Ygl80AGJ1loU3Z2I5ObKHRE5GzgboLq6mhrLGeUS9fX1OV2zX79BwE4ADBs2l5qa9a6Ww2vq6+t55ZUazjprCAsXVnDkkV9RU5NmHdAIsuuu+vPNN7k/z7Cx++67MGdOf7bdtpZXX30/6/FhrGffvkOB7RFRDBs2i5qa5qznZCOIet56qyCiWLYs/cItbuNHHW+5pYIPP+wLLCWo16aQeioFW2yxNytX9mTbbVdSU/OJu4VzEV/fWaWUbx+gDGgFTkzZfxvwSoZzXgVuS9l3ItAClHZ1v3Hjxim3efnll3M6bulSpUpLlaquVqqlxfVieE6u9Yw6Ua3nv/+tFCg1dWpux4exnp9+qlRJiVJHH+3eNcNYT7fpDnVUqvB6XnyxbiN33OFOebzCi+cJzFZpZKKvGr5SqllE5gCHAY/ZfjqMzAF4s4DU7NqHoSvUkub4UDBkiE7+0LdvdHKcG6LD976n12EfGFqnVnZ22EEnd9lii6BLYogj118PEyfCd78bdEnCQxCiaCrwgIi8A7yBjsIfCtwBICL3AyilTkscfwfwcxGZDvwF2BeYDJzsa6nzwJqPbzB4QRwE5TbbBF0CQ1zp2TO68VNe4bvAV0o9KiIDgCuAIcBHwFFKKSs55VYpxy8QkaOAaeipe0uBXyilQjklz2AwGAyGMBKIsVkpdTtwe4bfJqbZ9woQkXXmDAaDwWAIHyaXvsFgMBgM3QAj8A0Gg8Fg6AYYgW8wGAwGQzfACHyDwWAwGLoBRuAbDAaDwdANMALfYDAYDIZugBH4BoPBYDB0A4zANxgMBoOhGyA6z348EZFVwKKsBzpjILDa5WuGEVPPeGHqGR+6Qx3B1LMQtlZKDUrdGWuB7wUiMlspNT7ocniNqWe8MPWMD92hjmDq6QXGpG8wGAwGQzfACHyDwWAwGLoBRuA7586gC+ATpp7xwtQzPnSHOoKpp+sYH77BYDAYDN0Ao+EbDAaDwdANMALfYDAYDIZugBH4OSIi54rIAhHZKCJzRGT/oMvkJiJymYi8KyIbRGSViDwpIjsHXS6vSdRbicitQZfFbURkiIjcl3ieG0XkExE5MOhyuYmIFIvIdba2uUBErheRkqDLVggicoCIPCEiSxLv5+SU30VErhGRpSLSJCI1IrJTQMXNm67qKSKlIvIHEflARBpEZJmIPCwiWwVY5LzI9jxTjv1L4phL3C6HEfg5ICInATOAG4CxwJvAzCi+eF0wEbgd2Ac4GGgFXhCR/kEWyktEZG/gbOCDoMviNiLSD3gDEOBoYDRwPrAywGJ5wa+B84BfAN8BLkh8vyzIQrlAJfARuj5NaX7/FXAx+pnugX6uz4tIb99K6A5d1bMc2B34XWL7PWA48EwEB3TZnicAIjIJ2BNY6kkplFLmk+UDvA3clbLvC+DGoMvmYZ0rgTbg2KDL4lH9+gJfAQcBNcCtQZfJ5frdALwRdDl8qOdTwH0p++4Dngq6bC7WsR6YbPsuwDLgctu+XkAdcE7Q5XWrnhmO2RFQwJigy+t2PYGtgSXowflC4BK37200/CyISBkwDngu5afn0NpwXOmNtgDVBl0Qj7gTeFwp9XLQBfGI44G3ReRREVkpIvNE5OciIkEXzGVeBw4Ske8AiMiOaAvV04GWyltGAoOx9UlKqSbgVeLdJwH0SWxj1S8lLBb/B1yvlJrv1X2iZhYJgoFAMbAiZf8K4FD/i+MbM4B5wKyAy+E6InIWsB3w46DL4iHbAOcC04DfA7sBf0r8Fqd4hT+gB6efiEgbuk/7nVLq9mCL5SmDE9t0fdIwn8viGwnl6xbgSaXUt0GXx2WuBVYrpf7s5U2MwDdshohMBfYD9lNKtQVdHjcRkR3Q5u79lFItQZfHQ4qA2Uopy5c9V0RGof3bcRL4JwGnAacAH6MHNjNEZIFS6q9BFszgHgkN+EGgH3BcsKVxFxGZCExGv7ueYkz62VmN9mVXp+yvBpb7XxxvEZFpwMnAwUqpr4MujwdMQFttPhaRVhFpBQ4Ezk187xFs8VxjGfBJyr75QJwCTQH+CNyslHpEKfWhUuoBYCrRD9rrCqvf6S59kmXu3gU4RCm1JuAiuc1EYAiwzNYnbQ38QURctWQYgZ8FpVQzMAc4LOWnw9DR+rFBRGaQFPafBl0ej/g3MAY9mrY+s4FHEn83B1Iq93kD2CFl3/a4v1x00JSjB+R22oh337YALdg7+iQR6QnsT/z6pFLgUbSwP0gpFbsBDXp21C507pOWot1xh7h5I2PSz42pwAMi8g66I/0pMBS4I9BSuYiI3Aacig72qhURy09Yr5SqD6xgLqOUWgess+8TkQZgrVLqoyDK5BHTgDdF5HJ0hzkWPXXtt4GWyn2eBH4jIgvQJv2xwEXA/YGWqkBEpBIdZwJ68LKViOyGfk8Xi8h04Lci8inwOXAFOvr74QCKmzdd1RMt9B5DTzs8FlC2fml9IlAxEmR7nqRMlxWRFmC5UuozVwsS9BSFqHzQAVALgU1ojf+AoMvkcv1Uhs81QZfNh7rXELNpeYl6HQ28D2xEC4VfkFg/Iy4fdMDedLTlogn4Gh2j0TPoshVYr4kZ2uO9id8FuAbtutkIvALsHHS53awnMKKLfmly0GV383mmOX4hHkzLM4vnGAwGg8HQDYizn8tgMBgMBkMCI/ANBoPBYOgGGIFvMBgMBkM3wAh8g8FgMBi6AUbgGwwGg8HQDTAC32AwGAyGboAR+AZDGkRksogoEVknIlUpv5UkfrsmgHJdk7h3qJNmiUiRiEwXkWUi0i4i//bx3teIyMF+3S/l3heJyAdOVyUUzVwR+ZVXZTMYjMA3GLqmL/DroAsRQSYBF6Bz3e8L+CnIrkYvkesrItIPuByYohwmOEkcPwW4TET6e1A8g8EIfIMhC88B54tI6kIlscWlBYRGJ7bTlVKzlFKfu3DNsHMGei2Gf+V5/hPorHlnulYig8GGEfgGQ9dcn9he0dVBlqk9zf57RWSh7fuIhEn+pyJyo4gsF5E6EXlQRMpFZDsReVZE6kXkSxE5PcMtR4vIyyLSmDCbTxGRTu1ZRAaJyB0iskRENonIpyJydsoxluviABF5TETWAW9nqeuRIjJLRJpEZL2I/Dux7LD1+0J02leAtsT1J3dxvUEi8n8iskFEakXkHhE5LnHeRNtxh4vI04n6NorIRyJysYgU246xnsHlifM7uV5E5EAReTHxP29I/K93TinPESLyZqJu9SLymYhc1dX/JMGZwN+VbUlp2/M+J/GMliXcRE+KyJb2kxPnPYYR+AaPMALfYOiaZej1488Wka1dvO5l6AWYTgeuQq/rfgdaO/wv8H3gA+AeEdkpzfn/Bl5AL3b0MHBl4joAiEgf4HXgKLTwPRq90MyfReT8NNd7CL0K2yTgN5kKLSJHJspXnyjzz4CdgddFZFjisO+jc6GDXo54QuKcTPwT+C76f/JDoAX4U5rjtgFeBP5foj73Jer2O9sxExLbe233vjtR9qMT59cDPwZOQefif01EhieO2QataS9I1O849OJZFV2Un8S78R3gtQyHXIZePOX/oV0dE9Dru6fyKjAqUQ6DwV2CXlTAfMwnjB9gMnpxi+2A/ugV9v6W+K2ElIWF0IJHpbnOvcBC2/cRiXNfSjnun4n9P7btqwJagatT7wP8JuX8u4A6oF/i+5Vo8/CoNMetBkpS6jktx//LbOAL6/zEvpFoIT3Vtu/6dP+PNNc7PHH/H6TsfyKxf2KG8yTxHC4HaoEi228KuD7NOV8CL6bs65P4f0xPfJ+UOL+Pw/flpMR5qf9v63nXpOy/JLF/aMr+bRP7Twm6DZhP/D5GwzcYsqCUWgvcApxmN10XyMyU758mts/a7luLXjZzeJrz/57y/RGgEq1tAxyJNs0vSMwqKElE9j8LDAB2TDk/q99ZRCqA3YFHlVKttnIuQC8bfWC2a6Rhb/T69an3fzzN/YeIyF9EZBHaV96CHlj0A7bIUvZRaGH6UMr/oxGYBRyQOHRe4rqPiMgkEenyujaGJrarMvz+dMr3DxPbrVL2W+cPxWBwGSPwDYbcmIZeo3uKS9erTfne3MX+nmnOX5Hhu2VW3wItxFpSPo8lfh+Qcv6y7EWmCq1Zpzt2OdoS4pQhQK1SqiVlf6f6JeITngCOQQv5g9HrpFvm/HT/IzuW4P4rm/9PjiHx/1BKfQkcge4bHwCWi8hbIpJtMGPdf1OG39emfLeOSy23tcZ7ryz3MxgcE+q5vAZDWFBK1YvIjWhN/49pDtkIICJlSqlm2/5UweoW1ei13+3fAZYktmvQ1oELMpz/Wcr3XKaR1SaOG5zmt8FsLtRyYRlQJSKlKUI/dVbEtsB44FSlVIfvW0SOzfE+axLby9CxD6l0PDOl1MvAy4nZCvuiB3n/FZERSqnVWa5fRVJo54M1aMp0H4Mhb4yGbzDkzu1ogXp9mt8WJbYdEd+Jedn7eFSWH6R8/yE6GM0yFT+DDiJbrJSaneZT5/SGSqkGYA5wYkpk/NboetbkUY+3gGJ0oJ+dE1O+lye2HYMCESkFfpTmms1sriF/BiwEdsrw//gg9SJKqU1KqZeAm9BBeyO7qIflkik02M66R+qAzGAoGKPhGww5opTaJCJTgDvT/DwTWA/cJSJXAz3QyWbqPSrOWQkz97toE/SZ6CDC9Ynfp6EDyV4TkWloAVKBHgTsr5T6Xp73vRIdcf+UiNyOjhu4Fl33W5xeTCn1nIi8AdwpIgPRgXWTgF0Th7QntvPRg6rfiUgbWvBfmOGynwBHi8gzaKvEUqXUUhE5D/iPiJShYyBWoy0J+6AHRlNF5KdoV8jTwDfAQLRVYCnwURdVeQdtpt8TPTsiX/ZK1O2tAq5hMKTFaPgGgzPuQUepd0IptQ7tC25HC5Mb0VPLXvaoHN8DDkP7tX+MtjpcZyvPerQgexqdKfBZ4G+J8/Iuk1LqGfSUuH7oet6BFsb7KaWW5nnZ76MtEn9IXLMnemABeiBBwk1yPDpW4H7gNvQUtt+nud7PgQb0NMR3gbMT13gaLcwr0FP1nkVr74PRgXsA7yd+vxGddOlW9BS9g5VSGU31SqmNwH+AXF0MmTgGeEIp1VjgdQyGzRClHGWANBgMBs8RkVuBnwD9lVKZAuFCRSJJ0EvACKXU4jzOH4q2KhyulHrR3dIZDEbgGwyGgElk4esLfAyUoacUngf8USmVMQlQGBGR54HPlFI/z+PcacCuSqlAFv4xxB/jwzcYDEHTAPwSHYnfA21C/y3pZ0OEnfOB40VElANtSkQE7a5IFx9iMLiC0fANBoPBYOgGmKA9g8FgMBi6AUbgGwwGg8HQDTAC32AwGAyGboAR+AaDwWAwdAOMwDcYDAaDoRtgBL7BYDAYDN2A/w/a5bVUGHEQsgAAAABJRU5ErkJggg==\n", 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" ] }, - "execution_count": 36, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -917,7 +917,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 47, "id": "7b14a270", "metadata": {}, "outputs": [ @@ -927,8 +927,8 @@ "text": [ "DbAnalysisResultV1\n", "- name: @Parameters_FineHalfAngleAnalysis\n", - "- value: [0.48414805 0.48518258] ± [0.00112712 0.00318176]\n", - "- χ²: 42.61496084165496\n", + "- value: [0.48127945 0.49289139] ± [0.00058405 0.00163032]\n", + "- χ²: 180.10770235913844\n", "- quality: bad\n", "- extra: <4 items>\n", "- device_components: ['Q0']\n", @@ -942,7 +942,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 48, "id": "e994b6a6", "metadata": {}, "outputs": [], @@ -960,8 +960,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Adjust the phase of 0.0 of the sx pulse by -0.242 rad.\n", - "The backend reports an angle of -0.250 for the sx pulse.\n" + "Adjust the phase of 0.0 of the sx pulse by -0.241 rad.\n", + "The backend reports an angle of -0.241 for the sx pulse.\n" ] } ], @@ -975,7 +975,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 50, "id": "divided-messaging", "metadata": {}, "outputs": [ diff --git a/docs/tutorials/readout_mitigation.ipynb b/docs/tutorials/readout_mitigation.ipynb new file mode 100644 index 0000000000..d641bda3cc --- /dev/null +++ b/docs/tutorials/readout_mitigation.ipynb @@ -0,0 +1,419 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Readout Mitigation\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Readout mitigation is part of `qiskit-terra`. The readout mitigators can be initalized based on existing backend data, or via a readout mitigation experiment.\n", + "\n", + "In a readout mitigation experiment, simple circuits are generated for various combinations of \"0\" and \"1\" readout values. The results give us a matrix describing the probability to obtain a wrong measurement. This matrix is used to initialize the readout mitigation object, which is given as the result of the expriment." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from qiskit import QuantumCircuit\n", + "from qiskit.visualization import plot_histogram\n", + "from qiskit_experiments.library import ReadoutMitigationExperiment\n", + "# For simulation\n", + "from qiskit.providers.aer import AerSimulator\n", + "from qiskit.test.mock import FakeParis\n", + "\n", + "from qiskit.result.mitigation.utils import (\n", + " expval_with_stddev,\n", + " str2diag,\n", + " counts_probability_vector\n", + ")\n", + "\n", + "backend = AerSimulator.from_backend(FakeParis())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "SHOTS = 1024\n", + "qubits = [0,1,2,3]\n", + "num_qubits = len(qubits)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Standard mitigation experiment\n", + "\n", + "The default mitigation experiment is *local*, meaning error probability is measured individually for each qubit. The experiment generates two circuits, one for all \"0\" and one for all \"1\" results." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ░ ┌─┐ \n", + " q_0: ─░─┤M├─────────\n", + " ░ └╥┘┌─┐ \n", + " q_1: ─░──╫─┤M├──────\n", + " ░ ║ └╥┘┌─┐ \n", + " q_2: ─░──╫──╫─┤M├───\n", + " ░ ║ ║ └╥┘┌─┐\n", + " q_3: ─░──╫──╫──╫─┤M├\n", + " ░ ║ ║ ║ └╥┘\n", + "meas: 4/════╩══╩══╩══╩═\n", + " 0 1 2 3 \n", + " ┌───┐ ░ ┌─┐ \n", + " q_0: ┤ X ├─░─┤M├─────────\n", + " ├───┤ ░ └╥┘┌─┐ \n", + " q_1: ┤ X ├─░──╫─┤M├──────\n", + " ├───┤ ░ ║ └╥┘┌─┐ \n", + " q_2: ┤ X ├─░──╫──╫─┤M├───\n", + " ├───┤ ░ ║ ║ └╥┘┌─┐\n", + " q_3: ┤ X ├─░──╫──╫──╫─┤M├\n", + " └───┘ ░ ║ ║ ║ └╥┘\n", + "meas: 4/═════════╩══╩══╩══╩═\n", + " 0 1 2 3 \n" + ] + } + ], + "source": [ + "exp = ReadoutMitigationExperiment(qubits)\n", + "for c in exp.circuits():\n", + " print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "result = exp.run(backend).block_for_results()\n", + "mitigator = result.analysis_results(0).value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The resulting measurement matrix can be illustrated by comparing it to the identity." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result.figure(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mitigation matrices\n", + "\n", + "The individual mitigation matrices can be read off the mitigator." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1.01443299 -0.04123711]\n", + " [-0.01443299 1.04123711]]\n", + "\n", + "[[ 1.01139896 -0.04974093]\n", + " [-0.01139896 1.04974093]]\n", + "\n", + "[[ 1.01616162 -0.01818182]\n", + " [-0.01616162 1.01818182]]\n", + "\n", + "[[ 1.0060241 -0.02208835]\n", + " [-0.0060241 1.02208835]]\n", + "\n" + ] + } + ], + "source": [ + "for m in mitigator._mitigation_mats:\n", + " print(m)\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mitigation Example" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "qc = QuantumCircuit(num_qubits)\n", + "qc.h(0)\n", + "for i in range(1, num_qubits):\n", + " qc.cx(i - 1, i)\n", + "qc.measure_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "counts = backend.run(qc, shots=SHOTS, seed_simulator=42, method=\"density_matrix\").result().get_counts()\n", + "unmitigated_probs = {label: count / SHOTS for label, count in counts.items()}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "mitigated_quasi_probs = mitigator.quasi_probabilities(counts)\n", + "mitigated_stddev = mitigated_quasi_probs._stddev_upper_bound\n", + "mitigated_probs = (mitigated_quasi_probs.nearest_probability_distribution().binary_probabilities())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Probabilities" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "legend = ['Mitigated Probabilities', 'Unmitigated Probabilities']\n", + "plot_histogram([mitigated_probs, unmitigated_probs], legend=legend, sort=\"value_desc\", bar_labels=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Expectation value" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "diagonal_labels = [\"ZZZZ\", \"ZIZI\", \"IZII\", \"1ZZ0\"]\n", + "ideal_expectation = []\n", + "diagonals = [str2diag(d) for d in diagonal_labels]\n", + "qubit_index = {i: i for i in range(num_qubits)}\n", + "unmitigated_probs_vector, _ = counts_probability_vector(unmitigated_probs, qubit_index=qubit_index)\n", + "unmitigated_expectation = [expval_with_stddev(d, unmitigated_probs_vector, SHOTS) for d in diagonals]\n", + "mitigated_expectation = [mitigator.expectation_value(counts, d) for d in diagonals]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mitigated_expectation_values, mitigated_stddev = zip(*mitigated_expectation)\n", + "unmitigated_expectation_values, unmitigated_stddev = zip(*unmitigated_expectation)\n", + "legend = ['Mitigated Expectation', 'Unmitigated Expectation']\n", + "fig, ax = plt.subplots()\n", + "X = np.arange(4)\n", + "ax.bar(X + 0.00, mitigated_expectation_values, yerr=mitigated_stddev, color='b', width = 0.25, label=\"Mitigated Expectation\")\n", + "ax.bar(X + 0.25, unmitigated_expectation_values, yerr=unmitigated_stddev, color='g', width = 0.25, label=\"Unmitigated Expectation\")\n", + "ax.set_xticks([0.125 + i for i in range(len(diagonals))])\n", + "ax.set_xticklabels(diagonal_labels)\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Correlated readout mitigation\n", + "\n", + "In correlated readout mitigation on $n$ qubits, a circuit is generated for each of the possible $2^n$ combinations of \"0\" and \"1\". This results in more accurate mitigation in the case where the readout errors are correlated and not independent, but requires a large amount of circuits and storage space, and so is infeasible for more than a few qubits." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ░ ┌─┐ \n", + " q_0: ─░─┤M├───\n", + " ░ └╥┘┌─┐\n", + " q_1: ─░──╫─┤M├\n", + " ░ ║ └╥┘\n", + "meas: 2/════╩══╩═\n", + " 0 1 \n", + " ┌───┐ ░ ┌─┐ \n", + " q_0: ┤ X ├─░─┤M├───\n", + " └───┘ ░ └╥┘┌─┐\n", + " q_1: ──────░──╫─┤M├\n", + " ░ ║ └╥┘\n", + "meas: 2/═════════╩══╩═\n", + " 0 1 \n", + " ░ ┌─┐ \n", + " q_0: ──────░─┤M├───\n", + " ┌───┐ ░ └╥┘┌─┐\n", + " q_1: ┤ X ├─░──╫─┤M├\n", + " └───┘ ░ ║ └╥┘\n", + "meas: 2/═════════╩══╩═\n", + " 0 1 \n", + " ┌───┐ ░ ┌─┐ \n", + " q_0: ┤ X ├─░─┤M├───\n", + " ├───┤ ░ └╥┘┌─┐\n", + " q_1: ┤ X ├─░──╫─┤M├\n", + " └───┘ ░ ║ └╥┘\n", + "meas: 2/═════════╩══╩═\n", + " 0 1 \n" + ] + } + ], + "source": [ + "qubits = [0,3]\n", + "num_qubits = len(qubits)\n", + "exp = ReadoutMitigationExperiment(qubits, method=\"correlated\")\n", + "for c in exp.circuits():\n", + " print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

This code is a part of Qiskit

© Copyright IBM 2017, 2022.

This code is licensed under the Apache License, Version 2.0. You may
obtain a copy of this license in the LICENSE.txt file in the root directory
of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.

Any modifications or derivative works of this code must retain this
copyright notice, and modified files need to carry a notice indicating
that they have been altered from the originals.

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import qiskit.tools.jupyter\n", + "%qiskit_copyright" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "42e50baa22dbe56fc7f4f6bf32ac20952839a6a23dcf2ef84eded7e8cac03444" + }, + "kernelspec": { + "display_name": "qiskit38", + "language": "python", + "name": "qiskit38" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/t1.ipynb b/docs/tutorials/t1.ipynb index fffdc112f8..8dc125e61b 100644 --- a/docs/tutorials/t1.ipynb +++ b/docs/tutorials/t1.ipynb @@ -27,7 +27,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -41,16 +41,16 @@ "text": [ "DbAnalysisResultV1\n", "- name: @Parameters_T1Analysis\n", - "- value: [9.82109916e-01 1.79859186e-02 2.44740868e-05] ± [3.06727262e-02 3.33197395e-02 1.65928238e-06]\n", - "- χ²: 0.469527963280146\n", + "- value: [ 1.01709709e+00 -1.24842702e-02 1.24564876e-04] ± [8.34811272e-03 8.40832166e-03 3.49584508e-06]\n", + "- χ²: 0.45668433437636546\n", "- quality: good\n", "- extra: <4 items>\n", "- device_components: ['Q0']\n", "- verified: False\n", "DbAnalysisResultV1\n", "- name: T1\n", - "- value: 2.4474086824274952e-05 ± 1.659282377426947e-06 s\n", - "- χ²: 0.469527963280146\n", + "- value: 0.0001245648757569149 ± 3.495845076774465e-06 s\n", + "- χ²: 0.45668433437636546\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -63,22 +63,32 @@ "from qiskit_experiments.library import T1\n", "\n", "# A T1 simulator\n", - "from qiskit_experiments.test.t1_backend import T1Backend\n", + "from qiskit.test.mock import FakeVigo\n", + "from qiskit.providers.aer import AerSimulator\n", + "from qiskit.providers.aer.noise import NoiseModel\n", "\n", - "# Simulate T1 of 25 microseconds using a mock backend\n", - "t1 = 25e-6\n", - "backend = T1Backend(t1=[t1])\n", + "# Create a pure relaxation noise model for AerSimulator\n", + "noise_model = NoiseModel.from_backend(\n", + " FakeVigo(), thermal_relaxation=True, gate_error=False, readout_error=False\n", + ")\n", "\n", - "# Time intervals to wait before measurement\n", - "delays = np.arange(1e-6, 40e-6, 3e-6)\n", + "# Create a fake backend simulator\n", + "backend = AerSimulator.from_backend(FakeVigo(), noise_model=noise_model)\n", + "\n", + "# Look up target T1 of qubit-0 from device properties\n", + "qubit0_t1 = backend.properties().t1(0)\n", "\n", + "# Time intervals to wait before measurement\n", + "delays = np.arange(1e-6, 3 * qubit0_t1, 3e-5)\n", "# Create an experiment for qubit 0\n", "# with the specified time intervals\n", "exp = T1(qubit=0, delays=delays)\n", "\n", - "# Run the experiment circuits with 1000 shots each,\n", - "# and analyze the result\n", - "exp_data = exp.run(backend=backend, shots=1000).block_for_results()\n", + "# Set scheduling method so circuit is scheduled for delay noise simulation\n", + "exp.set_transpile_options(scheduling_method='asap')\n", + "\n", + "# Run the experiment circuits and analyze the result\n", + "exp_data = exp.run(backend=backend).block_for_results()\n", "\n", "# Print the result\n", "display(exp_data.figure(0))\n", @@ -111,26 +121,21 @@ "text": [ "DbAnalysisResultV1\n", "- name: T1\n", - "- value: b88108e2-4785-4a0e-b35d-f4e060d11a81\n", + "- value: 69bb5c63-b148-4611-8bf7-522ca2e5919f\n", "- device_components: ['Q0']\n", "- verified: False\n", "DbAnalysisResultV1\n", "- name: T1\n", - "- value: 487619f7-de90-480f-b69b-2951f3dd95c7\n", + "- value: 24fb01aa-6f6d-490b-8fac-8047287fe7dd\n", "- device_components: ['Q1']\n", "- verified: False\n" ] } ], "source": [ - "# A simulator where qubits 0 and 1 have T1 of 25 microseconds\n", - "backend = T1Backend(t1=[t1, t1])\n", - "\n", - "# An experiment for qubit 1\n", - "exp_q1 = T1(qubit=1, delays=delays)\n", - "\n", - "# A parallel experiment\n", - "parallel_exp = ParallelExperiment([exp, exp_q1])\n", + "# Create a parallel T1 experiment\n", + "parallel_exp = ParallelExperiment([T1(qubit=i, delays=delays) for i in range(2)])\n", + "parallel_exp.set_transpile_options(scheduling_method='asap')\n", "parallel_data = parallel_exp.run(backend).block_for_results()\n", "\n", "# View result data\n", @@ -161,7 +166,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -175,16 +180,16 @@ "text": [ "DbAnalysisResultV1\n", "- name: @Parameters_T1Analysis\n", - "- value: [9.72392961e-01 3.00791225e-02 2.36574352e-05] ± [2.86758057e-02 3.13365113e-02 1.55605735e-06]\n", - "- χ²: 1.406609273564397\n", + "- value: [ 1.00881999e+00 -1.22764820e-02 1.26298401e-04] ± [8.76727069e-03 8.68092420e-03 3.66448756e-06]\n", + "- χ²: 1.0065480102230437\n", "- quality: good\n", "- extra: <4 items>\n", "- device_components: ['Q0']\n", "- verified: False\n", "DbAnalysisResultV1\n", "- name: T1\n", - "- value: 2.3657435238459643e-05 ± 1.5560573532728255e-06 s\n", - "- χ²: 1.406609273564397\n", + "- value: 0.00012629840067391552 ± 3.664487564783831e-06 s\n", + "- χ²: 1.0065480102230437\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n", @@ -193,7 +198,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -207,16 +212,16 @@ "text": [ "DbAnalysisResultV1\n", "- name: @Parameters_T1Analysis\n", - "- value: [ 1.04480151e+00 -4.98690306e-02 2.72402609e-05] ± [3.67305139e-02 3.95114439e-02 1.94345488e-06]\n", - "- χ²: 0.4032439518030832\n", + "- value: [ 1.00905375e+00 -1.19766839e-02 1.16050698e-04] ± [7.34287426e-03 7.09018147e-03 3.10303106e-06]\n", + "- χ²: 0.6657310034408501\n", "- quality: good\n", "- extra: <4 items>\n", "- device_components: ['Q1']\n", "- verified: False\n", "DbAnalysisResultV1\n", "- name: T1\n", - "- value: 2.7240260931179397e-05 ± 1.9434548811782312e-06 s\n", - "- χ²: 0.4032439518030832\n", + "- value: 0.00011605069796406496 ± 3.1030310576193938e-06 s\n", + "- χ²: 0.6657310034408501\n", "- quality: good\n", "- device_components: ['Q1']\n", "- verified: False\n" @@ -234,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -254,13 +259,6 @@ "import qiskit.tools.jupyter\n", "%qiskit_copyright" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -282,7 +280,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.5" + "version": "3.7.5" } }, "nbformat": 4, diff --git a/docs/tutorials/t2ramsey_characterization.ipynb b/docs/tutorials/t2ramsey_characterization.ipynb index 56ef446498..da38e8b2fa 100644 --- a/docs/tutorials/t2ramsey_characterization.ipynb +++ b/docs/tutorials/t2ramsey_characterization.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "scrolled": true }, @@ -48,18 +48,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "qubit = 0\n", "# set the desired delays\n", - "delays = list(np.arange(1e-6, 50e-6, 1e-6))" + "delays = list(np.arange(1e-6, 50e-6, 2e-6))" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": { "scrolled": true }, @@ -79,6 +79,7 @@ "source": [ "# Create a T2Ramsey experiment. Print the first circuit as an example\n", "exp1 = T2Ramsey(qubit, delays, osc_freq=1e5)\n", + "\n", "print(exp1.circuits()[0])" ] }, @@ -86,32 +87,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We run the experiment on a simple, simulated backend, created specifically for this experiment's tutorial." + "We run the experiment on a simulated backend using Qiskit Aer with a pure T1/T2 relaxation noise model." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "from qiskit_experiments.test.t2ramsey_backend import T2RamseyBackend\n", - "# FakeJob is a wrapper for the backend, to give it the form of a job\n", - "from qiskit_experiments.test.utils import FakeJob\n", + "# A T1 simulator\n", + "from qiskit.test.mock import FakeVigo\n", + "from qiskit.providers.aer import AerSimulator\n", + "from qiskit.providers.aer.noise import NoiseModel\n", "\n", - "# The behavior of the backend is determined by the following parameters\n", - "backend = T2RamseyBackend(\n", - " p0={\n", - " \"A\": [0.5],\n", - " \"T2star\": [20e-6],\n", - " \"f\": [100100],\n", - " \"phi\": [0.0],\n", - " \"B\": [0.5],\n", - " },\n", - " initial_prob_plus=[0.0],\n", - " readout0to1=[0.02],\n", - " readout1to0=[0.02]\n", - ")" + "# Create a pure relaxation noise model for AerSimulator\n", + "noise_model = NoiseModel.from_backend(\n", + " FakeVigo(), thermal_relaxation=True, gate_error=False, readout_error=False\n", + ")\n", + "\n", + "# Create a fake backend simulator\n", + "backend = AerSimulator.from_backend(FakeVigo(), noise_model=noise_model)" ] }, { @@ -125,14 +121,12 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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PqILO68vVHXfcQUpKCosWLcrRhJGX6tWr53gdGRlJ9erV3f5+eqpq1apUrVqVJk2aULFiRTp16sTIkSOpWbMmVatWZd++fTnOd72umtFreeTIkdx4443cfvvtALRs2ZIjR45w++23c//99+d5z2rVqgHQrFmzzH3lypUjMTExs/lnypQplCpVimeffTbznNmzZ1OzZk1WrVpFx44d3X6mSpUqnZEveQX0L774guHDh/Phhx/StGlTt9cLVxr0veD6n+VF86QKc2effTbvvvsutWvXPiP4ulSrVo1vvvmGiy66CLBNat9++23mP0GwwTb37IA//fRT5nCjZs2aER0dze+//87ll19e6PS2adOG2bNnc/LkSbel/YEDB9KnTx/q1atH1apV6datW77XHDNmDMOHD8/3HFeNg7cSEuzknW+88QYxMTF07949x3FjTOa133nnHWrWrMnZZ5+d57VOnTrFqVOnzgi0ERERBX4pio6OZsGCBVxxxRV0796dzz//nPKF+OY/fvx4PvroI7799lvKli3r9ft9wfXZT5w4AUD79u15+OGHc8ys99lnn5GYmJj5+3j06NE88zH3F9fsOnToAMDmzZupUaMGYIdQJicnU7t27Xyvmz2dRbF161auu+46nn32WS699NIiXy8UafW+FzToq9xcY5z79u3L6tWr2b59O59//jmDBg3in3/+AWDo0KE8++yzvPfee2zevJn77rvvjAB/0UUX8cknn7BgwQI2b97MsGHDMjubAZQpU4bhw4fz2GOP8cYbb7Bt2zbWrl3LK6+8wrRp0zxO75133klqairXX3893333Hdu2beOdd97JUU3cvXt3KlWqxJNPPsmAAQPyrS4H56v3ASZPnsyaNWvYsmULU6ZM4e677+app57KEWife+451q1bx4YNGxg7dixPP/00L774YmaQ2L17N02aNGH+/PkAlC1bli5duvDII4+QlJTEjh07mDFjBu+88w5XX311gWmKjY1l4cKFlCtXju7du3Pw4EGvPtPnn3/Oo48+ytSpU4mNjWXv3r3s3bv3jDHy2R04cCDzvL179/LNN9/QpEmTHPu88dFHH/HWW2+xfv16du7cyccff8zgwYM5//zzM0vJN910E3FxcQwYMID169fzwQcf8PTTTzNs2LDMmqMrrriCadOmMWfOHHbs2MFnn33G448/zuWXX575s548eXKOWqlGjRpx1VVXMXToUL766is2btzIrbfeSpUqVTK/yPbq1YsffviBMWPGsHXrVn744QduvfVWataseUbTmreOHTvGlVdeSbdu3bjuuusKnYchz11jfzhsTnTky+7nn21HvqZNvbpssRaKHfnyG7KX19CuLVu2yLXXXivly5eXmJgYadSokdx9992ZneVOnTol9913n5QrV07KlSsnd9999xlD9k6ePCl33nmnVKpUSSpVqiSjRo3Kc8jes88+K02bNpWoqCiJj4+Xbt26ZQ6BcnXk++6773KkD5B58+Zlvl6/fr1cdtllUqpUKSldurS0b99e1q1bl+M9Tz75pBhjZMeOHflnoo/069dPKlasKFFRUdKqVSuZOXPmGedceOGFUq5cOYmJiZHzzjtPFi1alOO4Kz/efPPNzH3JyckyYMAASUxMlJiYGGncuLH85z//yRyKlpfcQ/aOHj0qF110kbRr105SUlI8HrL3xBNPeD1kr0uXLm6HmZHPcDN3PvvsMzn//PMz861hw4by0EMPyYEDB3Kc9/PPP0unTp0kOjpaqlatKqNHj86RR6dOnZLRo0dLgwYNJCYmRmrUqCFDhgyRAwcOZHbkc33e7A4fPiy33367VKhQQcqXLy+XX365bNu2Lcc577zzjpx99tlSqlQpiY+Pl8svv1w2bNiQ7+fyZMie6/fB0zwM1458fl1a19/atWsn3sytXtBKRocO2bb8UqXgn3+0E58ngnnFrXbt2gVs7v27776b9evXez3Rir/mNR8yZAjbtm3js88+8/m9Ai1c5ooPFuGSn4H8/5Cbt/9HjTEhvbRu0ChXDsqUsQE/JQUqVgx0ipRy1qFDh9i4cSMzZ87k3XffDXRylFIO0zZ9L2m7vgpnV111FRdffDG33XYbvXr1CnRylFIO05K+l2rVgg0b4PffoXXrQKdGhaqiLvLiK0WZ110pFfy0pO8lLekrpZQKVRr0vaRBXymlVKjSoO8lDfrKU8aYfLcBAwawc+dO/v3vf1OvXj1iY2OpV68eI0aM4NixYx7fx91a4sFs+fLlXHnllVSvXh1jDDNmzMjzvC1btnDNNddQvnx54uLiOPvss9m0aVO+1z558iSjRo2ibt26REdHU6tWLV588UUffIrAWblyJZGRkWcs+Xzq1CnGjBlD/fr1iYmJ4ayzzjpjimFP8z67nTt35vk7nP3aAwYMwBhD2bJlc5yTexZLFVjapu8lDfrKU9kn4Pnoo48YOHBgjn2xsbGZq+69/PLLNGzYkE2bNjFo0CD+/vtvrybdcUJ+s/Q5LTU1lRYtWtC/f3/69++f5zk7duygQ4cO9O/fn6VLl1K+fHl++eUXSpcune+1b7jhBv744w+mTZtGw4YN2bdvn1dfonxh586d1K1bN98Z6zyVkpJC//79ufjii9m9e3eOY66VGF977TWaNm3K4sWLufrqq1m1ahVt2rQBPMt7dz799FPOOuuszNcVsw1hmjRpEk8//TSpqamZP6MOHTrQuXPnwn5U5QvuBvCHw+b05DwiItu32wl6atTw6tLFVqhNzuMr8+bN83gilSlTpkjFihUzX588eVLuueceqVatmkRFRUmNGjXk4YcfFpG8J28REdm/f7/ccMMNUr16dYmJiZFmzZrJG2+8keM+Xbp0kcGDB8sDDzwg8fHx0q5dO4c+rXdKlSqVYwIdlxtvvFFuuukmr661ePFiKVu2bL5rr+e2bt06jyY1evLJJ6VWrVoSFRUlCQkJ0q9fP4/v4ZoYxglXX321jB49+oxJg0Tsio4TJ07Mse+aa66Rf/3rX3ley13e5+Zu4qe8uCbnWblypQDy1Vdf5Xs+IK+++qr06dNH4uLipG7dujJr1qwc5xQl7wsrXCfn0ep9L1Wvbifl2bMHTp8OdGpUODp8+HCOBV1efPFF5s+fz5w5c/jxxx+ZO3cujRs3BuCDDz6gRo0ajBo1KnOdcIDjx49z9tln89FHH7FhwwaGDh3KHXfcwRdffJHjXrNnz0ZEWLFiBTNnzswzPStWrMixsl9e27hx4xzNg/T0dBYuXEizZs3o0aMHlStX5pxzzmHu3Ln5vu/DDz/knHPOYcKECdSoUYOGDRty7733kpqaWqT0vP/++4wfP56pU6eydetWPvroI84999wiXbMwpk6dyr59+xg5cmSex0+cOJE5X75LbGwsK1eudOT+11xzDVWqVKFDhw689957+Z47ffp0mjdvzgUXXFDgdceMGcNVV13FTz/9RN++fbntttsyF+EJlrwPF1q976WoKKhWzQb9PXuyqvuVcsKuXbsYP348jz76aI59jRo1olOnTqSmptKsWbPMf6QVK1YkIiKCMmXKZK6ABnZ1tgcffDDz9aBBg1i6dCnvvPMOF198ceb+unXrFri8a7t27QpcwrWiwzNV/fnnn6SmpjJu3LjMefWXLl3Kv/71L0qXLu12DoHt27ezcuVKoqOjef/99zl48CD33HMPe/bsKTBI5WfXrl1Uq1aNSy65hJIlS1KrVi3atctzwrNM2ZshJKNaP/u+Tp068cknn3ichnXr1vHkk0/yzTffuF2d79JLL2XixIl07dqVhg0b8sUXX/DBBx+Qlpbm8X3yUrp0acaPH0+HDh2IjIxkwYIF9O3bl7feeoubb775jPMPHTrEu+++y1NPPeXR9fv165d5nbFjxzJp0iSWL1/OzTffXKi8V+5p0C+EWrVswP/tNw36yjn79u2jR48edO/ePcfypAMGDKB79+40atSIrl270rt3by677LJ8F8JJS0vj6aefZu7cuezevZsTJ05w8uTJM6by9GQRk9jY2CIv5eot14pqV111Veaa6K1bt+b7779n8uTJboN+eno6xhjefvttypUrB9g5ES699FL27duXuWqft6677jomTZpE3bp1ufTSS+nRowdXXnkl0dHRbt+T/YvS7t276dq1a459sbGxHt//xIkT9O3bl/Hjx1O3bl23502aNImBAwfSrFkzjDHUr1+fW2+9lTfeeMPje+UlPj6eBx54IPN1u3bt2L9/P88++2yeQX/27Nmkp6fTr18/j67fqlWrzOeRkZFUrlyZP//8Eyhc3iv3tHq/ELQzn3La3r17ufDCC2nRogWzZs3KXM0M7PK9O3fu5KmnnkJEuOWWW+jevXu+S42OHz+e559/ngcffJAvvviCtWvX0rt3b06ePJnjPE96Vgeiej8+Pp7IyMgca68DNG3aNLPaNy/VqlWjevXqmQHf9R4g3/fllrtkXLNmTTZv3syrr75K2bJleeCBB2jbti1Hjhxxe43sqwy6lo7Nvq969eoepyc5OZlNmzZx6623EhkZSWRkJGPGjGHDhg1ERkayZMkSwC7R/OGHH3LkyBF27dqV2fGxXr16Ht/LU+eddx5bt27N89j06dO59tprPa4Byr0stTEm8/e7MHmv3AtISd8YcyfwIFAN2ADcJyIr8jn/JuAhoBFwGPgcGC4iAVkTUYO+clJycjIXXnghzZs355133slzGdoyZcrQp08fLr30UgYOHMj555/Ptm3baNSoEVFRUWcEqZUrV3LFFVdklrREhC1bthRqDfhAVO9HRUVxzjnnsHnz5hz7t2zZkhlA89KhQwfmzZuXowf5li1bAPJ9H9iaFpft27efcTwmJoZevXrRq1cvHnnkEapWrcpXX33FJZdc4vHnKqzq1auzbt26HPumTp3KZ599xvz58zPXuc+e1urVq3Pq1Cnef/99rr/+esfTtHbtWqpVq3bG/u+//56ffvqJiRMnOnavQOZ9uPF70DfG9AUmAXcCKzMePzHGNBORM8KoMaYDMAsYDnwIJABTgf8BF+c+3x9q1rSPGvRVUe3Zs4euXbuSmJjIxIkT2b9/f+axypUrExERwYQJE6hWrRqtW7fmxIkTvP3225QtW5YaNWoAdpz+ihUruPnmm4mOjiY+Pp5GjRoxd+5cVq5cSXx8PC+99BI7duzIHLblDV9U76emprJt2zbAVsn/9ttvrF27looVK1Ir41v1Qw89xPXXX0+nTp246KKLWLZsGXPmzOHDDz/MvI5ryJmrE+JNN93E2LFjufXWWxk9ejQHDx5k6NCh9OnThypVquSbprFjx1K9enUiIiIy+0P88ssvHDhwgAULFnD69GnOO+88Spcuzdy5cylZsiQNGzZ0e73s67THxMSQnJycY19UVJRXJeHcY/KrVKlCdHR0jv2rV69m9+7dtG7dmt27dzN69GjS09N56KGHMs/xJO9HjBjBt99+m9nx86233qJkyZK0adOGEiVKsHDhQqZMmcIzzzxzRlpnzJhBw4YNHVtdc8aMGV7nvcqHu279vtqA1cD0XPu2Ak+5OX84sCvXvluB1ILu5YsheyIi8+fbYXtull1X2eiQPcvdkL0333zT7RrfrrXsp02bJm3atJHSpUtLmTJlpHPnzjmGQX399dfSqlUriY6OzrzHgQMH5Oqrr5bSpUtL5cqV5cEHH5QhQ4ZIly5dMt/XpUsXueuuu3z6ud1xrUGfe8u9tvybb74pDRs2lJiYGGnZsqW8/fbbOY536dIlx2cSEfnll1+ke/fuEhsbK4mJiXLnnXdmDiPLi2vI3sMPPyxVq1aVMmXKyMiRI+WWW26RcuXKyapVq2T+/PmZ69DHxcVJu3btZOHChfl+Rnc/V9eWO93eymvIXlJSkjRt2lSio6OlUqVK0q9fP9m9e3eOczzJ+1tuuSVzHXoRkRkzZkjTpk0lLi5OypQpI23btj1jWJ2IHa5XqlQpeeaZZzz+HOQaGikiUrt2bXnuuedERAqV904I1yF7/g74UcBp4Lpc+6cAX7p5T3vgJHAFYIB4YDHwbkH381XQX7PG5lzLll5dvljSoO+s/IKXKhx34/RV4YTL72gw/X9wMuj7u3o/HogA9uXavw/oltcbRORrY8wN2Or8WGyTxGfALXmdb4wZBAwCSEhI8GrVsNTUVI/OP3SoJNCBHTtOkZT0lcfXL448zdNAOH78OP/880+gk+GVtLS0kEtzsHN1GDty5IjmrQPC5Xf0+PHjQfO/y8n/o0E/ZM8Y0wx4CRiLLeFXA54DXgXOmENSRKYB0wDatWsn3rQrJSUledQOJQKxsZCaWpKzz+5K2bIe36LY8TRPAyEmJoYyZcoEOhle+eeff0IuzcHONfSxVKlSmrcOCJff0ZiYmKD53+Xk/1F/B/39QBq2M152CYC7nvgjgG9F5LmM1z8bY44AK4wxj4rIH75JqnvG2B78mzfD779D8+b+ToFSyim1a9fOnDxHqXDn13H6InISWAN0z3WoO7DKzdvisF8UsnO9Dtg8AzpsTymlVKgJRPX+BGCWMeZb4CtgMJAIvAJgjJkJICKuqvuFwHRjzBCyqvcnAj9IHkP8/EWH7SmllAo1fg/6IjLXGFMJGIkN4OuBniKyK+OUWrnOn2GMKQPcDTwPHAKWAg/7L9Vn0pK+UkqpUBOQjnwiMhU7wU5ex7rmse8lbGe+oKFBP2/btsGYMXDhhXDTTYFOjVJKqex07v1CcgX9338PbDqCSVqaDfSzZsFtt0GdOjB7di3+/jvQKVPFwe+//07Xrl1p1qwZrVq1Yt68eYFOklJBR4N+IWmb/pmmTIHvvrNLD7dqBXv3wuuv16NmTZg/P9CpU+EuMjKSiRMnsnHjRpYsWcJ9992ni7IolYsG/UJyrTORnGzH7Rd3v/0GriXgX3kF1q6Fzz6Ddu0OcOwYPPCArQlQCuxywZdffrmj13StTwBQtWpV4uPjOXDggKP3UCrUadAvpDJloHRpOH4cDh0KdGr8KyUFpk+HsWPt44EDcNddcOQIXHstXHmlncugWzd4+umfqVsXduyAjz8OdMqVvwwYMABjzBmba7W+SZMmMXv2bAC6du3K3Xff7ej916xZQ1paGjVdVXJ+snz5cq688kqqV6+OMYYZM2YU+J7Ro0efkU9Vq1bNcc6UKVNo1aoVZcuWpWzZsrRv356P9Q9KFULQz8gXzBITYcsW2LMHCrFiacgRgVGjYPx4iIiAo0chLs4G/FOnoGxZePHFnO+JiIB77oFhw2DSJPuFQBUP3bp1Y9asWTn2xcfHA+RY795pBw4coH///kyfPt1n93AnNTWVFi1a0L9//8wVAD3RuHHjHNOsRkRE5Dheo0YNnnnmGRo2bEh6ejpvvfUWvXv3Zs2aNbRq1cqp5KtiQEv6RZCYaB+TkwObDn8ZNQomTLC1G0eO2C8BR47YgA/QoUNWnmR3661QqhQsXQrr1/s3zSpwoqOjqVq1ao4tMtKWM1zV+wMGDODLL79kypQpmaXcnTt35nm9vn37UqlSpRzrtG/atIm4uDjmzJkDwIkTJ+jduzePPPIIF1xwga8/4hl69uzJuHHj6NOnT+b0vp6IjIzMkU+VK1fOcfyqq67isssuo0GDBjRq1Ij//ve/lClThq+//trpj6DCnAb9InC16+/ZE9h0+ENKii3hHz3q/pylS+HgwTP3ly8Pt2Qsj5S7JkAVb5MmTaJ9+/bceuutJCcnk5yc7LZKfuLEidx00008+eSTgA3wN954I3369OGGG25ARBgwYAAXXXQR/fr1K/De48aNo3Tp0lSrVo3SpUvnua1YscLRz+vO9u3bSUxMpG7dutxwww1s377d7blpaWnMmTOH1NTUgHyxUaFNg34RuEq1xSHov/eerarPT2QkuBsldc899nH2bHQIXzHx6aef5gigl1122RnnlCtXjqioKOLi4jJLubmrtl2qVavGAw88wMGDB9m1axePPPIIhw8fZsqUKQB89dVXzJ07lw8//JDWrVvTunVr1q1b5zZ9gwcPZu3ataxcuZK1a9fmubVr186ZzMjHeeedx4wZM/j000+ZPn06e/fu5YILLuDvXH8o69ato3Tp0kRHRzN48GDmz59Py5YtfZ4+FV60Tb8IsvfgD3d79+Zfygd7fK+bZZOaNIFLL4XFi+H11+Ghh5xPowounTt3Ztq0aZmvY2Nji3zNOnXqUL58eZ599lmmTZvG8uXLM1d069ixY+YyuZ6oWLEiFStWDPiqcLm/DJ1//vnUq1ePt956i2HDhmXub9y4MWvXruXQoUO899573HLLLSQlJdGiRQt/J1mFMC3pF0FxKulXrWo77eUnLs6e586999rHyZPh9Gnn0qaCU1xcHA0aNMjcqlev7sh1zzrrLKZOncrIkSNp3759oa8TTNX72ZUuXZrmzZuzdevWHPujoqJo0KABbdu25amnnqJ169a88MILfk+fCm1a0i+C4lTS79MnK2i7k5YG113n/niPHtCwIWzdCv/3f3Z4n1JRUVGkeTGJg4jQvHlzRo4cWaT7Dh48mOuvv57U1FRKly6d5zlOfVHxxvHjx/nll1+48MIL8z0vPT2dEydO+ClVKlxoSb8IilNJv0IFGD7cfWk/Ls4ez2/oYokSWW37r73meBJViKpTpw7ffvstO3fuZP/+/flW0U+ZMoXly5dz+vRpt23/nqpYsSINGjSgfv36OWoksm/eNkmkpqZm9gdIT0/nt99+Y+3atfyWberOyZMn06RJk8zXw4cP58svv2THjh2sXr2aPn36cOTIEW5x9X4FHnnkEVasWMHOnTtZt24dI0aMICkpiX/9619FygNV/GjQL4LiNivfmDF2vH1MjN3ATsITE2P3jxlT8DVcNQHLl8PJk75Lqwodw4cPJyoqimbNmlG5cuUcATK7jRs38uCDD3LXXXexdetWjhbUySQAvv/+e9q0aUObNm04duwYTzzxBG3atGHUqFGZ5+zfv5/Nmzdnvv7jjz+48cYbady4Mddccw3R0dF888031K5dO/OcvXv3cvPNN9O4cWMuvvhivvvuOz755JM8O0cqlS8RCdutbdu24o1ly5Z5db6ISOnSIiCSkuL1W0PWgQMiV1xhP3fXrvl/9rzytFkz+94VK3yWRI94+/sRDA4fPhzoJATE8ePH5ayzzpIbb7xRjhw5IiVKlJCvv/7akWsX1zz1lXDJz2D6/+BtbAK+FzdxUUv6RVScqvhdKlSA/fvt82HDvJ+N8KKL7OPSpY4mS4WxRx55hEOHDvHyyy8TFxdHw4YNmTRpkttaAaVU3jToF1Fx6szncuAArF4NJUtCAX2N8qRBX3ljyZIlTJ48mdmzZ2dO3/vYY4+xdOnSHO3eSqmCae/9IiqOJf3PP4f0dOjSxS465K0uXWxfgK+/zpq/Xyl3LrnkEk655nrO0K9fP49m3VNK5aQl/SIqjiX9xYvt46WXFu79FStCmza2I9+qVc6lSymlVP406BdRcSvpi8Cnn9rnPXoU/jpaxa+UUv6nQb+IittKexs22C84VatCUVb01KCvlFL+p0G/iIrTSnuQVcq/9FLbLl9YHTvaBXq++w4OHXImbUoppfIXkKBvjLnTGLPDGHPcGLPGGNMpn3NnGGMkj+2IP9PsTnEr6Scl2cdLLinadcqUgXPPtR0CAzC9uVJKFUt+D/rGmL7AJGAc0AZYBXxijKnl5i1DgWq5tu3Au75PbcGyl/SLw6x8a9fax3POKfq1tIpfKaX8KxAl/WHADBGZLiKbROQeIBkYktfJInJIRPa6NqA+UA+Y7r8ku1emjB22duxY+FdT//UX7N4NpUpB/fpFu1ZKStZSvfPm2ddKKaV8y6/j9I0xUUBbYHyuQ0uACzy8zEBgg4gEzWCvxETYssVW8Xs7O10o+ekn+3jWWXbxnMIQgVGjYPz4rGv88YetMXnwQTt/f1H6CnijWrVqtGvXzj83c8jx48eJcS18oByheeqscMnPaq5q3DDj78l54oEIYF+u/fuAbgW92RhTDrgeGJHPOYOAQQAJCQkkuRqhPZCamurV+S6xsa2B8ixatJZ9+w56/f5Q8cEHNYAGxMfvJilpa4Hnw5l5umePHaf/n//Y16+8chbbtlXg+us3ULHiX7zzTlY/CV974IEH/HMjB+W3DKwqHM1TZ4VTfhYmHvhCYWNTntxNyu+LDUgEBOica/8oYLMH778LOA5U9OR+/lhwR0TkxhvtAjKzZhXq7SHj5pvt53z1Vc/fkz1PDxwQiYmx13C3xcQUr8WLvFXY31HlnuapszQ/nRfKC+7sB9KAhFz7E4C9Hrx/IPC+iBxwOmFFUVyG7bk68bVuXbj3v/ceFLQEekSEbeNXSinlPL8GfRE5CawBuuc61B3bi98tY8y5wFkESQe+7IrDsL3jx2HTJtsO36JF4a6xd29W5z13jh615ymllHJeIBbcmQDMMsZ8C3wFDMZW+78CYIyZCSAi/XO9bxCwVUSS/JdUzxSHkv7GjZCWBk2aFH6BnKpV7XuP5DPDQlycPU8ppZTz/D5kT0TmAvcBI4G1QEegp4jsyjilVsaWyRhTBrgBeM1vCfVCcZh/v6hV+wB9+tgvDvlJS4Prriv8PZRSSrkXkKV1RWQqMNXNsa557PsHCNruoMWhet+JoF+hAgwfDhMm5F3NHxFhj4fzsEellAoknXvfAcVhVj4ngj7YcfjDhkFMjJ3kxxj7HOyXgjFjinZ9pZRS7gWkpB9uXLPypabC4cNQrlygU+QskZwT8xSFMTB2rA38771nO+1VqgRDh8Lff9v2/jAZ4quUUkFHg75DqlWDrVttaT/cgv7OnfbLTEKCc53sKlSAgQOzXr/2Gvz4o61R6NjRmXsopZTKSav3HRLO7fpOVe3np21b+7hmje/uoZRSxZ0GfYeE87A9fwb977/33T2UUqq406DvkHAu6TvVnp8fLekrpZTvadB3SDiP1fdHSb9lS4iMhF9+sR0ilVJKOU+DvkPCtXo/JQV27YLYWGjUyHf3iYmxgV8k60uGstLT7UiHK6+Ejz6qVuAER0op5Y4GfYeEa/W+q2q/ZcuCF8spKm3Xz8kV7M86y85SuHAhPP98Y847D775JtCpU0qFIg36DgnXkr4/2vNdinO7fkoKTJ9u5zCYPh1Wr84K9uvXQ40a8NhjEB9/gjVroH17uPVW2L8/0ClXSoUSHafvkOwlfRE7CU048Ed7vktxDPoiMGoUjB9va1KOHrVNKceO2WM1asCjj8Jtt0F0NHTo8C0rVnTi+edhxgzYvBlWrPB9LYxSKjxoSd8hpUvbFeKOHoV//gl0apyzbp19bNXK9/dq1QpKlixenflGjbJrERw/bmcjFLG/Q64vjjfeCEOG2IAPEBubxrhxtvSfmAhffw0vvxzYz6CUCh0a9B1iTFYVf7isBy8CW7bY502b+v5+0dHQooW9748/+v5+gZaSYkv4eS0+BDYfXnoJDh4881jDhjBlin0+YgT8/rvPkqmUCiMa9B3kmqI2XDrz7dtnay0qVrTz4/tDcarif++9gqvlIyJg3ry8j/XuDddcY2tF7rwzfBd7Uko5R4O+g8KtpO8q5ftyqF5uxSno793rvpTvcvRo/r9PL71k13r46CP3Xw6UUspFg76Dwq2kr0Hft6pWtf1A8hMXl/8iR4mJ8Mwz9vm999omA6WUckeDvoO0pF90rvkANm+2PdjDWZ8+FDjRTlqaHbaXn4EDoVMn2xxz1VVZw/70C4BSKjcN+g7Skn7RxcRA48Z2YpqNG/1330CoUAGGD7dD9PISF2ePly+f/3WMgebN7fMVK+yIgPvvt7UAjz+ubf1KqSwa9B2kJX1nuIYH/vyzf+8bCGPG2A55kDW3Q6lS9svPsGH2eEFGjYKZM3PuO3LEDgOcMMEeV0op0Ml5HBVOJf20NNi2zT5v0MC/927ZEubMKR5B35isKv4rr7R9GqpWtVX6BZXwIWvY3/HjeR8/etQef+ABz66nlApvGvQdFE4l/V274NQpOyNcqVL+vberpO+aGCicHTgAH35og/9LL0HNmt6935thfwMHFjqZSqkwEZDqfWPMncaYHcaY48aYNcaYTgWcH2WMGZPxnhPGmN+MMff6K72eqlwZSpSAv/6yATOUBapqH7KC/k8/hX979DvvwMmT0L279wEfnBn2p5QqPvwe9I0xfYFJwDigDbAK+MQYUyuft80BegCDgMbAdUDQVf5GRNjAD/Dnn4FNS1EFMujXrGnHnu/fb3ukh7M33rCPt95auPc7MexPKVV8BKKkPwyYISLTRWSTiNwDJAND8jrZGHMJcDHQU0Q+E5GdIrJaRJL8l2TPuar4Q71dP5BB3xjbrg/hXcX/88/www+2rd3Vmc9bTg37U0oVD34N+saYKKAtsCTXoSXABW7e1hv4DhhmjPnDGLPVGPOiMaa071JaeK4SVahXpwYy6EPx6MH/5pv28aabbG/9wnAN+3NX2o+N9WzYn1KqePB3R754IALIXWm7D+jm5j31gI7ACeBaoDzwEpAI9Ml9sjFmELYZgISEBJKSkjxOXGpqqlfn560xUI2kpM2ULh26xf2ffz4fiCElZTVJSYWfJaeweRodnQg0YsmSvbRt+0uh7x+sTp0yvPlmeyCKVq3WkJTk2dKMeeXnxRfbBZH27rW1JOnp8PrrLdm0qRJXXLGdiy/+jSL/WocxZ/7ulYvmp/MczVMR8duGDdQCdM61fxSw2c17lgDHgHLZ9l2ScZ2E/O7Xtm1b8cayZcu8Oj8vI0aIgMiYMUW+VMAcPSpijEhkpMjJk0W7VmHz9KuvbD62aVO0+wer99+3n69FC5H0dM/fl19+HjggMm2a/d0bOtRev1YtkbS0Iic3rDnxd6+yaH46z9s8Bb4XN3HR3yX9/UAakJBrfwLgrkI8GdgtIoey7duU8ViLM2sNAioc2vR//dX2mq9Xz65vHwgtWtjHjRvh9GmIDLPBpbNn28dbb82alKeoKlTIGpaXng7z58Nvv8FXX9lpepV3UlLskMi9e22zXZ8+No+VCmV+bdMXkZPAGqB7rkPdsb348/IVkJirDd/V0rzL2RQWXTi06Qe6PR+gbFmoUwdOnICtWwOXDl84cQI++8w+91UHuxIl4IYb7PN33vHNPcKViJ2+ODERBg/WaY1VeAlE7/0JwABjzO3GmKbGmEnYav9XAIwxM40x2ScVfRv4G3jTGNPcGNMBO+TvPREJuoFx4TBBTzAEfQjfznwrV0Jqqh2hUJix+Z668Ub7OG9e6M8b4ZT0dEhKglmzYOJEG8QnT27AggVZwXzUKDt98fHj9nzQaY1V+PB7pamIzDXGVAJGAtWA9djheK5Se61c56caY7phO+99B6QAHwKP+C3RXgiHqXiDKegvWGCDft++gU2LkxYtso89e/r2PmedBU2awC+/wBdfQI8evr1fsNu5EwYMgC+/zH2kBu+/b6dAfvBBeO45WxuTF53WWIW6gLSUishUYKqbY13z2LcZ23kv6GWv3hdxrr3Wn4Il6IfrWH1/BX1jbGn/iSdsFX9xDfoi8Prrtoo+NRWqVIFu3aBSJahYEXbs2MmSJXVYs8Y2iZQooP6zuE5rrH0cwkOYdY8KvNKl7ZaaCocOhWZpIFiCfjhW72/fbkve5cpB+/a+v58r6M+fD6+84n4Z33DjClC//mq/ZLm+OPbpAy+/DPHxWecmJe3k5Zfr8Mortur+yJH8r13cpjUWsfkyfrydMjo93a7Hce+9dg6IMWNCs3BTXGnQ94Fq1Wzns717Qy/op6TYtQPi4mzHpUBq0ACio+3iP4cO2UAZ6j75xD5ecol/RkY0bGirrdesscHv2mt9f89Ayh6gSpTIuS5B794wd27eJfm4OLuUcVSUrRE4fdr9PYrbtMbZ+zi4uL4YTZhgH8eO9X+6VOEEZMGdcBfK7fqunvKNGgX+23tkJDRvbp+vXx/YtDjFFfQvu8x/97zpJvtYHHrxZw9QuRciWrLE1nrk51//KnjVwuI0rbFr6WZ3izq5+jgcPOjXZKki0KDvA6Hcgz9YqvZdwqmK/9gxWLrUPvdn+3rfvvYL3EcfweHD/ruvvzkRoCpUsJ353DWDxMUVr2mNvVm6WYUGDfo+EMol/WAL+q7OfOEQ9L/80gb+s8/O+mLoD9WrQ+fOtkf6/Pn+u6+/ORWgxoyxvfOjo3NOChUdbZsAxowpelpDhS7dHH406PuAlvSd4yrph0MPfn/12s+La8x+OFfx793rTCc8Y2wbdXIyTJ5s+0UAnHsujB4d+GYvf9Klm8OPBn0f0JK+c7JX74fyTGgi8PHH9nkggn6fPrbU+vnn8Pff/r+/P1StWnBJ35sAVaEC3HGHnUypShVYscKO4S9OdOnm8KNB3wdCtaQvAtu22ef16wc2LS5Vqtjtn39sL/5QtXWrHa5XsaItMfpbpUpw0UX2H/SCBf6/vz/UquWbAFWlCrz1ln3++OPw3XeFS18oKmjp5uLWxyEcaND3gVAt6f/9tw2uZcvaIBEswmGSHlfVfo8eBZdGfeWaa+zjBx8E5v6+lJ6e1TPf3eJMRQlQPXrAfffZoXw33WT/ToqLMWNsX4aYGDs+3xj7GBNT/Po4hAMdp+8Dobrozo4d9rFeveBqt2zZ0k4ju24dXHFFoFNTOK6heoGo2nfp3RuGDLFD1/75B8qUCVxanPbOO7B6tf3b698fXnzRfrk6etQG+7S0ogeop5+GZcvgp5/sxDRvvulc+oOZq4/DsGE5Z+S77jo7d8bWrbbZ6Kuv7LlHjzZg6VJbS3DBBbZmK5j+nxR3GvR9ID7e/sP5+287g1VUVKBT5Jnt2+1jvXqBTUduoV7SP3nStgeDnf41UBISoGNHm5aPP85ahS/UHT0Kj2SsxDFunF2u+JFHzgxQRa2Cjo6Gt9+2kx3NmGHH9Afy5+lv2Zdu/uUXO8JhyRL444/cZ9bIMUqkcWP7RaxfPztbqU7lmyUQUxt7FfSNMecDPYDzsSvjxQL7gc3Al8CHIpLidCJDTUSEbQdMToZ9+3y7kpqTNOj7xvff26F6TZvawBtI11xjg/4HH4RP0B8/3gaeNm3gllvsvuwByknNmtkJgB59FO6803YwjYlx/j7B6vBhW+qfODFr1sLKleHii+HCC+38Bt9+u5X4+IYkJ8P//R9s3gyPPWa3EiXsl6fjx20NTHGdyjf7zJHZa6T8kR8etekbY24xxqzDrnl/PxAHbAVWY1e9Ow94DdhtjJlhjKnrm+SGDldnvlBq1w/WoN+8uf0D2LzZlppDTVKSfezaNZCpsK6+2j4uWmS/iIS63bvhmWfs8xdeKHixHCc88ID9Ard1Kzz7rO/vFwxEYPZsW2ofP942lwwcCGvX2lLqO+/AoEG2NH/ttbt54gm71sPvv9tapWbN7HXS0+3vnUjxXq44+8yRR474Nz8K/BMxxvwMPA0sAtoC5UWks4hcKyI3i0hPEWkKVAQGAlWAjcaYMFoM1Xuh2K7vCvp1g+wrW1ycHU1w+rStVgw1wRT0a9eGdu3sP5glSwKdmqIbNcqWkq65Brp08c89o6Lsoj1gmxNcI17C1YkTtvNiv372/9n558O338K0aXb55vy+aEVG2oWlXP9b8lLcpvIN9NTGnnwvfh2oKyIPi8iPInmPlhaRQyLyPxHpia3+P+hgOkOOlvSdFapV/KdO2Q5OYGfFCwbh0ov/t99g5kwbdFylfX/p0sW2U584AXffHdpzSOQnJcUuDjVnju34+eab9ve5XTvPr6FT+eYU6PwoMOiLyCQROV7Qebne85OILC58skJfqJX0T52y/0SNsaXBYBOqQf/77+039yZNgmfWMlfQX7DA/txD1fPP29qfvn3tioz+9txztu/A4sX2H3m42bULOnSA5cvtipsrVsCAAd43oXgyle+RI7appjgI9NTGOk7fR0KtpP/bb7a9rUYN29Em2ITqdLzBVLXv0rix7Sdx8KAdghaK/voLpk+3z1099/2tShV46in7fOjQ8Bq7/9NPthp/0yb7u/LNN7YqvzA8mcoX4N134cCBwt0jlAR6amOPg74xprcx5k1jzGpjzNaMbXXGvt6+SV7oCrWSfjBX7UPolvRdQd9f7c2eCvUq/hdftB3CevXK+kIYCAMHwnnn2S/3//1v4NLhpF9/tVX6e/faHvkrVxZtBJInU/mC/YJx3nn2/uEs0FMbe9KRr4IxZiXwAXAhdojeNxnbfqAr8IEx5itjTDEecZlTqJX0gz3o169vhwP9/rttZwwF2dvzgzXoz5/v2T/kYHL4sF0IB2DEiMCmpUQJeOkl+3zCBNujP5Tt22cD/p9/2jkIPvmk6PMbeDKV7z332CGX27bZJoW1a4t2z2B27Fj+fSJ8PbWxJyX954FaQBcRqSMivUSkX8bWS0TqAp2B6sB43yQz9GhJ31kREVnDftavD2xaPLVmjW2rbNzYv0vpeuKss+zP+s8/YdWqQKfGO6++apsmOnWyASLQzjkHbrvNfsm7//5Ap6bwDh+Gyy6z/wvatrW1QE419RU0le+kSXbp6Ysvtl88unSxr8PJ/v2202ft2rb2BGw+uCZv89fUxp4E/SuB4SKywt0JIrISeBjo7VC6Ql72oB8KPXuzT8EbrEKtij9Yq/bB/rO59lr7PJQ6obnGMUPgS/nZjRtn16z4+OOs1RRDyYkTdg6HH3+0nSIXLXJ2mmbXVL579tj5FJ580j4mJ9v9xtj7ffyxrf4+fBguvRQ+/NC5NATKyZP2szZoAFOm2L5TffrYjpH799taqzFjzswPX/Ek6EdjJ+ApyEHAowlnjTF3GmN2GGOOG2PWGGM65XNuV2OM5LE18eRegRIXZ+elPnkyNJYyDfaSPoRe0HeVVIKpE192ffrYx/fft/+IQsFbb9kv0q1b20VwgkVCAowebZ/ff78NoqFCxNZULF1qCytLlthOir7gminx8cftY+4q7OhoOzxwyBCbh9deC2+84Zu0+MPixdCihS29Hzpkm05++skOx+vY0a66mV9++IInQf9r4DFjjNvvfRnHRmBn7MtXxqQ9k4BxQJuM93xijKlVwFubA9WybUHfehZK7foa9J116lRWFV4wlvTBVkvXrGmHSq1eHejUFCw93U5aArbHfrBN23r33Vkz9U2aFOjUeO655+yaAqVLw6efBn5yrogIWyJ+4gn7M//3v+3wzFCSkmLXgOjRw/4+NG5sazE+/dR+CQgkT4L+fUAzYJcxZpYx5mFjzKCM7WFjzExgZ8Y5wzy43jBghohMF5FNInIPkAwMKeB9f4rI3mxb0Hc/Sky0j8Ee9FNS7BYXZ+fRDlauoL9+ffA3mfzwA6SmQqNGWb8HwcaYrNJ+KFTxL1pkO3rVqZOV7mBSsmRWsB87NjTGnX/ySdaQx9mzCz8sz2nG2JoTV34OH27n7hex/6umT7d5PH168HXsXbDADnOcMcPWXDz9tC2o9OwZHF9UPZmcZyNwFvAW0B5bQn8lYxsHdABmAq1FZEN+1zLGRGGn8s09AegS4IICkvK9MSbZGPOFMebCgtIdDFz/7PfsCWw6ChKsS+rmlpBgVzA8dMj24g9mwdyen132oB/sX6QmTrSPd99d8IxmgdK9u13CODU1+Dv1bdkCN95of+5PPglXXRXoFJ3p3nth1iz78x43zvZ6r1YNBg+2UzDff7/9P/v444H//U1JgZtvtvmYnGyXFf7pJ3j4YfuFMFh4tMqeiCRjF9q53xgTA7iG5h0UEW+W7YgHIoB9ufbvA9wtUumqBfgO22egH/CFMaZLXp0LjTGDgEEACQkJJLn++3ogNTXVq/MLcvp0PaAWK1dup06d3xy7rtO+/LIy0JyyZfeTlORs13in87RmzbPYv78C//vfz7RvH7wzecyf3xKoRJUqG0lK+tOx6zqdn+npEB/fnt9+i+bVV9fQpElwzjCzfXspvvjiHGJi0mjc+GuSkk47dm2n8/SGG6L59NNzmTcvgmee+Znzzgu+39PU1AjuuutsDh0qRadOf9Gx4wacygKn87NGDRgzphKjRzfjhx8iaNp0PzffvIno6KzK3hIl7KI/gapV+/77CjzzTBP2748mJiaN22/fTu/eu0lOdqam19E8FRG/bdjleAXonGv/KGCzF9dZBCwo6Ly2bduKN5YtW+bV+QV54QURELnrLkcv67hnnrHpvO8+56/tdJ7ee69N61NPOXpZR50+LVKmjE3nH384e22n81NE5J57bFofesjxSzvm9tt997fkizx97jmb3nr1RI4edfzyRZKWJnL55TZ9LVqIHD7s7PV9kZ8HDohERdk0u9tiYkRSUhy/db6OHBG5++6sNJx/vsiWLc7fx9s8Bb4XN3HRk8l5rvH2i4Qxppox5vw8Du0H0oDcq4onAN6MaF8NNPQ2Xf4WKm36odCJzyUUOvP99JOdkrVuXahePdCpKVj2oXuBriLNy19/2SpesNW9oWDoUNtha/t2Wy0dTMaMgY8+sj3pP/zQ2aF5vvLeewVXkft70Z4VK+woksmT7WqC//2v3dcwyCOTJx35XjLGrDXGDDbGVMzvRGNMJ2PMNGAbcMbkmCJyElgDdM91qDse9PzPpjW22j+ohUqbvgZ9Z7l67XdyOxA1uHTsaIdobd9u/2kFWyepadPs8K1evWzHyFBQsqRdTx7sCoCBXhLa1fntX/+y7fclStihcfXrBzZdngr0IjXZpabaGQQ7d7Y986tUsZ0hhwyxwT/YeRL0G2Kn4B0D7DPG/JzRi3+CMeYpY8wrxpglxpgDQFLG+d1FZJqb600ABhhjbjfGNDXGTMJW+78CYIyZmTEigIzX92XM+9/QGNPcGPMUdhKgyYX8zH6jQd95zZvbx19+sXMgBKMVGT1NQiXoR0TYiVkALroouDpJnTwJU6fa50OHBiYNhdWhA9x+ux2+OWRIYPJQxP78EhPhjjvs0DywQX/FiuCs2cmLJ4vUxMb6fiXLJUtswWNytujz5592Yp1A/614ypPe+0dFZAxQA7gZW1JvC9yG7dx3BbZz3iSguYhcKCJuS+0iMhc7DHAksBboCPQUkV0Zp9TK2FyigOeAn4EVGef3EpGgXyok+zj9YP1FSEuzS2iCHQoV7EqXtqWTU6cCX3rKi0hWSb9jx8CmxRupqfYxLS1rop4jR7JmwBs1KjDpeu89+6W5WTM7F3yoeeQR+zublAT9+vm/5mTUKPvzO3485/+g06cD+3P1lieL1Bw9ajv9ecLbYX+bNsHll9tZAnfuPHOUUzD8rXjMXWN/Xhu2932MN+8J5BbojnwiIuXL2w4ef/3l+KUdsXOnTV9iom+u74s8vfZam+YZMxy/dJFt3WrTVrmySHq689f3VSep6Ojg6ySVni5yzjn2/q++6rv7+CJP09NFRo60+WZMVj5GRdn9vvjdyO3AAXt/f/9cfZGfIjbf4uLy/hwlSmQ93nabyI4deV8j+8/F9Z5SpezrvH4uf/1lO49GRNhzS5cWiYwM/jyliB35Iowxo40xKdihdYeNMe8bY8r79NtImAj2Kn5X1X6gZ+HyRps29vHHHwObjrxkL+UH85wH2b33XsFtkf7uJAV2IaDvvrNTld58s3/vXVTuStgnT9rZ5fxRGnzvvYLnMwjEz7Ww8lu05+GHs2ZpfOMN2/fjzjvPnCAp+8/FXY2WiJ2h8rbboFYtOzugiG32Gj264EWIgj1PPWnTH4wdUvcjdhW9/wOuAl7wYbrCRqgE/VBoz3cJ5qDvas8Ppar9YOokld0LGf9hBg8uuD03mKSk2OmC3eXpsWP2+MGDvk3H3r02oOUnED/Xwspv0Z5x4+Cpp2w1/M032+aLl1+2TYFXXmkD9w8/5P9zOXrUzp531llw/vnw5pv2Z9WrF/z8s73e0aPB+bfiDU/6Gg4EpovIHa4dxpg7gMnGmDvE9shXbrja9TXoO8cV9Neutd/WS3jy1dVPQq0TH2R1ksovQMTF+b6TVHY7dsD8+bYX/F13+e++TvCkhA22NDhwoO/S8acHc0L5++fqBNeiPXlp2NAO7xwxwpbK582DhQvtBgXXvp0+bUcGVapkS/qDBtnV8VyC8W/FW578u6wH5K6smIvtvFfb8RSFmWAfqx+KQb9aNTsl7+HDWVMIB4N9++wQnlKlsr6YhAJPOkmlpcF11/knPQAvvWS/0PXtG7xrF7jjSc3J8ePw66++S8OPP9qSakH8/XP1l2bN4N137XTdr79uP2NsrGcdqq+9Fv74A559NmfAh+D8W/GWJ0G/NHA41z7XXJ0hMK1DYGn1vm8EYxW/qz3//PNDY7yuS4UKdkETd1XocXH2uD+W/QT7Ze611+zzYJ+/Pi+eDC8DmDvXTuJUEG97mm/caBd3OXLEThAUG5v3ef7+uQZCjRq2xP7uu7YpICYm//NLlbI99N2dF2x/K4XhacVodWNMPdeGLf2fsT/jmMpGg75vZK/iDxahNilPdtk7SbmaS6Ki7Othw+xxf3njDRsMGza0y5EGyyRBnvKkNGiMHfp15ZW23Tgv2cfYezp3wpo1dtKYvXvtnAvffQcPPJB35zd//1wD7frrCz7Hk1J6fh0KQyJP3XXrd21AOnbq3NxbnvsLup4/t2AYsrdypR3Gce65jl+6yA4ftmmLjrbzcfuCr4bvvPuuTXvPnj65fKG0bWvT9PnnvruHr/LT5cABkf797edo3Nj/w/ROnRIpV87e3zXULb8hVU7wRZ7mN7wsLs4OA6tWzb6+4gqRkye9v8bIkTnPX75cpGxZe7xXr5xz/h84IDJtmsiYMfbRlz9XX/+OFoW3eZqfYM5T8hmy50kl5K0++K5RbARzm76rPbxu3eDqDOeJYKve/+cfm5bISFu9H6oqVIAXX7TVoZs322WM/VlVecMN9p6QVZJ1dZqaMME+jh3rv/QUlqu0N3687dR39Kit+k1LyyoNDh5sl15euBBuuSVrCVnIGgFw/Hje1z961B5/4AH781m82M6qeOyYLdHOmmVralzy6/xWnHjyc/FUqOZpgUFfRN7yR0LCVfZZ+YKtp3n2oB9q6tWzC4UkJ9sOdAm5l3Dys2++sT/fdu1sVV8oK1fOrgk/Zw7873/w6KP+uW9KCnyQzzybuQNdMHMNLxs2zPbm37vXtvVfd11W2lu0gE8+gYsvtsvCJifbvgz163s+xn72bNtMMHGiDVy33WbXKvBk9EBx5MnPJdyFUHej0BQTYycXOXAA9u+3izMEi1AO+iVK2BWuVqywJewePQKXlpQUmDTJPi9Xzr6uUCFw6XFC//426M+caYc/+WOioXHjCu5d7Zr4JFRKWAWVBs891654d911dqreli1tPhw6VPAIgCNHbNv+wYP272HECPjPf4KrYBGsQrWU7gT99fCDYO3MF4qz8WUX6Cr+7B2tPvnE7lu+PHQW3shP9+629mTzZluL4Q/vv1/wOcE+8UlhdOlie9zfdJOtnr//fvtlq6ClZMEG/Nat7Qxy48ZpwFcF018RP8hexR9MXCX9UOu57xLooJ/XlJ4nToTQwhv5iIyEAQPsc1dbui+tXOnZnAvBPvFJYcXH26aUBQvsl8bt2z1bRXLsWNtDv10736dRhQcN+n4QrCX9UK7eh8AG/YKmWnW1P/t6qlVfuvdeW9p8/33Yts2393rySftYUFt0sE98UlRXXAEbNtiJYZo0cd+sUrKkXdN95MjQmhNCBZ4GfT8IxqAvEvpBv2pVGyS2bbM9zv05ljvcFjPJS2KiXQ5WxC4S4yurVsHnn9uOmffdF9oTnzihfHl48EFb5f/YY3aBF9dkMXFxWQvMuPqRKOUNDfp+EIxB/88/bWm0fPnQ+yfqakuvUydrEpSHH/ZvW3qwLlLjtOHD7eOMGZ7N5V4YrlL+vffCc8+F+MQnDnL1NE9Otl9qx4yxvfSTk+3+UFnFUQUXrRjyg2Bs0w/l9vzsbekuruf+GssdDgtveKJpUztr3IIFMHmy80H3m29gyRIoXdp2YNMhVWcqzj3NlfO0pO8HwVjSD9Wq/WBpSw+HhTc89dBD9nHyZEhNdfbarlL+PffYlc1cXIHu8cftY3EN+Eo5TYO+H2jQd06wtKW7Ft5wN6wqnNqfO3SA9u3tF6433nDuup9+ardSpWzJXinlexr0/cBVxbt3b8GlQ38J1TH6wdSWPmYM1Kpln0dHh3f7s6u0//zzcOpU0a+XmmqnoQXbXBMfX/RrKqUKpm36fhAdbasu//7bzsoX6CljIXTb9IOpLT0tzU4BDLYd+vjx8G1/vvJKaNQItmyx65O7AnZhPf447Nplh11qKV8p/9GSvp8EWxV/qFbvB1Nb+g8/2BJrw4Z2iFU4tz+XKJHVOfLBB7Nqigpj9Wo73Cwiws41r+PMlfKfgAR9Y8ydxpgdxpjjxpg1xhiPViA3xnQ0xpw2xqz3dRqdFkxB//Rp+O03+7x27cCmxVuutvRgGMv95Zf2sWtX398rGFx3nV3BLTXVztZXmKaqkyftFyMRu3DO2Wc7nkylVD78HvSNMX2BScA4oA2wCvjEGFOrgPdVAGYCX/g8kT4QTEH/99/tP+zq1bMm/QglY8bkHMudnT/b0pOS7GOXLv65X6AZA1On2iaMFSvghRe8v8azz8K6dXYluSeecD6NSqn8BaKkPwyYISLTRWSTiNwDJANDCnjf68BbwNe+TqAvBNNY/VCt2ndxjeXes8cGntGjITbWHhs0yD+Tlpw+beeLh+IT9MH2TXn9dfv8scdgfR51bikpMH26/RlNn541U+Jnn2U1EUyb5r62RinlO34N+saYKKAtsCTXoSXABfm8704gAfiP71LnW8FU0g/1oO/iGsv9xBNZgferr/xz77Vr4fBhW2KtUcM/9wwWPXvaL1cnT9ppel0Lw2RfdXDwYNsr//777es+faBXL3vu3XfDRRcF9jMoVVz5uwtNPBAB7Mu1fx/QLa83GGNaAk8A54tImimgGGeMGQQMAkhISCDJVQfrgdTUVK/O98aBA/FAC9at209SUuC6JKSlwaJFdYHaHDu2ky++2FnguPei8GWeZpeYWBuoy7vv/kHVqj5eHQZ4990aQAMaNUomKWmzz+/n4q/8LEjv3hF89FE71q6N5ZxzDjJ8+GYiIo5RsaJd0z27b7+tyrx5jRExXHvtH1x99TaC4CNkCpY8DRean85zNE9FxG8bkAgI0DnX/lHA5jzOjwY2Av2y7RsNrPfkfm3bthVvLFu2zKvzvfHNNyIg4mWSHJOeLjJypEhMjE0HiERF2dcjR9rjvuDLPM1u6VL7mc4+2y+3kyuusPebOdM/93PxV356YvVqkSpVbD5ER4tERmb9buW1RUaKHDgQ6FSfKZjyNBxofjrP2zwFvhc3cdHfbfr7gTRsVX12CUBe06lUA5oCb2b02j+N/YLQPOP1JT5NrYMC3aaf13z1J0+Gx9rvAOeea4eA/fST81PF5paWBsuX2+fFqT0/t3PPhU2bbE/+EydsP4f8REfbGRWVUoHj16AvIieBNUD3XIe6Y3vx57YbaAm0zra9AmzLeJ7Xe4JSIGflC5b56n2pVCk70Utamh0H7kvffw+HDtmJjWrlO+Yk/FWsCG++Cf37F3xuOKw6qFSoC0Tv/QnAAGPM7caYpsaYSdhq/1cAjDEzjTEzAUTklIisz74BfwInMl77uEznnKgoO9Voejo88kjOXs2+Fizz1ftahw720ded+RYtso+XXebb+4SSjh0L7o0fDqsOKhXq/B70RWQucB8wElgLdAR6isiujFNqZWxhw9Wr+e+/7evx47N6Nftj/fdgmq/el/wd9Hv29O19QkmfPvYLbX7CZdVBpUJZQGbkE5GpIlJHRKJFpK2ILM92rKuIdM3nvaNFpIVfEuoQV3t69uB+5Ij/2tNd89XnJxxKYa6g//XXvmtC2bfPVu/HxBSfmfg8EUwzJSql3NO5930sGNrTg2m+el9KTIQ6deCff/KeNMYJn35qHy+8UCeXyS33TInhvOqgUqFKg76PBUN7enEqhfm6il+r9t3LPVPik0/ax+Rku98fMyUqpfKn61v5WLC0p7tKWePGZbW9liplS/jhVArr0AH+9z8b9O+809lrnz4Nixfb5xr03XPNlKiUCj4a9H0sWNZ/d5XC5s+HDRvgjjugbdvwW/vdlyX9VavsUL3Gje1wPaWUCjVave9jwdSeLpK1pO5//xuea783bw5ly8KuXbB7t7PX1qp9pVSo06DvY8HUnn7ggO3kVqaMnVQlHEVEQPv29vmyZc5eW4O+UirUadD3g+y9ml2dmWJj/d+reVvGOjQNGoR3p6pevezj/PnOXfP33+068KVKQadOzl1XKaX8SYO+H2Tv1dyggd03ZIj/ezVnD/rhrHdv+/jJJwV3ovTUJ5/Yx27d7BzySikVijTo+1GFCnDeefZ5y5b+b08vLkG/Zk27GMyxY1m97YtKq/aVUuFAg76fuRZocXWo86fiEvQBrrnGPn7wQdGvdeIEfP65fa5BXykVyjTo+1nNmvZRg75vuYL+woV2CeGiWLTIDrls1Qpq1Ch62pRSKlA06PuZlvT9o2FD24Ry6BAsXVq0a02bZh8HDChyspRSKqA06PtZoIL+wYOwf78dNVCtmn/vHShOVPHv3Gn7BURFebZmvFJKBTMN+n6WPej7eknd7H791T6G+3C97FxB/8MPC7/q3uuv259Tnz5QqZJjSVNKqYDQoO9nZctCuXK2Z/nff/vvvsWpat+lZUuoXx/++qtw0/KePg1vvGGfDxrkbNqUUioQNOgHgKu0//vv/rtncQz6xhStin/RIju3QqNG0Lmzs2lTSqlA0KAfAIFo1y+OQR9yBv28mlNSUmD6dDtJ0vTp9rWLqwPfoEHFp0lEKRXedJW9ANCg7z/nnguJibZW5fvv4Zxz7H4RGDUKxo+38/UfPWrXQbj3XrsWwu2321n4oqLgllsC+xmUUsopWtIPAA36/lOihO2EB/Cf/2TtHzUKJkyA48ftGHwR+3j8uN3frx+kp9uagvj4wKRdKaWcpkE/APwd9I8cgb177ZzxxXFymUcesSsLLlhg2+lTUmwJ3928/EePwooV9rl24FNKhRMN+gHg76DvGq5Xr54t+RY31arB6NH2+b33wjvv2Cr9glSpAl27+jJlSinlXwEJAcaYO40xO4wxx40xa4wxbhcrNcZ0McasMsb8bYw5Zoz5xRgz3J/pdZq/p+J1Ve3Xr++f+wWje+6BZs3sF6D33/ds9b0LL9QOfEqp8OL3oG+M6QtMAsYBbYBVwCfGmFpu3pIKvAh0BpoB/wGeNMbc6Yfk+kRioi1xJycXfV54TxTX9vzsSpaEyZPt8xUrICam4PMvvtj36VJKKX8KREl/GDBDRKaLyCYRuQdIBobkdbKIrBGROSKyQUR2iMhsYDHgtnYg2JUsaQO/COze7fv7adC3LrwQ+vaFU6fsynn5KVECrrvOP+lSSil/8WvQN8ZEAW2BJbkOLQEu8PAabTLO/dLZ1PmXPyfo0aCfZfx4KFXK9sx3164fGwsPPgjly/s1aUop5XP+HqcfD0QA+3Lt3wd0y++Nxpg/gMrYND8pIq+4OW8QMAggISGBpKQkjxOXmprq1flFERPTFEhg8eJNpKfnzg5nbdhwPhDD/v3fkJR03Kf3ys2feeqpf/+7Oi++2PCM+fgjItK5++4fOffcf0hMhCBLNhCc+RnqNE+dpfnpPEfzVET8tgGJgACdc+0fBWwu4L11gZbAQOAA0K+g+7Vt21a8sWzZMq/OL4qHHhIBkf/+17f3OXrU3icyUuTUKd/eKy/+zFNvrF8vMnOmyNChIq1aiVSpIvLvf4ukpAQ6ZfkL1vwMZZqnztL8dJ63eQp8L27ior9L+vuBNCAh1/4EYG9+bxSRHRlP1xljEoDRwCynE+gv/hq2tyMj1+rUgUidfzFT8+Z269cv0ClRSin/8WubvoicBNYA3XMd6o7txe+pEkC0U+kKBH8FfW3PV0op5RKIst8EYJYx5lvgK2Awttr/FQBjzEwAEemf8foeYAewOeP9nYHhwFT/JttZGvSVUkr5m9+DvojMNcZUAkYC1YD1QE8R2ZVxSu7x+hHAM0Ad4DTwK/AIGV8SQpUr6O/aZYfu+WoSGA36SimlXALSyisiU3FTUheRrrleTwQm+jxRfla+vB06lpoKhw75bniYBn2llFIuxXAm9uBgjH+q+DXoK6WUctGgH0B169pH14I4Tjt50jYflChhe+8rpZQq3jToB1CTJvbxl198c/0dO+zMczVr2mV1lVJKFW8a9AOocWP7uHlz/ucV1saN9rFZM99cXymlVGjRoB9Avi7pr19vH5s39831lVJKhRYN+gGUvaRvZxp2livot2jh/LWVUkqFHg36AVSlCpQrBwcPwp9/On99DfpKKaWy06AfQMZkVfE73a5/4gRs2WLv0bSps9dWSikVmjToB5irit/pdv0tW+D0aahfH+LinL22Ukqp0KRBP8B8VdLXqn2llFK5adAPMF+V9DXoK6WUyk2DfoD5qqS/YYN91KCvlFLKRYN+gNWvb6fJ3bHDdr5zipb0lVJK5aZBP8Cio6FePTtdrmtxnKI6cgS2b4eSJaFhQ2euqZRSKvRp0A8CTk/Hu2mTneyncWOIinLmmkoppUKfBv0g4PR0vFq1r5RSKi8a9IOA0yV9nXNfKaVUXjToBwGnh+1pSV8ppVReNOgHgezD9pxYeEeDvlJKqbxo0A8ClStD+fJw6BDs21e0a6WkwO7dEBsLdes6kjyllFJhQoN+EHBy4R3XpDzNmkFERNGupZRSKrwEJOgbY+40xuwwxhw3xqwxxnTK59xrjDFLjDF/GWP+McasNsZc6c/0+oNT7fo6E59SSil3/B70jTF9gUnAOKANsAr4xBhTy81bugBLgV4Z5y8C5uf3RSEUOVXS1/Z8pZRS7kQG4J7DgBkiMj3j9T3GmB7AEGBE7pNFZGiuXU8aY3oBvYEVvkyoPzlV0tegr5RSyh2/lvSNMVFAW2BJrkNLgAu8uFQZIMWpdAUDJ0r6IrBunX2uQV8ppVRu/i7pxwMRQO4+6vuAbp5cwBhzF1ADmOXm+CBgEEBCQgJJSUkeJy41NdWr85106pShRInO7NwJS5asICoq3etrHDhQkr//7kCpUqfZunWlY3P5F0Ug8zQcaX46T/PUWZqfznMyTwNRvV9oxphrgeeAviKyK69zRGQaMA2gXbt20rVrV4+vn5SUhDfnO61+fdi6FRITOxeqpL54sX0866xILrywq4MpK7xA52m40fx0nuapszQ/nedknvq7I99+IA1IyLU/Adib3xuNMX2wpfv+IrLQN8kLLFcVv6td3ltffmkfO3RwJj1KKaXCi1+DvoicBNYA3XMd6o7txZ8nY8z12IA/QETe810KA+u88+zj8uWFe//SpfbxwgudSY9SSqnwEojq/QnALGPMt8BXwGAgEXgFwBgzE0BE+me8vgEb8IcDy40xVTOuc1JEDvg57T7lCtbLlnn/3sOH4fvvITISOnZ0Nl1KKaXCg9+DvojMNcZUAkYC1YD1QM9sbfS5x+sPxqZzYsbm8iXQ1Zdp9bdzzoFSpeywvb17oWrVgt/jsmIFpKVB+/ZQpozv0qiUUip0BWRGPhGZKiJ1RCRaRNqKyPJsx7qKSNdcr00eW9e8rh3KSpbMKqV721HTVbV/0UWOJkkppVQY0bn3g4yrg6a3Vfyu87U9XymllDsa9INMYdr1DxyAtWshKgou8GaKI6WUUsWKBv0g07atbZPfutUukeuJL7+0s/G1b2+X1FVKKaXyokE/yERGQqeMpYQ8bdfX9nyllFKe0KAfhLxt19f2fKWUUp7QoB+EvGnX37cPNmyw1fquyX2UUkqpvGjQD0Jt2kC5crB9O/z2W/7nupoAOna0HfmUUkopdzToB6GICOjc2T4vqF1f2/OVUkp5SoN+kPK0XV/b85VSSnlKg36Q8qRd/48/7NC+MmXsUD+llFIqPxr0g9RZZ0GFCrBrF+zcmfc5b75pHzt3tkP9lFJKqfxo0A9SJUpAly72+VtvZe1PSYHp0+Huu+HJJ+2+++/3f/qUUkqFHi0fBrFBg+DDD21wP/ts+PZbGD/efiE4etSeExFhO/tddBEYE8jUKqWUCnYa9IPYZZfB2LHw+OPQp48N9seP5zwnLQ0mTLDPx471fxqVUkqFDq3eD3KPPQbXXAMnT54Z8F2OHrU1AAcP+jVpSimlQowG/SBnjK26L1HATyoiAubN80+alFJKhSYN+iHgwAFIT8//nKNHYe9e/6RHKaVUaNKgHwKqVoVSpfI/Jy7OnqeUUkq5o0E/BPTpYzvs5SctDa67zj/pUUopFZo06IeAChVg+HBbms9LXJw9Xr68X5OllFIqxAQk6Btj7jTG7DDGHDfGrDHGdMrn3GrGmLeNMb8YY9KMMTP8mNSgMWYMDBsGMTG2qt8Y+xgTY/ePGRPoFCqllAp2fh+nb4zpC0wC7gRWZjx+YoxpJiJ5LSQbDewHngYG+S2hQcYYOw5/2DB47z3baa9qVVulryV8pZRSngjE5DzDgBkiMj3j9T3GmB7AEGBE7pNFZCdwL4Axpo+/EhmsKlSAgQMDnQqllFKhyK/V+8aYKKAtsCTXoSXABf5Mi1JKKVXc+LtNPx6IAPbl2r8P0AFnSimllA+F3dz7xphBZLT9JyQkkJSU5PF7U1NTvTpfFUzz1Fman87TPHWW5qfznMxTfwf9/UAakJBrfwLgyHxyIjINmAbQrl076dq1q8fvTUpKwpvzVcE0T52l+ek8zVNnaX46z8k89Wv1voicBNYA3XMd6g6s8mdalFJKqeImENX7E4BZxphvga+AwUAi8AqAMWYmgIj0d73BGNM642lZID3j9UkR2ei/ZCullFKhze9BX0TmGmMqASOBasB6oKeI7Mo4pVYeb/sx1+srgF1AHV+lUymllAo3AenIJyJTgalujnXNY5/xdZqUUkqpcGdEJNBp8BljzF/YGgFPxWM7GyrnaJ46S/PTeZqnztL8dJ63eVpbRCrndSCsg763jDHfi0i7QKcjnGieOkvz03map87S/HSek3mqq+wppZRSxYQGfaWUUqqY0KCf07RAJyAMaZ46S/PTeZqnztL8dJ5jeapt+koppVQxoSV9pZRSqpjQoK+UUkoVExr0szHG1DLGLDTGHDHG7DfGvGiMiQp0ukKFMWaSMeZ7Y8xxY8xON+e0NMZ8aYw5ZozZbYwZZYzRyZdyMcacZYx5xxjze0ZebTbGPGSMKZHrPM1PDxljKhtjFhtj9hhjTmTk7RRjTLlc52meeskYE5+RV2KMic91TPPTCxl5mHsbnOucQudp2C2tW1jGmAjgY+BvoBNQCXgLMMA9AUxaKCmBzbOWwCW5DxpjygKfAcuBc4AmwJvAEeB5/yUzJLQF/gL6Ab8B5wLTsX+z40DzsxDSgfnAo9iJThoAU7D5ej1onhbBm8Ba7DoqmTQ/C20g8FG214dcT4qcpyKim+3MeBn2n0LNbPtuBo4DZQOdvlDagOHAzjz2DwEOA7HZ9o0EdpPRqVS3fPP1WWCN5qejeXovkKx5WqQ8HAp8AVwECBCv+Vmk/BSgTz7Hi5SnWr2fpT2wSUR+z7ZvMRCNLXWpomsPrBCRY9n2LcaWDuoEJEWhpSyQku215mcRGGMSgWuAL7Pt1jz1gjGmDfAw0B9baMpN87NwJmU0MX9njBmcq1mvSHmqQT9LVWBfrn37gbSMY6ro8srjfdmOKTeMMWcDA4CXs+3W/CyEjL4SR7Elo3+AW7Md1jz1kDGmFDAHuEdEdrs5TfPTe6OAvkA3bP4+j22ScilSnmrQVyrIGWMaY/ubTBSR9wOdnjBwP3A2cBVQD5gY0NSErheBlfo76SwRGSsiK0VkrYg8DzwJPOjU9TXoZ9kLJOTaFw9EZBxTRZdXHidkO6ZyMcY0AZKAOSLySK7Dmp+FICJ7ReQXEVkA3AEMMsbUzDiseeq5i4EBxpjTxpjT2HZ9gL3GmP+6nqP5WVSrgbLGmOz5Vug81aCf5WugqTGmRrZ93YETwJrAJCnsfA10MsbEZNvXHdgD7AxIioKYMaYZNuDPE5H78zhF87PoXP8DozMeNU89dwlwFtA6Y7s9Y39XbC0AaH46oTW2Q/nBjNdFy9NA91QMlg1bol8HLAXaYNtTdgMvBTptobJhh0C1BiZk/AK2ztiiMo6Xw34TnQO0wHaiOgw8EOi0B9sGNMe2083BttNlbtnO0fz0Lk8vB27JyKs6QC9gI/C15qkj+duVM3vva356l4dXYIfrtQDqY79IHQImOZWnAf+QwbQBtbBjI49ix+u/CEQHOl2hsmFLpZLHVifbOS2x40uPA8nAE+jQnbzycrSbvJRc52l+ep6n3bClpIPAMWAL8AxQQfPUkfw9I+hrfnqdhz2AH7EdTI9gC6JDgUin8lQX3FFKKaWKCW3TV0oppYoJDfpKKaVUMaFBXymllComNOgrpZRSxYQGfaWUUqqY0KCvlFJKFRMa9JUKUcaYAcYYybYdMcbsNMbMN8Zcb4wxhbxu14zrdXU2xfneM8dn8dE9Rma7xx++uIdSwU6DvlKh7zrscps9gcexU0e/A3xmjIkNZMIK4RrsZ/GFNzOuvchH11cq6EUGOgFKqSJbKyLbsr2eZYyZB8wDngXuCUyyCuVHEdnpiwuLXf51tzHmL19cX6lQoCV9pcKQ2OVO/w8YaIyJc+03xsQZY54xxuwwxpzMeHzMGJPv/wJjzCXGmEXGmGRjzFFjzHpjzAPGmIhs5yw0xvyYx3vrGmPSjTGDvf0cxpg6GdXxA3LtP6MJwhhzqTFmlTHmkDEm1Riz2Rgzytt7KhXONOgrFb4WYVePawdgjIkEFmMX8ZgEXAa8hm0SeK6Aa9XDLp16G3ahmrew6wP8N9s5LwOtjTHn5nrvIOw84v8r/EfJnzGmHrAA2AH0Ba7ELvxUylf3VCoUafW+UuHrt4zHahmPNwIdgS4isjxj3xcZ/f2eMMY8IyJ/5nUhEXnF9Tyjg+AKIAoYbox5VETSgU+B7dg16r/NOLckcCvwPxH5x8kPl8vZGekZIiKHM/Yt9eH9lApJWtJXKny5eu+7esP3AHYBq4wxka4NWAKUBM53eyFjqhljXjXG7AJOAqeA/wDlgSoAGYH/VeAGY0y5jLf2BhIy9vvS2ow0zTHG9DHGVPHx/ZQKSRr0lQpfNTMekzMeqwC1scEx+/ZtxvFKeV0ko71/AXY9+v8AFwHnkFW1H5Pt9NeBCKBfxuvBwLcickZbv5MyOjJeiv2fNgvYa4z5xhjTxZf3VSrUaPW+UuGrF3a97TUZr//Gtnlf7+b8nW7218f2C+gnIrNdO40xV+Q+UUT+Nsa8C9xhjFkMXIjtQ1BUuf9Xlc7j3suAZcaYaKADMAb42BhTR0T2O5AGpUKeBn2lwpAx5lpsZ7ZJInI0Y/enwLVAqoj84sXlXL3/T2W7fkngX27Onwp8je0keAiY48W93GmR67XbpggROQEsNcaUxo5gqAto0FcKDfpKhYPWxph4bEe2Wthq+OuAz4AR2c77H7ZT3RfGmOeBnzLeUx/7BaF3ti8I2W3C9gX4rzEmDRv873eXGBH5JmPoXmfgJTfX9NbtxpjfgR+xtQ53Z+y/1BjzG3BJxv0WAb8D8djPvgdY78D9lQoLGvSVCn3zMh6PA38CPwA3AO+JSOaUtiJyyhhzKfAIdhhdXexQul+Bj7Ed9M4gIieNMb2BycBM4ADwBnZ0wPR80tQG5zrwTQT6AOOAbdgOguOAIcDn2C8wlwFPYfsuHABWAv8SkWMOpUGpkGey/U9QSilHGGO+AtJFpJOH5w/ATpPbANglIqcz9tfB9kO4VURmFDFNBtvJ8HXgYhGpUZTrKRWKtKSvlHJERge6s4FuwAXAVYW4jGs64UItFlSAx4CxGc93++D6SgU9DfpKKadUA1YBB4FxIrLAi/cuxA4D9KXXsZ0ZwU1ThlLhTqv3lVJKqWJCJ+dRSimligkN+koppVQxoUFfKaWUKiY06CullFLFhAZ9pZRSqpjQoK+UUkoVE/8PkS5M+9HjFqMAAAAASUVORK5CYII=\n", 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" ] @@ -142,6 +136,10 @@ } ], "source": [ + "# Set scheduling method so circuit is scheduled for delay noise simulation\n", + "exp1.set_transpile_options(scheduling_method='asap')\n", + "\n", + "# Run experiment\n", "expdata1 = exp1.run(backend=backend, shots=2000)\n", "expdata1.block_for_results() # Wait for job/analysis to finish.\n", "\n", @@ -151,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -160,25 +158,25 @@ "text": [ "DbAnalysisResultV1\n", "- name: @Parameters_T2RamseyAnalysis\n", - "- value: [ 4.76195058e-01 5.01091305e-01 2.03265836e-05 1.00410831e+05\n", - " -2.31712461e-02] ± [6.23807790e-03 1.52343668e-03 4.41997916e-07 1.79229097e+02\n", - " 1.45903369e-02]\n", - "- χ²: 0.920854727025373\n", + "- value: [5.05908971e-01 5.00847669e-01 1.65841661e-05 9.97634544e+04\n", + " 6.92029373e-03] ± [8.74644192e-03 2.16094942e-03 4.91573818e-07 3.08683183e+02\n", + " 2.15207736e-02]\n", + "- χ²: 1.5297381584559218\n", "- quality: good\n", "- extra: <4 items>\n", "- device_components: ['Q0']\n", "- verified: False\n", "DbAnalysisResultV1\n", "- name: Frequency\n", - "- value: 100410.83059462237 ± 179.22909697719712 Hz\n", - "- χ²: 0.920854727025373\n", + "- value: 99763.45436329024 ± 308.6831829884889 Hz\n", + "- χ²: 1.5297381584559218\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n", "DbAnalysisResultV1\n", "- name: T2star\n", - "- value: 2.0326583583409876e-05 ± 4.419979162771111e-07 s\n", - "- χ²: 0.920854727025373\n", + "- value: 1.6584166057724038e-05 ± 4.915738180699149e-07 s\n", + "- χ²: 1.5297381584559218\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -201,12 +199,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -216,7 +214,6 @@ } ], "source": [ - "from qiskit_experiments.library.characterization import T2RamseyAnalysis\n", "user_p0={\n", " \"A\": 0.5,\n", " \"T2star\": 20e-6,\n", @@ -226,6 +223,7 @@ " }\n", "exp_with_p0 = T2Ramsey(qubit, delays, osc_freq=1e5)\n", "exp_with_p0.analysis.set_options(p0=user_p0)\n", + "exp_with_p0.set_transpile_options(scheduling_method='asap')\n", "expdata_with_p0 = exp_with_p0.run(backend=backend, shots=2000)\n", "expdata_with_p0.block_for_results()\n", "\n", @@ -235,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -244,25 +242,25 @@ "text": [ "DbAnalysisResultV1\n", "- name: @Parameters_T2RamseyAnalysis\n", - "- value: [ 4.75079813e-01 4.98234022e-01 2.05287241e-05 1.00208063e+05\n", - " -1.33480413e-02] ± [6.26841353e-03 1.52335745e-03 4.48734921e-07 1.77505428e+02\n", - " 1.45744909e-02]\n", - "- χ²: 1.0725034677926117\n", + "- value: [5.03897974e-01 5.01517511e-01 1.70919903e-05 9.98603996e+04\n", + " 3.22160893e-02] ± [8.79588577e-03 2.15932005e-03 5.05910614e-07 2.96475320e+02\n", + " 2.10774613e-02]\n", + "- χ²: 0.8052616586914084\n", "- quality: good\n", "- extra: <4 items>\n", "- device_components: ['Q0']\n", "- verified: False\n", "DbAnalysisResultV1\n", "- name: Frequency\n", - "- value: 100208.06296861691 ± 177.50542777064467 Hz\n", - "- χ²: 1.0725034677926117\n", + "- value: 99860.39957514184 ± 296.475320432314 Hz\n", + "- χ²: 0.8052616586914084\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n", "DbAnalysisResultV1\n", "- name: T2star\n", - "- value: 2.052872412506479e-05 ± 4.4873492119964766e-07 s\n", - "- χ²: 1.0725034677926117\n", + "- value: 1.709199032368464e-05 ± 5.059106139511856e-07 s\n", + "- χ²: 0.8052616586914084\n", "- quality: good\n", "- device_components: ['Q0']\n", "- verified: False\n" @@ -277,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -322,7 +320,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.5" + "version": "3.7.5" } }, "nbformat": 4, diff --git a/qiskit_experiments/VERSION.txt b/qiskit_experiments/VERSION.txt index 0ea3a944b3..0d91a54c7d 100644 --- a/qiskit_experiments/VERSION.txt +++ b/qiskit_experiments/VERSION.txt @@ -1 +1 @@ -0.2.0 +0.3.0 diff --git a/qiskit_experiments/calibration_management/__init__.py b/qiskit_experiments/calibration_management/__init__.py index 8513a71a84..b715f8469d 100644 --- a/qiskit_experiments/calibration_management/__init__.py +++ b/qiskit_experiments/calibration_management/__init__.py @@ -37,6 +37,11 @@ :toctree: ../stubs/ Calibrations + ParameterValue + FixedFrequencyTransmon + BasisGateLibrary + BaseCalibrationExperiment + Managing Calibration Data ========================= @@ -143,5 +148,6 @@ """ from .calibrations import Calibrations +from .parameter_value import ParameterValue from .base_calibration_experiment import BaseCalibrationExperiment -from .basis_gate_library import FixedFrequencyTransmon +from .basis_gate_library import FixedFrequencyTransmon, BasisGateLibrary diff --git a/qiskit_experiments/curve_analysis/curve_analysis.py b/qiskit_experiments/curve_analysis/curve_analysis.py index ed9b5e26ba..aec13b3dc0 100644 --- a/qiskit_experiments/curve_analysis/curve_analysis.py +++ b/qiskit_experiments/curve_analysis/curve_analysis.py @@ -250,14 +250,6 @@ def __init__(self): #: Backend: backend object used for experimentation self.__backend = None - # Add expected options to instance variable so that every method can access to. - for key in self._default_options().__dict__: - setattr(self, f"__{key}", None) - - # Add fixed parameters to instance variable so that every method can access to. - for key in self.__fixed_parameters__: - setattr(self, f"__{key}", None) - @classmethod def _fit_params(cls) -> List[str]: """Return a list of fitting parameters. @@ -346,6 +338,8 @@ def _default_options(cls) -> Options: that contains a set of configurations to create a fit plot. extra (Dict[str, Any]): A dictionary that is appended to all database entries as extra information. + curve_fitter_options (Dict[str, Any]) Options that are passed to the + specified curve fitting function. """ options = super()._default_options() @@ -366,6 +360,7 @@ def _default_options(cls) -> Options: options.curve_plotter = "mpl_single_canvas" options.style = PlotterStyle() options.extra = dict() + options.curve_fitter_options = dict() # automatically populate initial guess and boundary fit_params = cls._fit_params() @@ -577,7 +572,7 @@ def _is_target_series(datum, **filters): # Extract X, Y, Y_sigma data data = experiment_data.data() - x_key = self._get_option("x_key") + x_key = self.options.x_key try: x_values = np.asarray([datum["metadata"][x_key] for datum in data], dtype=float) except KeyError as ex: @@ -675,21 +670,6 @@ def _experiment_options(self, index: int = -1) -> Dict[str, Any]: # Ignore experiment metadata or job metadata is not set or key is not found return None - def _analysis_options(self, index: int = -1) -> Dict[str, Any]: - """Returns the analysis options of given job index. - - Args: - index: Index of job metadata to extract. Default to -1 (latest). - - Returns: - Analysis options. This option is used for analysis. - """ - try: - return self.__experiment_metadata["job_metadata"][index]["analysis_options"] - except (TypeError, KeyError, IndexError): - # Ignore experiment metadata or job metadata is not set or key is not found - return None - def _run_options(self, index: int = -1) -> Dict[str, Any]: """Returns the run options of given job index. @@ -772,67 +752,17 @@ def _data( raise AnalysisError(f"Specified series {series_name} is not defined in this analysis.") - def _arg_parse(self, **options) -> Dict[str, Any]: - """Parse input kwargs with predicted input. - - Class attributes will be updated according to the ``options``. - For example, if ``options`` has a key ``p0``, and the class - has an attribute named ``__p0``, then the attribute ``__0p`` - will be updated to ``options["p0"]``. - - Options that don't have matching attributes will be included - in the returned dictionary. - - Args: - options: User-input keyword argument options. - - Returns: - Keyword arguments not specified in the default options - of the class. - """ - extra_options = dict() - for key, value in options.items(): - private_key = f"__{key}" - if hasattr(self, private_key): - setattr(self, private_key, value) - else: - extra_options[key] = value - - return extra_options - - def _get_option(self, arg_name: str) -> Any: - """A helper function to get specified field from the input analysis options. - - Args: - arg_name: Name of option. - - Return: - Arbitrary object specified by the option name. - - Raises: - AnalysisError: - - When `arg_name` is not found in the analysis options. - """ - try: - return getattr(self, f"__{arg_name}") - except AttributeError as ex: - raise AnalysisError( - f"The argument {arg_name} is selected but not defined. " - "This key-value pair should be defined in the analysis option." - ) from ex - def _run_analysis( self, experiment_data: ExperimentData ) -> Tuple[List[AnalysisResultData], List["pyplot.Figure"]]: # # 1. Parse arguments # - extra_options = self._arg_parse(**self.options.__dict__) # Update all fit functions in the series definitions if fixed parameter is defined. # Fixed parameters should be provided by the analysis options. if self.__fixed_parameters__: - assigned_params = {k: self._get_option(k) for k in self.__fixed_parameters__} + assigned_params = {k: self.options.get(k, None) for k in self.__fixed_parameters__} # Check if all parameters are assigned. if any(v is None for v in assigned_params.values()): @@ -870,7 +800,7 @@ def _run_analysis( # No data processor has been provided at run-time we infer one from the job # metadata and default to the data processor for averaged classified data. - data_processor = self._get_option("data_processor") + data_processor = self.options.data_processor if not data_processor: run_options = self._run_options() or dict() @@ -883,9 +813,8 @@ def _run_analysis( ) from ex meas_return = run_options.get("meas_return", None) - normalization = self._get_option("normalization") - data_processor = get_processor(meas_level, meas_return, normalization) + data_processor = get_processor(meas_level, meas_return, self.options.normalization) if isinstance(data_processor, DataProcessor) and not data_processor.is_trained: # Qiskit DataProcessor instance. May need calibration. @@ -899,15 +828,14 @@ def _run_analysis( # # 4. Run fitting # - curve_fitter = self._get_option("curve_fitter") formatted_data = self._data(label="fit_ready") # Generate algorithmic initial guesses and boundaries default_fit_opt = FitOptions( parameters=self._fit_params(), - default_p0=self._get_option("p0"), - default_bounds=self._get_option("bounds"), - **extra_options, + default_p0=self.options.p0, + default_bounds=self.options.bounds, + **self.options.curve_fitter_options, ) fit_options = self._generate_fit_guesses(default_fit_opt) @@ -918,7 +846,7 @@ def _run_analysis( fit_results = [] for fit_opt in set(fit_options): try: - fit_result = curve_fitter( + fit_result = self.options.curve_fitter( funcs=[series_def.fit_func for series_def in self.__series__], series=formatted_data.data_index, xdata=formatted_data.x, @@ -969,13 +897,13 @@ def _run_analysis( "dof": fit_result.dof, "covariance_mat": fit_result.pcov, "fit_models": fit_models, - **self._get_option("extra"), + **self.options.extra, }, ) ) # output special parameters - result_parameters = self._get_option("result_parameters") + result_parameters = self.options.result_parameters if result_parameters: for param_repr in result_parameters: if isinstance(param_repr, ParameterRepr): @@ -991,14 +919,14 @@ def _run_analysis( value=fit_result.fitval(p_name, unit), chisq=fit_result.reduced_chisq, quality=quality, - extra=self._get_option("extra"), + extra=self.options.extra, ) analysis_results.append(result_entry) # add extra database entries analysis_results.extend(self._extra_database_entry(fit_result)) - if self._get_option("return_data_points"): + if self.options.return_data_points: # save raw data points in the data base if option is set (default to false) raw_data_dict = dict() for series_def in self.__series__: @@ -1012,8 +940,8 @@ def _run_analysis( name=DATA_ENTRY_PREFIX + self.__class__.__name__, value=raw_data_dict, extra={ - "x-unit": self._get_option("xval_unit"), - "y-unit": self._get_option("yval_unit"), + "x-unit": self.options.xval_unit, + "y-unit": self.options.yval_unit, }, ) analysis_results.append(raw_data_entry) @@ -1021,23 +949,23 @@ def _run_analysis( # # 6. Create figures # - if self._get_option("plot"): - fit_figure = FitResultPlotters[self._get_option("curve_plotter")].value.draw( + if self.options.plot: + fit_figure = FitResultPlotters[self.options.curve_plotter].value.draw( series_defs=self.__series__, raw_samples=[self._data(ser.name, "raw_data") for ser in self.__series__], fit_samples=[self._data(ser.name, "fit_ready") for ser in self.__series__], tick_labels={ - "xval_unit": self._get_option("xval_unit"), - "yval_unit": self._get_option("yval_unit"), - "xlabel": self._get_option("xlabel"), - "ylabel": self._get_option("ylabel"), - "xlim": self._get_option("xlim"), - "ylim": self._get_option("ylim"), + "xval_unit": self.options.xval_unit, + "yval_unit": self.options.yval_unit, + "xlabel": self.options.xlabel, + "ylabel": self.options.ylabel, + "xlim": self.options.xlim, + "ylim": self.options.ylim, }, fit_data=fit_result, result_entries=analysis_results, - style=self._get_option("style"), - axis=self._get_option("axis"), + style=self.options.style, + axis=self.options.axis, ) figures = [fit_figure] else: diff --git a/qiskit_experiments/curve_analysis/standard_analysis/error_amplification_analysis.py b/qiskit_experiments/curve_analysis/standard_analysis/error_amplification_analysis.py index 19b9ef8634..df15a85ef9 100644 --- a/qiskit_experiments/curve_analysis/standard_analysis/error_amplification_analysis.py +++ b/qiskit_experiments/curve_analysis/standard_analysis/error_amplification_analysis.py @@ -149,14 +149,14 @@ def _generate_fit_guesses( user_opt.bounds.set_if_empty(amp=(0, 2 * max_abs_y)) # Base the initial guess on the intended angle_per_gate and phase offset. - apg = self._get_option("angle_per_gate") - phi = self._get_option("phase_offset") + apg = self.options.angle_per_gate + phi = self.options.phase_offset # Prepare logical guess for specific condition (often satisfied) d_theta_guesses = [] offsets = apg * curve_data.x + phi - amp = user_opt.p0.get("amp", self._get_option("amp")) + amp = user_opt.p0.get("amp", self.options.amp) for i in range(curve_data.x.size): xi = curve_data.x[i] yi = curve_data.y[i] @@ -192,11 +192,10 @@ def _evaluate_quality(self, fit_data: curve.FitData) -> Union[str, None]: This quantity is set in the analysis options. """ fit_d_theta = fit_data.fitval("d_theta").value - max_good_angle_error = self._get_option("max_good_angle_error") criteria = [ fit_data.reduced_chisq < 3, - abs(fit_d_theta) < abs(max_good_angle_error), + abs(fit_d_theta) < abs(self.options.max_good_angle_error), ] if all(criteria): diff --git a/qiskit_experiments/curve_analysis/visualization/curves.py b/qiskit_experiments/curve_analysis/visualization/curves.py index 403d61fb3f..4c01da31fd 100644 --- a/qiskit_experiments/curve_analysis/visualization/curves.py +++ b/qiskit_experiments/curve_analysis/visualization/curves.py @@ -62,17 +62,10 @@ def plot_curve_fit( plot_opts["linewidth"] = 2 # Result data - if isinstance(result, dict): - # TODO: remove this after T1 T2 migration to curve analysis - fit_params = result["popt"] - param_keys = result["popt_keys"] - fit_errors = result["popt_err"] - xmin, xmax = result["x_range"] - else: - fit_params = result.popt - param_keys = result.popt_keys - fit_errors = result.popt_err - xmin, xmax = result.x_range + fit_params = result.popt + param_keys = result.popt_keys + fit_errors = result.popt_err + xmin, xmax = result.x_range # Plot fit data xs = np.linspace(xmin, xmax, num_fit_points) diff --git a/qiskit_experiments/curve_analysis/visualization/fit_result_plotters.py b/qiskit_experiments/curve_analysis/visualization/fit_result_plotters.py index db1eed90ec..aca8281daf 100644 --- a/qiskit_experiments/curve_analysis/visualization/fit_result_plotters.py +++ b/qiskit_experiments/curve_analysis/visualization/fit_result_plotters.py @@ -366,7 +366,7 @@ def format_val(float_val: float) -> str: value_repr = f"{val: .3g}" # write error bar if it is finite value - if fitval.stderr is not None and not np.isinf(fitval.stderr): + if fitval.stderr is not None and np.isfinite(fitval.stderr): # with stderr err, err_prefix = detach_prefix(fitval.stderr, decimal=3) err_unit = err_prefix + fitval.unit diff --git a/qiskit_experiments/framework/base_experiment.py b/qiskit_experiments/framework/base_experiment.py index 7653690b30..6298a59ee0 100644 --- a/qiskit_experiments/framework/base_experiment.py +++ b/qiskit_experiments/framework/base_experiment.py @@ -38,8 +38,8 @@ class BaseExperiment(ABC, StoreInitArgs): def __init__( self, qubits: Sequence[int], - analysis: Optional[BaseAnalysis] = None, backend: Optional[Backend] = None, + analysis: Optional[BaseAnalysis] = None, experiment_type: Optional[str] = None, ): """Initialize the experiment object. @@ -47,6 +47,7 @@ def __init__( Args: qubits: list of physical qubits for the experiment. backend: Optional, the backend to run the experiment on. + analysis: Optional, the analysis class instance that performs default post processing. experiment_type: Optional, the experiment type string. Raises: diff --git a/qiskit_experiments/framework/composite/batch_experiment.py b/qiskit_experiments/framework/composite/batch_experiment.py index 718c630ec2..2415c6fb74 100644 --- a/qiskit_experiments/framework/composite/batch_experiment.py +++ b/qiskit_experiments/framework/composite/batch_experiment.py @@ -13,12 +13,13 @@ Batch Experiment class. """ -from typing import List, Optional from collections import OrderedDict +from typing import List, Optional from qiskit import QuantumCircuit from qiskit.providers.backend import Backend from .composite_experiment import CompositeExperiment, BaseExperiment +from .composite_analysis import CompositeAnalysis class BatchExperiment(CompositeExperiment): @@ -40,12 +41,18 @@ class BatchExperiment(CompositeExperiment): documentation for additional information. """ - def __init__(self, experiments: List[BaseExperiment], backend: Optional[Backend] = None): + def __init__( + self, + experiments: List[BaseExperiment], + backend: Optional[Backend] = None, + analysis: Optional[CompositeAnalysis] = None, + ): """Initialize a batch experiment. Args: experiments: a list of experiments. backend: Optional, the backend to run the experiment on. + analysis: Optional, the analysis class instance that performs default post processing. """ # Generate qubit map @@ -57,7 +64,7 @@ def __init__(self, experiments: List[BaseExperiment], backend: Optional[Backend] self._qubit_map[physical_qubit] = logical_qubit logical_qubit += 1 qubits = tuple(self._qubit_map.keys()) - super().__init__(experiments, qubits, backend=backend) + super().__init__(experiments, qubits, backend=backend, analysis=analysis) def circuits(self): diff --git a/qiskit_experiments/framework/composite/composite_analysis.py b/qiskit_experiments/framework/composite/composite_analysis.py index ddf6671cb8..967573482e 100644 --- a/qiskit_experiments/framework/composite/composite_analysis.py +++ b/qiskit_experiments/framework/composite/composite_analysis.py @@ -16,8 +16,7 @@ from typing import List, Dict import numpy as np from qiskit.result import marginal_counts -from qiskit_experiments.framework import BaseAnalysis, ExperimentData, AnalysisResultData -from qiskit_experiments.database_service.device_component import Qubit +from qiskit_experiments.framework import BaseAnalysis, ExperimentData class CompositeAnalysis(BaseAnalysis): @@ -69,7 +68,6 @@ def _run_analysis(self, experiment_data: ExperimentData): # child data is handled by the `replace_results` kwarg of the # parent container it is safe to always clear and replace the # results of child containers in this step - analysis_results = [] for i, (sub_data, sub_exp) in enumerate(zip(marginalized_data, component_exps)): sub_exp_data = experiment_data.child_data(component_ids[i]) @@ -85,26 +83,12 @@ def _run_analysis(self, experiment_data: ExperimentData): # we always run with replace result on component analysis sub_exp.analysis.run(sub_exp_data, replace_results=True) - # Record the component experiment id and type as an analysis result - # for evidence analysis has started and to display in the service DB - result = AnalysisResultData( - name=sub_exp_data.experiment_type, - value=sub_exp_data.experiment_id, - device_components=[ - Qubit(qubit) for qubit in sub_exp_data.metadata.get("physical_qubits", []) - ], - ) - analysis_results.append(result) - - # Add callback to wait for all component analysis to finish before returning + # Wait for all component analysis to finish before returning # the parent experiment analysis results - def _wait_for_components(experiment_data, component_ids): - for comp_id in component_ids: - experiment_data.child_data(comp_id).block_for_results() + for comp_id in component_ids: + experiment_data.child_data(comp_id).block_for_results() - experiment_data.add_analysis_callback(_wait_for_components, component_ids=component_ids) - - return analysis_results, [] + return [], [] def _initialize_components(self, experiment, experiment_data): """Initialize child data components and return list of child experiment IDs""" diff --git a/qiskit_experiments/framework/composite/composite_experiment.py b/qiskit_experiments/framework/composite/composite_experiment.py index 34325eda4d..7dc0c9adc9 100644 --- a/qiskit_experiments/framework/composite/composite_experiment.py +++ b/qiskit_experiments/framework/composite/composite_experiment.py @@ -29,6 +29,7 @@ def __init__( experiments: List[BaseExperiment], qubits: Sequence[int], backend: Optional[Backend] = None, + analysis: Optional[CompositeAnalysis] = None, experiment_type: Optional[str] = None, ): """Initialize the composite experiment object. @@ -37,14 +38,15 @@ def __init__( experiments: a list of experiment objects. qubits: list of physical qubits for the experiment. backend: Optional, the backend to run the experiment on. + analysis: Optional, the analysis class instance that performs default post processing. experiment_type: Optional, composite experiment subclass name. """ self._experiments = experiments self._num_experiments = len(experiments) super().__init__( qubits, - analysis=CompositeAnalysis(), backend=backend, + analysis=analysis or CompositeAnalysis(), experiment_type=experiment_type, ) diff --git a/qiskit_experiments/framework/composite/parallel_experiment.py b/qiskit_experiments/framework/composite/parallel_experiment.py index d1ad92abd1..f6bda4fb65 100644 --- a/qiskit_experiments/framework/composite/parallel_experiment.py +++ b/qiskit_experiments/framework/composite/parallel_experiment.py @@ -17,6 +17,7 @@ from qiskit import QuantumCircuit, ClassicalRegister from qiskit.providers.backend import Backend from .composite_experiment import CompositeExperiment, BaseExperiment +from .composite_analysis import CompositeAnalysis class ParallelExperiment(CompositeExperiment): @@ -38,17 +39,23 @@ class ParallelExperiment(CompositeExperiment): documentation for additional information. """ - def __init__(self, experiments: List[BaseExperiment], backend: Optional[Backend] = None): + def __init__( + self, + experiments: List[BaseExperiment], + backend: Optional[Backend] = None, + analysis: Optional[CompositeAnalysis] = None, + ): """Initialize the analysis object. Args: experiments: a list of experiments. backend: Optional, the backend to run the experiment on. + analysis: Optional, the analysis class instance that performs default post processing. """ qubits = [] for exp in experiments: qubits += exp.physical_qubits - super().__init__(experiments, qubits, backend=backend) + super().__init__(experiments, qubits, backend=backend, analysis=analysis) def circuits(self): diff --git a/qiskit_experiments/library/__init__.py b/qiskit_experiments/library/__init__.py index 9dd3e5ac17..aec486a81f 100644 --- a/qiskit_experiments/library/__init__.py +++ b/qiskit_experiments/library/__init__.py @@ -71,6 +71,10 @@ ~characterization.RamseyXY ~characterization.FineFrequency ~characterization.ReadoutAngle + ~hamiltonian.ZXHeat + ~hamiltonian.ZX90HeatXError + ~hamiltonian.ZX90HeatYError + ~hamiltonian.ZX90HeatZError .. _calibration: @@ -142,6 +146,7 @@ class instance to manage parameters and pulse schedules. from .randomized_benchmarking import StandardRB, InterleavedRB from .tomography import StateTomography, ProcessTomography from .quantum_volume import QuantumVolume +from .mitigation import ReadoutMitigationExperiment # Experiment Sub-modules from . import calibration @@ -149,3 +154,4 @@ class instance to manage parameters and pulse schedules. from . import randomized_benchmarking from . import tomography from . import quantum_volume +from . import hamiltonian diff --git a/qiskit_experiments/library/characterization/analysis/__init__.py b/qiskit_experiments/library/characterization/analysis/__init__.py index e2acdf3241..e100871223 100644 --- a/qiskit_experiments/library/characterization/analysis/__init__.py +++ b/qiskit_experiments/library/characterization/analysis/__init__.py @@ -17,7 +17,7 @@ from .fine_amplitude_analysis import FineAmplitudeAnalysis from .fine_drag_analysis import FineDragAnalysis from .fine_frequency_analysis import FineFrequencyAnalysis -from .remsey_xy_analysis import RamseyXYAnalysis +from .ramsey_xy_analysis import RamseyXYAnalysis from .t2ramsey_analysis import T2RamseyAnalysis from .t1_analysis import T1Analysis from .cr_hamiltonian_analysis import CrossResonanceHamiltonianAnalysis diff --git a/qiskit_experiments/library/characterization/analysis/remsey_xy_analysis.py b/qiskit_experiments/library/characterization/analysis/ramsey_xy_analysis.py similarity index 100% rename from qiskit_experiments/library/characterization/analysis/remsey_xy_analysis.py rename to qiskit_experiments/library/characterization/analysis/ramsey_xy_analysis.py diff --git a/qiskit_experiments/library/characterization/analysis/readout_angle_analysis.py b/qiskit_experiments/library/characterization/analysis/readout_angle_analysis.py index 503d9e8529..d2c674296d 100644 --- a/qiskit_experiments/library/characterization/analysis/readout_angle_analysis.py +++ b/qiskit_experiments/library/characterization/analysis/readout_angle_analysis.py @@ -13,9 +13,11 @@ Readout Angle Analysis class. """ +from typing import List, Optional import numpy as np -from qiskit_experiments.framework import BaseAnalysis, AnalysisResultData +from qiskit_experiments.framework import BaseAnalysis, AnalysisResultData, Options +from qiskit_experiments.framework.matplotlib import get_non_gui_ax class ReadoutAngleAnalysis(BaseAnalysis): @@ -23,22 +25,73 @@ class ReadoutAngleAnalysis(BaseAnalysis): A class to analyze readout angle experiments """ + @classmethod + def _default_options(cls) -> Options: + """Return default analysis options. + + Analysis Options: + plot (bool): Set ``True`` to create figure for fit result. + ax(AxesSubplot): Optional. A matplotlib axis object to draw. + """ + options = super()._default_options() + options.plot = True + options.ax = None + return options + def _run_analysis(self, experiment_data): angles = [] + radii = [] + centers = [] for i in range(2): center = complex(*experiment_data.data(i)["memory"][0]) angles.append(np.angle(center)) + radii.append(np.absolute(center)) + centers.append(center) angle = (angles[0] + angles[1]) / 2 if (np.abs(angles[0] - angles[1])) % (2 * np.pi) > np.pi: angle += np.pi + extra_results = {} + extra_results["readout_angle_0"] = angles[0] + extra_results["readout_angle_1"] = angles[1] + extra_results["readout_radius_0"] = radii[0] + extra_results["readout_radius_1"] = radii[1] + analysis_results = [ - AnalysisResultData( - name="ReadoutAngle", - value=angle, - extra={"angle_ground": angles[0], "angle_excited": angles[1]}, - ) + AnalysisResultData(name="readout_angle", value=angle, extra=extra_results) ] - return analysis_results, [] + if self.options.plot: + ax = self._format_plot(centers, ax=self.options.ax) + figures = [ax.get_figure()] + else: + figures = None + + return analysis_results, figures + + @staticmethod + def _format_plot(centers: List[complex], ax: Optional["matplotlib.pyplot.AxesSubplot"] = None): + """Format the readout_angle plot + + Args: + centers: the two centers of the level 1 measurements for 0 and for 1. + ax: matplotlib axis to add plot to. + + Returns: + AxesSubPlot: the matplotlib axes containing the plot. + """ + largest_extent = ( + np.max([np.max(np.abs(np.real(centers))), np.max(np.abs(np.imag(centers)))]) * 1.1 + ) + + ax = get_non_gui_ax() + ax.plot(np.real(centers[0]), np.imag(centers[0]), "ro", markersize=24) + ax.plot(np.real(centers[1]), np.imag(centers[1]), "bo", markersize=24) + ax.set_xlim([-largest_extent, largest_extent]) + ax.set_ylim([-largest_extent, largest_extent]) + ax.set_xlabel("I [arb.]") + ax.set_ylabel("Q [arb.]") + ax.set_title("Centroid Positions") + ax.legend(["0", "1"]) + return ax diff --git a/qiskit_experiments/library/characterization/fine_drag.py b/qiskit_experiments/library/characterization/fine_drag.py index 97da5d9d8c..fa95a4ca41 100644 --- a/qiskit_experiments/library/characterization/fine_drag.py +++ b/qiskit_experiments/library/characterization/fine_drag.py @@ -10,7 +10,7 @@ # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. -"""Fine DRAG calibration experiment.""" +"""Fine DRAG characterization experiment.""" from typing import List, Optional import numpy as np @@ -30,11 +30,11 @@ class FineDrag(BaseExperiment): # section: overview - The class :class:`FineDrag` runs fine DRAG calibration experiments (see :class:`DragCal` - for the definition of DRAG pulses). Fine DRAG calibration proceeds by iterating the - gate sequence Rp - Rm where Rp is a rotation around an axis and Rm is the same rotation - but in the opposite direction and is implemented by the gates Rz - Rp - Rz where the Rz - gates are virtual Z-rotations, see Ref. [1]. The executed circuits are of the form + :class:`FineDrag` runs fine DRAG characterization experiments (see :class:`DragCal` + for the definition of DRAG pulses). Fine DRAG proceeds by iterating the gate sequence + Rp - Rm where Rp is a rotation around an axis and Rm is the same rotation but in the + opposite direction and is implemented by the gates Rz - Rp - Rz where the Rz gates + are virtual Z-rotations, see Ref. [1]. The executed circuits are of the form .. parsed-literal:: @@ -217,10 +217,10 @@ def circuits(self) -> List[QuantumCircuit]: class FineXDrag(FineDrag): - """Class to fine calibrate the DRAG parameter of an X gate. + """Class to fine characterize the DRAG parameter of an X gate. # section: see_also - qiskit_experiments.library.calibration.fine_drag.FineDrag + qiskit_experiments.library.characterization.fine_drag.FineDrag """ def __init__(self, qubit: int, backend: Optional[Backend] = None): @@ -246,10 +246,10 @@ def _pre_circuit() -> QuantumCircuit: class FineSXDrag(FineDrag): - """Class to fine calibrate the DRAG parameter of an SX gate. + """Class to fine characterize the DRAG parameter of an SX gate. # section: see_also - qiskit_experiments.library.calibration.fine_drag.FineDrag + qiskit_experiments.library.characterization.fine_drag.FineDrag """ def __init__(self, qubit: int, backend: Optional[Backend] = None): diff --git a/qiskit_experiments/library/characterization/ramsey_xy.py b/qiskit_experiments/library/characterization/ramsey_xy.py index a8a0e588de..22cea3444e 100644 --- a/qiskit_experiments/library/characterization/ramsey_xy.py +++ b/qiskit_experiments/library/characterization/ramsey_xy.py @@ -30,8 +30,8 @@ class RamseyXY(BaseExperiment): # section: overview This experiment differs from the :class:`~qiskit_experiments.characterization.\ - t2ramsey.T2Ramsey` since it is sensitive to the sign of frequency offset from the main - transition. This experiment consists of following two circuits: + t2ramsey.T2Ramsey` since it is sensitive to the sign of the frequency offset from + the main transition. This experiment consists of following two circuits: .. parsed-literal:: diff --git a/qiskit_experiments/library/hamiltonian/__init__.py b/qiskit_experiments/library/hamiltonian/__init__.py new file mode 100644 index 0000000000..afb001138e --- /dev/null +++ b/qiskit_experiments/library/hamiltonian/__init__.py @@ -0,0 +1,64 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. +""" +=============================================================================================== +Hamiltonian Tomography Experiments (:mod:`qiskit_experiments.library.hamiltonian`) +=============================================================================================== + +.. currentmodule:: qiskit_experiments.library.hamiltonian + +This module provides a set of experiments to characterize Hamiltonian level +properties of qubits or control sequences. + +HEAT Experiments +================ + +HEAT stands for `Hamiltonian Error Amplifying Tomography` which amplifies the +dynamics of entangler along the interrogated axis on the target qubit with +the conventional error amplification (ping-pong) technique. + +These are the base experiment classes for developer to write own experiments. + +.. autosummary:: + :toctree: ../stubs/ + :template: autosummary/experiment.rst + + HeatElement + BatchHeatHelper + +HEAT for ZX Hamiltonian +----------------------- + +.. autosummary:: + :toctree: ../stubs/ + :template: autosummary/experiment.rst + + ZXHeat + ZX90HeatXError + ZX90HeatYError + ZX90HeatZError + +HEAT Analysis +------------- + +.. autosummary:: + :toctree: ../stubs/ + :template: autosummary/analysis.rst + + HeatElementAnalysis + HeatAnalysis + +""" + +from .heat_base import HeatElement, BatchHeatHelper +from .heat_zx import ZXHeat, ZX90HeatXError, ZX90HeatYError, ZX90HeatZError +from .heat_analysis import HeatElementAnalysis, HeatAnalysis diff --git a/qiskit_experiments/library/hamiltonian/heat_analysis.py b/qiskit_experiments/library/hamiltonian/heat_analysis.py new file mode 100644 index 0000000000..74c34dc996 --- /dev/null +++ b/qiskit_experiments/library/hamiltonian/heat_analysis.py @@ -0,0 +1,144 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. +""" +Analysis for HEAT experiments. +""" + +from typing import List + +import numpy as np + +from qiskit_experiments.curve_analysis import ErrorAmplificationAnalysis +from qiskit_experiments.exceptions import AnalysisError +from qiskit_experiments.framework import ( + CompositeAnalysis, + ExperimentData, + AnalysisResultData, + Options, + FitVal, +) + + +class HeatElementAnalysis(ErrorAmplificationAnalysis): + """An analysis class for HEAT experiment to define the fixed parameters. + + # section: overview + + This is standard error amplification analysis. + + # section: see_also + qiskit_experiments.curve_analysis.ErrorAmplificationAnalysis + """ + + __fixed_parameters__ = ["angle_per_gate", "phase_offset", "amp"] + + @classmethod + def _default_options(cls) -> Options: + """Default analysis options.""" + options = super()._default_options() + options.angle_per_gate = np.pi + options.phase_offset = np.pi / 2 + options.amp = 1.0 + + return options + + +class HeatAnalysis(CompositeAnalysis): + r"""A composite error amplification analysis to get unitary error coefficients. + + # section: fit_model + + This analysis takes two error amplification experiment results performed with + different control qubit state to distinguish the local rotation term from + the controlled rotation term amplified along a specific error axis. + + This analysis takes a set of `d_theta` parameters from child error amplification results + which might be represented by a unique name in the child experiment data. + With these fit parameters, two Hamiltonian coefficients will be computed as + + .. math:: + + A_{I\beta} = \frac{{d\theta_{\beta 0}} + d\theta_{\beta 1}}}{2} + + A_{Z\beta} = \frac{{d\theta_{\beta 0}} - d\theta_{\beta 1}}}{2} + + where, :math:`\beta \in [X, Y, Z]` is one of single qubit Pauli term, + :math:`d\theta_{\beta k}` is `d_theta` parameter extracted from the HEAT experiment + with the control qubit state :math:`|k\rangle \in [|0\rangle, |1\rangle]`. + + # section: see_also + HeatElementAnalysis + + """ + + def __init__(self, fit_params: List[str], out_params: List[str]): + """Create new HEAT analysis. + + Args: + fit_params: Name of error parameters for each amplification sequence. + out_params: Name of Hamiltonian coefficients. + """ + super(HeatAnalysis, self).__init__() + + if len(fit_params) != 2: + raise AnalysisError( + f"{self.__class__.__name__} assumes two fit parameters extracted from " + "a set of experiments with different control qubit state input. " + f"{len(fit_params)} input parameter names are specified." + ) + self.fit_params = fit_params + + if len(out_params) != 2: + raise AnalysisError( + f"{self.__class__.__name__} assumes two output parameters computed with " + "a set of experiment results with different control qubit state input. " + f"{len(out_params)} output parameter names are specified." + ) + self.out_params = out_params + + def _run_analysis(self, experiment_data: ExperimentData): + + # wait for child experiments to complete + super()._run_analysis(experiment_data) + + # extract d_theta parameters + fit_results = [] + for i, pname in enumerate(self.fit_params): + fit_results.append( + experiment_data.child_data(i).analysis_results(pname) + ) + + # Check data quality + is_good_quality = all(r.quality == "good" for r in fit_results) + + # Compute unitary terms + ib = (fit_results[0].value.value + fit_results[1].value.value) / 2 + zb = (fit_results[0].value.value - fit_results[1].value.value) / 2 + + # Compute new variance + sigma = np.sqrt( + fit_results[0].value.stderr ** 2 + fit_results[1].value.stderr ** 2 + ) + + estimate_ib = AnalysisResultData( + name=self.out_params[0], + value=FitVal(value=ib, stderr=sigma, unit="rad"), + quality="good" if is_good_quality else "bad", + ) + + estimate_zb = AnalysisResultData( + name=self.out_params[1], + value=FitVal(value=zb, stderr=sigma, unit="rad"), + quality="good" if is_good_quality else "bad", + ) + + return [estimate_ib, estimate_zb], None diff --git a/qiskit_experiments/library/hamiltonian/heat_base.py b/qiskit_experiments/library/hamiltonian/heat_base.py new file mode 100644 index 0000000000..f81486da22 --- /dev/null +++ b/qiskit_experiments/library/hamiltonian/heat_base.py @@ -0,0 +1,233 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. +""" +Base Class for general Hamiltonian Error Amplifying Tomography experiments. +""" + +from abc import ABC +from typing import List, Tuple, Optional + +from qiskit import circuit, QuantumCircuit +from qiskit.providers import Backend + +from qiskit_experiments.framework import BaseExperiment, BatchExperiment, Options +from .heat_analysis import HeatElementAnalysis, HeatAnalysis + + +class HeatElement(BaseExperiment): + """Base class of HEAT experiment elements. + + # section: overview + + Hamiltonian error amplifying tomography (HEAT) is designed to amplify + the dynamics of entangler circuit on the target qubit along a specific axis. + + The basic form of HEAT circuit is represented as follows. + + .. parsed-literal:: + + (xN) + ┌───────┐ ░ ┌───────┐┌───────┐ ░ ┌───────┐ + q_0: ┤0 ├─░─┤0 ├┤0 ├─░─┤0 ├─── + │ prep │ ░ │ heat ││ echo │ ░ │ meas │┌─┐ + q_1: ┤1 ├─░─┤1 ├┤1 ├─░─┤1 ├┤M├ + └───────┘ ░ └───────┘└───────┘ ░ └───────┘└╥┘ + c: 1/═══════════════════════════════════════════╩═ + 0 + + The circuit in middle is repeated by ``N`` times to amplify the Hamiltonian + coefficients along interrogated axis on the target qubit. The ``prep`` circuit + is carefully chosen based on the generator of ``heat`` gate to investigate, + and the ``echo`` and ``meas`` circuit depend on the axis of error to amplify. + Only target qubit is measured following to the projection by ``meas`` circuit. + + The amplified response may consist of the contribution of from the local and + controlled rotation terms. Thus, usually multiple error amplification experiments + with different control qubit states are combined to distinguish the terms in the analysis. + + The ``heat`` gate is a special gate kind to represent + the entangler pulse sequence of interest, thus one must provide the definition of it + through the backend or custom transpiler configuration, i.e. instruction schedule map. + This gate name can be overridden via the experiment option of this experiment. + + # section: note + + This class is usually not exposed to end users. + Developer of new HEAT experiment must design amplification sequence and + instantiate the class implicitly in the batch experiment. + The :class:`BatchHeatHelper` provides a convenient wrapper class of + the :class:`qiskit_experiments.framework.BatchExperiment` for implementing a + typical HEAT experiment. + + # section: analysis_ref + :py:class:`HeatElementAnalysis` + + # section: reference + .. ref_arxiv:: 1 2007.02925 + """ + + def __init__( + self, + qubits: Tuple[int, int], + prep_circ: QuantumCircuit, + echo_circ: QuantumCircuit, + meas_circ: QuantumCircuit, + backend: Optional[Backend] = None, + **kwargs + ): + """Create new HEAT sub experiment. + + Args: + qubits: Index of control and target qubit, respectively. + prep_circ: A circuit to prepare qubit before the echo sequence. + echo_circ: A circuit to selectively amplify the specific error term. + meas_circ: A circuit to project target qubit onto the basis of interest. + backend: Optional, the backend to run the experiment on. + + Keyword Args: + See :meth:`experiment_options` for details. + """ + super().__init__(qubits=qubits, backend=backend, analysis=HeatElementAnalysis()) + self.set_experiment_options(**kwargs) + + # These are not user configurable options. Be frozen once assigned. + self._prep_circuit = prep_circ + self._echo_circuit = echo_circ + self._meas_circuit = meas_circ + + @classmethod + def _default_experiment_options(cls) -> Options: + """Default experiment options. + + Experiment Options: + repetitions (Sequence[int]): A list of the number of echo repetitions. + cr_gate (Gate): A gate instance representing the entangler sequence. + """ + options = super()._default_experiment_options() + options.repetitions = list(range(21)) + options.heat_gate = circuit.Gate("heat", num_qubits=2, params=[]) + + return options + + @classmethod + def _default_transpile_options(cls) -> Options: + """Default transpile options.""" + options = super()._default_transpile_options() + options.basis_gates = ["sx", "x", "rz", "heat"] + options.optimization_level = 1 + + return options + + def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: + opt = self.experiment_options + + circs = list() + for repetition in opt.repetitions: + circ = circuit.QuantumCircuit(2, 1) + circ.compose(self._prep_circuit, qubits=[0, 1], inplace=True) + circ.barrier() + for _ in range(repetition): + circ.append(self.experiment_options.heat_gate, [0, 1]) + circ.compose(self._echo_circuit, qubits=[0, 1], inplace=True) + circ.barrier() + circ.compose(self._meas_circuit, qubits=[0, 1], inplace=True) + circ.measure(1, 0) + + # add metadata + circ.metadata = { + "experiment_type": self.experiment_type, + "qubits": self.physical_qubits, + "xval": repetition, + } + + circs.append(circ) + + return circs + + +class BatchHeatHelper(BatchExperiment, ABC): + """A wrapper class of ``BatchExperiment`` to implement HEAT experiment. + + # section: overview + + This is a helper class for experiment developers of the HEAT experiment. + This class overrides :meth:`set_experiment_options` and :meth:`set_transpile_options` + methods of :class:`BatchExperiment` so that it can override options of + subsequence amplification experiments to run them on the same set up. + From end users, this experiment seems as if a single HEAT experiment. + + # section: analysis_ref + :py:class:`HeatAnalysis` + """ + + def __init__( + self, + heat_experiments: List[HeatElement], + heat_analysis: HeatAnalysis, + backend: Optional[Backend] = None, + ): + """Create new HEAT experiment. + + Args: + heat_experiments: A list of error amplification sequence that might be + implemented as :class:``HeatElement`` instance. + heat_analysis: HEAT analysis instance. + backend: Optional, the backend to run the experiment on. + """ + super().__init__(experiments=heat_experiments, backend=backend, analysis=heat_analysis) + + @classmethod + def _default_experiment_options(cls) -> Options: + """Default experiment options. + + Experiment Options: + repetitions (Sequence[int]): A list of the number of echo repetitions. + cr_gate (Gate): A gate instance representing the entangler sequence. + """ + options = super()._default_experiment_options() + options.repetitions = list(range(21)) + options.heat_gate = circuit.Gate("heat", num_qubits=2, params=[]) + + return options + + def set_experiment_options(self, **fields): + """Set the analysis options for :meth:`run` method. + + Args: + fields: The fields to update the options + """ + # propagate options through all nested amplification experiments. + for comp_exp in self.component_experiment(): + comp_exp.set_experiment_options(**fields) + + super().set_experiment_options(**fields) + + @classmethod + def _default_transpile_options(cls) -> Options: + """Default transpile options.""" + options = super()._default_transpile_options() + options.basis_gates = ["sx", "x", "rz", "heat"] + options.optimization_level = 1 + + return options + + def set_transpile_options(self, **fields): + """Set the transpiler options for :meth:`run` method. + + Args: + fields: The fields to update the options + """ + # propagate options through all nested amplification experiments. + for comp_exp in self.component_experiment(): + comp_exp.set_transpile_options(**fields) + + super().set_transpile_options(**fields) diff --git a/qiskit_experiments/library/hamiltonian/heat_zx.py b/qiskit_experiments/library/hamiltonian/heat_zx.py new file mode 100644 index 0000000000..ba18b70be7 --- /dev/null +++ b/qiskit_experiments/library/hamiltonian/heat_zx.py @@ -0,0 +1,212 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. +""" +HEAT experiments for ZX Hamiltonian. +""" + +from typing import Tuple, Optional + +import numpy as np +from qiskit.circuit import QuantumCircuit +from qiskit.providers import Backend + +from .heat_base import BatchHeatHelper, HeatElement +from .heat_analysis import HeatAnalysis + + +class ZXHeat(BatchHeatHelper): + """HEAT experiment for the ZX-type entangler. + + # section: overview + This experiment is designed to amplify the error contained in the + ZX-type generator, a typical Hamiltonian implemented + by a cross resonance drive, which is a foundation of the CNOT gate. + + The preparation circuit locks target qubit state along X-axis, + in which the state is insensitive to the controlled X rotation + imposed by the drive with the ZX-type generator. + + The echo circuit refocuses ZX rotation to identity (II) then applies + a pi-pulse along the interrogated error axis. X error and Y error are + amplified outward the X-Y plane to draw a ping-pong pattern with the + state flip by the pi-pulse echo, while Z error is amplified outward + the X-Z plane in the same manner. + Measurement is projected onto Y-axis in this case. + Because the echoing axis are anti-commute with other Pauli terms, + errors in other axes are cancelled out to reduce rotation + in the interrogated axis. + This enables to selectively amplify the Hamiltonian dynamics in the specific axis. + + .. parsed-literal:: + (xN) + ░ ┌───────┐ ░ + q_0: ────────────░─┤0 ├───────────────────────────░───────── + ┌─────────┐ ░ │ heat │┌────────────────┐┌──────┐ ░ ┌───┐┌─┐ + q_1: ┤ Ry(π/2) ├─░─┤1 ├┤ Rx(-1.0*angle) ├┤ A(π) ├─░─┤ B ├┤M├ + └─────────┘ ░ └───────┘└────────────────┘└──────┘ ░ └───┘└╥┘ + c: 1/══════════════════════════════════════════════════════════╩═ + 0 + (xN) + ┌───┐ ░ ┌───────┐ ░ + q_0: ───┤ X ├────░─┤0 ├──────────────────────░───────── + ┌──┴───┴──┐ ░ │ heat │┌───────────┐┌──────┐ ░ ┌───┐┌─┐ + q_1: ┤ Ry(π/2) ├─░─┤1 ├┤ Rx(angle) ├┤ A(π) ├─░─┤ B ├┤M├ + └─────────┘ ░ └───────┘└───────────┘└──────┘ ░ └───┘└╥┘ + c: 1/═════════════════════════════════════════════════════╩═ + 0 + + ZX-HEAT experiments are performed with combination of two + error amplification experiments shown above, where (``A``, ``B``) + are gates designed based on the interrogated error axis. + Namely, (``Rx(pi)``, ``I``), (``Ry(pi)``, ``I``), (``Rz(pi)``, ``Rx(pi/2)``) + for X, Y, Z axis, respectively. + The circuit in middle is repeated by ``N`` times for the error amplification. + + # section: note + The ``heat`` gate is a special gate to represent the entangler pulse sequence. + This gate is usually not provided by the backend, and thus user must provide + the pulse definition to run this experiment. + This pulse sequence should be pre-calibrated to roughly implement the + ZX(angle) evolution otherwise selective amplification doesn't work properly. + + # section: see_also + HeatElement + + # section: analysis_ref + :py:class:`HeatAnalysis` + + # section: reference + .. ref_arxiv:: 1 2007.02925 + """ + + def __init__( + self, + qubits: Tuple[int, int], + error_axis: str, + backend: Optional[Backend] = None, + angle: Optional[float] = np.pi/2, + ): + """Create new HEAT experiment for the entangler of ZX generator. + + Args: + qubits: Index of control and target qubit, respectively. + error_axis: String representation of axis that amplifies the error, + either one of "x", "y", "z". + backend: Optional, the backend to run the experiment on. + angle: Angle of controlled rotation, which defaults to pi/2. + """ + + amplification_exps = [] + for control in (0, 1): + prep = QuantumCircuit(2) + echo = QuantumCircuit(2) + meas = QuantumCircuit(2) + + if control: + prep.x(0) + echo.rx(angle, 1) + else: + echo.rx(-angle, 1) + prep.ry(np.pi/2, 1) + + if error_axis == "x": + echo.rx(np.pi, 1) + elif error_axis == "y": + echo.ry(np.pi, 1) + elif error_axis == "z": + echo.rz(np.pi, 1) + meas.rx(np.pi/2, 1) + else: + raise ValueError(f"Invalid error term {error_axis}.") + + exp = HeatElement( + qubits=qubits, + prep_circ=prep, + echo_circ=echo, + meas_circ=meas, + backend=backend, + ) + amplification_exps.append(exp) + + analysis = HeatAnalysis( + fit_params=[f"d_heat_{error_axis}0", f"d_heat_{error_axis}1"], + out_params=[f"A_I{error_axis.upper()}", f"A_Z{error_axis.upper()}"], + ) + + super(ZXHeat, self).__init__( + heat_experiments=amplification_exps, + heat_analysis=analysis, + backend=backend, + ) + + +class ZX90HeatXError(ZXHeat): + """HEAT experiment for X error amplification for ZX(pi/2) Hamiltonian. + + # section: see_also + :py:class:`ZXHeat` + """ + + def __init__(self, qubits: [int, int], backend: Optional[Backend] = None): + """Create new experiment. + + qubits: Index of control and target qubit, respectively. + backend: Optional, the backend to run the experiment on. + """ + super(ZX90HeatXError, self).__init__( + qubits=qubits, + error_axis="x", + backend=backend, + angle=np.pi / 2, + ) + + +class ZX90HeatYError(ZXHeat): + """HEAT experiment for Y error amplification for ZX(pi/2) Hamiltonian. + + # section: see_also + :py:class:`ZXHeat` + """ + + def __init__(self, qubits: [int, int], backend: Optional[Backend] = None): + """Create new experiment. + + qubits: Index of control and target qubit, respectively. + backend: Optional, the backend to run the experiment on. + """ + super(ZX90HeatYError, self).__init__( + qubits=qubits, + error_axis="y", + backend=backend, + angle=np.pi/2, + ) + + +class ZX90HeatZError(ZXHeat): + """HEAT experiment for Z error amplification for ZX(pi/2) Hamiltonian. + + # section: see_also + :py:class:`ZXHeat` + """ + + def __init__(self, qubits: [int, int], backend: Optional[Backend] = None): + """Create new experiment. + + qubits: Index of control and target qubit, respectively. + backend: Optional, the backend to run the experiment on. + """ + super(ZX90HeatZError, self).__init__( + qubits=qubits, + error_axis="z", + backend=backend, + angle=np.pi / 2, + ) diff --git a/qiskit_experiments/library/mitigation/__init__.py b/qiskit_experiments/library/mitigation/__init__.py new file mode 100644 index 0000000000..25d6a23a35 --- /dev/null +++ b/qiskit_experiments/library/mitigation/__init__.py @@ -0,0 +1,41 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. + +""" +=============================================================================================== +Readout Mitigation Experiments (:mod:`qiskit_experiments.library.mitigation`) +=============================================================================================== + +.. currentmodule:: qiskit_experiments.library.mitigation + +Experiment +=========== +.. autosummary:: + :toctree: ../stubs/ + :template: autosummary/experiment.rst + + ReadoutMitigationExperiment + + +Analysis +======== + +.. autosummary:: + :toctree: ../stubs/ + :template: autosummary/analysis.rst + + CorrelatedMitigationAnalysis + LocalMitigationAnalysis +""" +from .mitigation_experiment import ReadoutMitigationExperiment +from .mitigation_analysis import CorrelatedMitigationAnalysis +from .mitigation_analysis import LocalMitigationAnalysis diff --git a/qiskit_experiments/library/mitigation/mitigation_analysis.py b/qiskit_experiments/library/mitigation/mitigation_analysis.py new file mode 100644 index 0000000000..8ec1049044 --- /dev/null +++ b/qiskit_experiments/library/mitigation/mitigation_analysis.py @@ -0,0 +1,161 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. +""" +Measurement calibration analysis classes +""" +from typing import List, Tuple +import numpy as np +import matplotlib.pyplot as plt +from qiskit.result import CorrelatedReadoutMitigator +from qiskit.result import LocalReadoutMitigator +from qiskit.result import marginal_counts +from qiskit_experiments.framework import ExperimentData +from qiskit_experiments.framework.matplotlib import get_non_gui_ax +from qiskit_experiments.framework import BaseAnalysis, AnalysisResultData, Options + + +class CorrelatedMitigationAnalysis(BaseAnalysis): + """ + Measurement correction analysis for a full calibration + """ + + def _run_analysis( + self, experiment_data: ExperimentData, **options + ) -> Tuple[List[AnalysisResultData], List["matplotlib.figure.Figure"]]: + data = experiment_data.data() + qubits = experiment_data.metadata["physical_qubits"] + labels = [datum["metadata"]["label"] for datum in data] + matrix = self._generate_matrix(data, labels) + result_mitigator = CorrelatedReadoutMitigator(matrix, qubits=qubits) + analysis_results = [AnalysisResultData("Correlated Readout Mitigator", result_mitigator)] + ax = options.get("ax", None) + figures = [self._plot_calibration(matrix, labels, ax)] + return analysis_results, figures + + def _generate_matrix(self, data, labels) -> np.array: + list_size = len(labels) + matrix = np.zeros([list_size, list_size], dtype=float) + # matrix[i][j] is the probability of counting i for expected j + for datum in data: + expected_outcome = datum["metadata"]["label"] + j = labels.index(expected_outcome) + total_counts = sum(datum["counts"].values()) + for measured_outcome, count in datum["counts"].items(): + i = labels.index(measured_outcome) + matrix[i][j] = count / total_counts + return matrix + + def _plot_calibration(self, matrix, labels, ax=None) -> "matplotlib.figure.Figure": + """ + Plot the calibration matrix (2D color grid plot). + + Args: + matrix: calibration matrix to plot + ax (matplotlib.axes): settings for the graph + + Returns: + The generated plot of the calibration matrix + + Raises: + QiskitError: if _cal_matrices was not set. + + ImportError: if matplotlib was not installed. + + """ + + if ax is None: + ax = get_non_gui_ax() + figure = ax.get_figure() + ax.matshow(matrix, cmap=plt.cm.binary, clim=[0, 1]) + ax.set_xlabel("Prepared State") + ax.xaxis.set_label_position("top") + ax.set_ylabel("Measured State") + ax.set_xticks(np.arange(len(labels))) + ax.set_yticks(np.arange(len(labels))) + ax.set_xticklabels(labels) + ax.set_yticklabels(labels) + return figure + + +class LocalMitigationAnalysis(BaseAnalysis): + """ + Measurement correction analysis for a full calibration + """ + + @classmethod + def _default_options(cls) -> Options: + """Return default analysis options. + + Analysis Options: + plot (bool): Set ``True`` to create figure for fit result. + ax(AxesSubplot): Optional. A matplotlib axis object to draw. + """ + options = super()._default_options() + options.plot = True + options.ax = None + return options + + def _run_analysis( + self, experiment_data: ExperimentData + ) -> Tuple[List[AnalysisResultData], List["matplotlib.figure.Figure"]]: + data = experiment_data.data() + qubits = experiment_data.metadata["physical_qubits"] + matrices = self._generate_matrices(data) + result_mitigator = LocalReadoutMitigator(matrices, qubits=qubits) + analysis_results = [AnalysisResultData("Local Readout Mitigator", result_mitigator)] + if self.options.plot: + figure = assignment_matrix_visualization( + result_mitigator.assignment_matrix(), ax=self.options.ax + ) + figures = [figure] + else: + figures = None + return analysis_results, figures + + def _generate_matrices(self, data) -> List[np.array]: + num_qubits = len(data[0]["metadata"]["label"]) + counts = [None, None] + for result in data: + for i in range(2): + if result["metadata"]["label"] == str(i) * num_qubits: + counts[i] = result["counts"] + matrices = [] + for k in range(num_qubits): + matrix = np.zeros([2, 2], dtype=float) + marginalized_counts = [] + for i in range(2): + marginalized_counts.append(marginal_counts(counts[i], [k])) + # matrix[i][j] is the probability of counting i for expected j + for i in range(2): + for j in range(2): + matrix[i][j] = marginalized_counts[j][str(i)] / sum( + marginalized_counts[j].values() + ) + matrices.append(matrix) + return matrices + + +def assignment_matrix_visualization(assignment_matrix, ax=None): + """Displays a visualization of the assignment matrix compared to the identity""" + if ax is None: + ax = get_non_gui_ax() + figure = ax.get_figure() + n = len(assignment_matrix) + diff = np.abs(assignment_matrix - np.eye(n)) + im2 = ax.matshow(diff, cmap=plt.cm.Reds, vmin=0, vmax=0.2) + ax.set_yticks(np.arange(n)) + ax.set_xticks(np.arange(n)) + ax.set_yticklabels(n * [""]) + ax.set_xticklabels(n * [""]) + ax.set_xlabel(r"$|A - I|$", fontsize=16) + figure.colorbar(im2, ax=ax) + return figure diff --git a/qiskit_experiments/library/mitigation/mitigation_experiment.py b/qiskit_experiments/library/mitigation/mitigation_experiment.py new file mode 100644 index 0000000000..a5febc3372 --- /dev/null +++ b/qiskit_experiments/library/mitigation/mitigation_experiment.py @@ -0,0 +1,114 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. +""" +Measurement calibration experiment classes. +""" +from typing import Iterable, List + +from qiskit import QuantumCircuit +from qiskit.exceptions import QiskitError +from qiskit_experiments.framework import BaseExperiment +from .mitigation_analysis import CorrelatedMitigationAnalysis, LocalMitigationAnalysis + + +class ReadoutMitigationExperiment(BaseExperiment): + """Class for readout mitigation experiments""" + + METHOD_LOCAL = "local" + METHOD_CORRELATED = "correlated" + ALL_METHODS = [METHOD_LOCAL, METHOD_CORRELATED] + + def __init__(self, qubits: Iterable[int], method=METHOD_LOCAL): + """Initialize a mitigation experiment. + + Args: + qubits: The qubits being mitigated + method: A string denoting mitigation method + + Raises: + QiskitError: if the given mitigation method is not recoginzed + + Additional info: + The currently supported mitigation methods are: + * "local": each qubit is mitigated by itself; this is the default method, + and assumes readout errors are independent for each qubits + * "correlated": All the qubits are mitigated together; this results in an exponentially + large mitigation matrix and so is useable only for a small number of qubits, + but might be more accurate than local mitigation. + """ + super().__init__(qubits) + if method not in self.ALL_METHODS: + raise QiskitError("Method {} not recognized".format(method)) + if method == self.METHOD_LOCAL: + self.helper = LocalMitigationHelper(self.num_qubits) + if method == self.METHOD_CORRELATED: + self.helper = CorrelatedMitigationHelper(self.num_qubits) + + self.analysis = self.helper.analysis() + + def circuits(self) -> List[QuantumCircuit]: + """Returns the experiment's circuits""" + return [self._calibration_circuit(self.num_qubits, label) for label in self.helper.labels()] + + @staticmethod + def _calibration_circuit(num_qubits: int, label: str) -> QuantumCircuit: + """Return a calibration circuit. + + This is an N-qubit circuit where N is the length of the label. + The circuit consists of X-gates on qubits with label bits equal to 1, + and measurements of all qubits. + """ + circ = QuantumCircuit(num_qubits, name="meas_mit_cal_" + label) + for i, val in enumerate(reversed(label)): + if val == "1": + circ.x(i) + circ.measure_all() + circ.metadata = {"label": label} + return circ + + +class CorrelatedMitigationHelper: + """Helper class for correlated mitigation experiment data""" + + def __init__(self, num_qubits: int): + """Creates the helper class + Args: + num_qubits: The number of qubits being mitigated + """ + self.num_qubits = num_qubits + + def analysis(self): + """Returns the analysis class for the mitigation""" + return CorrelatedMitigationAnalysis() + + def labels(self) -> List[str]: + """Returns the labels dictating the generation of the mitigation circuits""" + return [bin(j)[2:].zfill(self.num_qubits) for j in range(2 ** self.num_qubits)] + + +class LocalMitigationHelper: + """Helper class for local mitigation experiment data""" + + def __init__(self, num_qubits: int): + """Creates the helper class + Args: + num_qubits: The number of qubits being mitigated + """ + self.num_qubits = num_qubits + + def analysis(self): + """Returns the analysis class for the mitigation""" + return LocalMitigationAnalysis() + + def labels(self) -> List[str]: + """Returns the labels dictating the generation of the mitigation circuits""" + return ["0" * self.num_qubits, "1" * self.num_qubits] diff --git a/qiskit_experiments/library/quantum_volume/qv_analysis.py b/qiskit_experiments/library/quantum_volume/qv_analysis.py index f5a41e3f2d..f257937a5a 100644 --- a/qiskit_experiments/library/quantum_volume/qv_analysis.py +++ b/qiskit_experiments/library/quantum_volume/qv_analysis.py @@ -249,7 +249,7 @@ def _format_plot( hop_accumulative = np.cumsum(heavy_probs) / trial_list two_sigma = 2 * (hop_accumulative * (1 - hop_accumulative) / trial_list) ** 0.5 - # Plot inidivual HOP as scatter + # Plot individual HOP as scatter ax = plot_scatter( trial_list, heavy_probs, diff --git a/qiskit_experiments/library/randomized_benchmarking/interleaved_rb_experiment.py b/qiskit_experiments/library/randomized_benchmarking/interleaved_rb_experiment.py index 2684999793..4c0d8179ba 100644 --- a/qiskit_experiments/library/randomized_benchmarking/interleaved_rb_experiment.py +++ b/qiskit_experiments/library/randomized_benchmarking/interleaved_rb_experiment.py @@ -23,6 +23,7 @@ from qiskit.exceptions import QiskitError from qiskit.providers.backend import Backend +import qiskit_experiments.data_processing as dp from .rb_experiment import StandardRB from .interleaved_rb_analysis import InterleavedRBAnalysis @@ -86,6 +87,12 @@ def __init__( full_sampling=full_sampling, ) self.analysis = InterleavedRBAnalysis() + self.analysis.set_options( + data_processor=dp.DataProcessor( + input_key="counts", + data_actions=[dp.Probability(outcome="0" * self.num_qubits)], + ) + ) def _sample_circuits(self, lengths, rng): circuits = [] diff --git a/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py b/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py index 62c6e720a1..4d424e2c6b 100644 --- a/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py +++ b/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py @@ -181,16 +181,16 @@ def _extra_database_entry(self, fit_data: curve.FitData) -> List[AnalysisResultD ) # Calculate EPG - if not self._get_option("gate_error_ratio"): + if not self.options.gate_error_ratio: # we attempt to get the ratio from the backend properties - if not self._get_option("error_dict"): + if not self.options.error_dict: gate_error_ratio = RBUtils.get_error_dict_from_backend( backend=self._backend, qubits=self._physical_qubits ) else: - gate_error_ratio = self._get_option("error_dict") + gate_error_ratio = self.options.error_dict else: - gate_error_ratio = self._get_option("gate_error_ratio") + gate_error_ratio = self.options.gate_error_ratio count_ops = [] for meta in self._data(label="raw_data").metadata: @@ -208,7 +208,7 @@ def _extra_database_entry(self, fit_data: curve.FitData) -> List[AnalysisResultD gates_per_clifford, ) elif num_qubits == 2: - epg_1_qubit = self._get_option("epg_1_qubit") + epg_1_qubit = self.options.epg_1_qubit epg = RBUtils.calculate_2q_epg( epc, self._physical_qubits, diff --git a/releasenotes/notes/0.2/0_2_release-eef5e3ba256fc750.yaml b/releasenotes/notes/0.2/0_2_release-eef5e3ba256fc750.yaml new file mode 100644 index 0000000000..bd79da53af --- /dev/null +++ b/releasenotes/notes/0.2/0_2_release-eef5e3ba256fc750.yaml @@ -0,0 +1,42 @@ +--- +prelude: > + The Qiskit Experiments 2.0 release includes several bug fixes and + improvements and many new experiments added to the + :mod:`~qiskit_experiments.library`. Added experiments include a full suite + of single-qubit gate calibration and characterization experiments, and + two-qubit Cross-resonance Hamiltonian characterization experiments. + + There are also numerous changes and improvements to the base classes in + :mod:`~qiskit_experiments.framework` and + :mod:`~qiskit_experiments.calibration_management` to make + developing new experiments easier, to improve JSON serialization, and to + improve storing and loading experiments for the IBM experiment database + service. +features: + - | + You can now change the default ``matplotlib`` backend used for generated + figures by setting + ``qiskit_experiments.framework.matplotlib.default_figure_canvas`` to the + desired canvas. Note that it has to be a canvas for one of the + `non-interactive backend + `_. + For example, you can set ``default_figure_canvas`` to + :class:`~matplotlib.backends.backend_agg.FigureCanvasAgg` to use the + ``AGG`` backend. +upgrade: + - | + The default ``matplotlib`` backend used for generated figures was changed + from `AGG` to `SVG`. + - | + Due to changes in JSON serialization it is possible that some experiments + saved to the IBM Experiments database service using the Qiskit Experiments + 0.1 API may not be able to be loaded using Qiskit Experiments 0.2. +developer: + - | + Adds hooks for serialization and deserialization of custom classes to + the JSON :class:`qiskit_experiments.framework.ExperimentEncoder` + and :class:`qiskit_experiments.framework.ExperimentDecoder`. + To enable serialization classes must implement a method + ``__json_encode__(self) -> Any`` that returns a JSON serializable object, + and a class method ``_json_decode__(cls, value: Any)__ -> cls`` that can + reconstruct the object from the JSON deserialized value. diff --git a/releasenotes/notes/analysis-run-b4ba83436a562a01.yaml b/releasenotes/notes/0.2/base-analysis-b261afaa40518b53.yaml similarity index 52% rename from releasenotes/notes/analysis-run-b4ba83436a562a01.yaml rename to releasenotes/notes/0.2/base-analysis-b261afaa40518b53.yaml index a00b935639..e599fbb20c 100644 --- a/releasenotes/notes/analysis-run-b4ba83436a562a01.yaml +++ b/releasenotes/notes/0.2/base-analysis-b261afaa40518b53.yaml @@ -15,6 +15,12 @@ features: contains analysis results or figures, a copy with a unique experiment ID will be returned containing only the new analysis results and figures. This data can then be saved as its own experiment to a database service. + - | + Added a :meth:`~qiskit_experiments.framework.BaseAnalysis.set_options` + method and :meth:`~qiskit_experiments.framework.BaseAnalysis.options` + property to the :class:`qiskit_experiments.framework.BaseAnalysis` class + to store and retrieve any analysis options in the state of the analysis + instance. upgrade: - | Changed :meth:`~qiskit_experiments.framework.BaseAnalysis.run` to run @@ -22,3 +28,19 @@ upgrade: :meth:`~qiskit_experiments.framework.ExperimentData.add_analysis_callback`. Previously analysis was only run asynchronously if it was done as part of an experiments :meth:`~qiskit_experiments.framework.BaseExperiment.run`. +developer: + - | + The :class:`qiskit_experiments.framework.BaseAnalysis` class has + been changed to be an initialized class. + + This class now stores its set analysis options using the + :meth:`~qiskit_experiments.framework.BaseAnalysis.set_options` and + :meth:`~qiskit_experiments.framework.BaseAnalysis.options` and + ``_default_options`` methods. + The signature of the abstract method ``_run_analysis`` that must be + implemented by subclasses has been changed to remove the ``**kwargs``. + + Note that the state of this class should only be used to store option + values and derived configuration. The + :meth:`~qiskit_experiments.framework.BaseAnalysis.run` and + ``_run_analysis`` methods should not change the state of the instance. diff --git a/releasenotes/notes/analysis-b76909b1307d653b.yaml b/releasenotes/notes/0.2/base-experiment-14eba2646ef0f0b9.yaml similarity index 56% rename from releasenotes/notes/analysis-b76909b1307d653b.yaml rename to releasenotes/notes/0.2/base-experiment-14eba2646ef0f0b9.yaml index 0f47d84d98..3e9d5850f8 100644 --- a/releasenotes/notes/analysis-b76909b1307d653b.yaml +++ b/releasenotes/notes/0.2/base-experiment-14eba2646ef0f0b9.yaml @@ -1,11 +1,5 @@ --- -feature: - - | - Added a :meth:`~qiskit_experiments.framework.BaseAnalysis.set_options` - method and :meth:`~qiskit_experiments.framework.BaseAnalysis.options` - property to the :class:`qiskit_experiments.framework.BaseAnalysis` class - to store and retrieve any analysis options in the state of the analysis - instance. +features: - | The ``analysis`` kwarg of :meth:`qiskit_experiments.framework.BaseExperiment.run` can now optionally @@ -14,6 +8,32 @@ feature: current stored :meth:`~qiskit_experiments.framework.BaseExperiment.analysis` instance for that experiment will be used. Setting ``analysis=None`` disables analysis for the specific execution. + - | + Add ``backend`` as an optional ``__init__`` kwarg for all experiments to + allow setting the backend at initialization. The backand can also be set + and retrieved from the experiment object after construction using the + :meth:`~qiskit_experiments.framework.BaseExperiment.backend` + property and setter. + + When using the ``backend`` kwarg of + :meth:`~qiskit_experiments.framework.BaseExperiment.run` to specify + a backend this will temporarily override any currently set backends + for that single execution. + - | + Add :class:`~qiskit_experiments.framework.ExperimentConfig` dataclass + for storing the configuration of an experiment. This configuration can + be obtained by using the + :meth:`~qiskit_experiments.framework.BaseExperiment.config` property. + Experiments can also be reconstructed from their configuration using + the :meth:`~qiskit_experiments.framework.BaseExperiment.from_config` + class method. + - | + Adds automatic job splitting to + :class:`~qiskit_experiments.framework.BaseExperiment` for execution of + experiments with a larger number of circuits than can be run in a single + job on the target backend. This enables running large experiments on + legacy and non-IBM backends that don't handle job splitting + automatically. upgrade: - | The :meth:`qiskit_experiments.framework.BaseExperiment.analysis` property @@ -50,22 +70,14 @@ deprecations: :meth:`qiskit_experiments.framework.BaseExperiment.run` have been deprecated. Use ``analysis="default"`` instead of ``analysis=True``, and ``analysis=None``instead of ``analysis=False``. -developer: - | - The :class:`qiskit_experiments.framework.BaseAnalysis` class has - been changed to be an initialized class. - - This class now stores its set analysis options using the - :meth:`~qiskit_experiments.framework.BaseAnalysis.set_options` and - :meth:`~qiskit_experiments.framework.BaseAnalysis.options` and - ``_default_options`` methods. - The signature of the abstract method ``_run_analysis`` that must be - implemented by subclasses has been changed to remove the ``**kwargs``. - - Note that the state of this class should only be used to store option - values and derived configuration. The - :meth:`~qiskit_experiments.framework.BaseAnalysis.run` and - ``_run_analysis`` methods should not change the state of the instance. + Using the ``__analysis_class__`` class attrbiute to define a default + :class:`~qiskit_experiments.framework.BaseAnalysis` class in a + :class:`~qiskit_experiments.framework.BaseExperiment` subclass has + been deprecated. A default analysis class instance should now be passed to + the initialization method of + :class:`~qiskit_experiments.framework.BaseExperiment` instead. +developer: - | The :class:`qiskit_experiments.framework.BaseExperiment` class has been changed to optionally store an instance of a @@ -74,3 +86,11 @@ developer: particular experiment subclass should be set during that experiments init method, or as default options of the analysis subclass used by that experiment. + - | + Added a ``_set_backend`` method to + :class:`~qiskit_experiments.framework.BaseExperiment` that is called + when a backend is set via initalization or the ``backend`` setter. This + can be overridden in experiment subclasses if required. For example this + could be used to extract any needed configuration or properties from the + specified backend, or to update experiment options of configuration based + on the backend. \ No newline at end of file diff --git a/releasenotes/notes/0.2/calibrations-97c6ae807d54015d.yaml b/releasenotes/notes/0.2/calibrations-97c6ae807d54015d.yaml new file mode 100644 index 0000000000..0f43ae313f --- /dev/null +++ b/releasenotes/notes/0.2/calibrations-97c6ae807d54015d.yaml @@ -0,0 +1,40 @@ +--- +features: + - | + Added support for JSON serialization to + :class:`qiskit_experiments.calibration_management.BasisGateLibrary`. + - | + Simplified the update library for calibration experiments by merging + this functionality into the + :class:`qiskit_experiments.calibration_management.BaseCalibrationExperiment`. + + Future releases may fully deprecate the + :class:`qiskit_experiments.calibration_management.update_library.BaseUpdater` + in favour of moving its functionality into the + :class:`~qiskit_experiments.calibration_management.BaseCalibrationExperiment`. +upgrade: + - | + The ``BackendCalibrations`` class has bas been removed and its functionality + has been merged into the + :class:`qiskit_experiments.calibration_management.Calibrations` class. Users + should now use the :class:`Calibrations` + class which can be instantiated from a backend by using the + :meth:`~qiskit_experiments.calibration_management.Calibrations.from_backend` + method. +developer: + - | + :mod:`~qiskit_experiments.library.calibration` experiments have been changed + so that each experiment is a subclass of a + :mod:`~qiskit_experiments.library.characterization` experiment. + + Calibration experiments should now be constructed as subclasses of both + the relevant characterization experiment and the new + :class:`qiskit_experiments.calibration_management.BaseCalibrationExperiment` + abstract base class. + + This new base class implements the functionality to manage schedules + stored in instances of the + :class:`qiskit_experiments.calibration_management.Calibrations` class and + implements a framework where calibration experiments can specify an + :meth:`~qiskit_experiments.calibration_management.BaseCalibrationExperiment.update` + method to update the parameters that they are designed to calibrate. diff --git a/releasenotes/notes/0.2/curve-analysis-f4d62e011815c5c3.yaml b/releasenotes/notes/0.2/curve-analysis-f4d62e011815c5c3.yaml new file mode 100644 index 0000000000..822e30beb7 --- /dev/null +++ b/releasenotes/notes/0.2/curve-analysis-f4d62e011815c5c3.yaml @@ -0,0 +1,47 @@ +--- +issues: + - | + Curve analysis may have imperfection in the uncertainty propagation computation. + Fit paramters consist of the nominal part and standard error, however, + the correlation of these paramters are not precisely taken into account. + This sometimes result in the overestimation of the confidence interval of fit curves, + or overestimation of the standard error of some analysis values computed with + multiple fitting parameters. This issue will be solved in the version 0.3. + See `qiskit-experiments/#551 `_ + for details. +developer: + - | + There have been several changes to the + :class:`qiskit_experiments.curve_analysis.CurveAnalysis` class which may + effect developers implementing experiments using curve analysis. + + These include: + + * Default data pre-processing for curve fit data has been added to + :class:`~qiskit_experiments.curve_analysis.CurveAnalysis`. The input data + to the analysis is averaged over the same x values and sorted by x values. + This processing is necessary to correctly analyze an experimental data which + has duplicate x values. + + * The handling of user provided initial guesses and fit boundaries has been + changed so that subclasses no longer need to use user provided option values. + Propagation of user values is now handled by the base class. + + * The subclass method name to provide these initial guesses for curve fitting + has bee changed from ``_setup_fitting`` to + :meth:`~qiskit_dynamics.curve_analysis.CurveAnalysis._generate_fit_guesses`. + See the documentation for more details. + + * Tick labels and fit parameters shown in the fit result plot generated by + :class:`qiskit_experiments.curve_analysis.CurveAnalysis` + subclasses are formatted with unit with prefactor scalings. + For example, 1e7 can now be displayed as 10 MHz. + + To enable this change, all experiments using + :class:`~qiskit_experiments.curve_analysis.CurveAnalysis` or a subclass + analysis class should set analysis options ``xval_unit`` and ``yval_unit``. + - | + A ``model_description`` fields has been added to the + :class:`qiskit_experiments.curve_analysis.SeriesDef` dataclass. This field + stores the string representation of the fit model of the curve and will be + stored in the experiments analysis result extra field. diff --git a/releasenotes/notes/0.2/data-processors-d6430844d2701eb1.yaml b/releasenotes/notes/0.2/data-processors-d6430844d2701eb1.yaml new file mode 100644 index 0000000000..344e52faca --- /dev/null +++ b/releasenotes/notes/0.2/data-processors-d6430844d2701eb1.yaml @@ -0,0 +1,25 @@ +--- +feature: + - | + The :class:`qiskit_experiments.data_processing.Probability` + data processing node has been enhanced to compute the estimated mean and + standard deviation of a measured outcome probability using a Bayesian update + of a a Beta distribution prior from the observed measurement outcomes. + The default prior is an uninformative prior. The user can also provide a custom + prior for the probability distribution. +fixes: + -| + The :class:`qiskit_experiments.data_processing.Probability` data processing + node will no longer return exactly 0 or 1 for a probability estimate. This + fixes an issue where this could cause division by 0 when computing weights + during curve fitting analysis. +upgrade: + - | + Data format used in the :class:`qiskit_experiments.data_processing.DataProcessor` + has been changed from `Tuple[Any, Any]` to `np.ndarray`. + - | + Uncertainty propagation in the + :class:`qiskit_experiments.data_processing.DataProcessor` class is now + computed using the `uncertainties `_ + package. + See :mod:`qiskit_experiments.data_processing` module documentation for details. diff --git a/releasenotes/notes/0.2/experiment-data-5465208160fe6b6a.yaml b/releasenotes/notes/0.2/experiment-data-5465208160fe6b6a.yaml new file mode 100644 index 0000000000..455db7ca2f --- /dev/null +++ b/releasenotes/notes/0.2/experiment-data-5465208160fe6b6a.yaml @@ -0,0 +1,111 @@ +--- +features: + - | + Adds + :meth:`~qiskit_experiments.database_service.DbExperimentData.add_analysis_callback` + method to :class:`~qiskit_experiments.framework.ExperimentData` + for adding a post-processing analysis function to run as a callback after + currently executing experiment jobs are finished. + - | + Added a ``block`` kwarg with default value ``block=True`` to the + :meth:`qiskit_experiments.framework.ExperimentData.analysis_results` + method. If this is True then calling + :meth:`~qiskit_experiments.framework.ExperimentData.analysis_results` + will block to wait for all running analysis callbacks to finish before + returning results. This prevents issues where trying to retrieve analysis + results before analysis was finished would raise an error that the result + could not be found. + + Note that in the case of + :class:`~qiskit-experiments.framework.ParallelExperiment` and + :class:`~qiskit-experiments.framework.BatchExperiment` blocking or + calling ``analysis_results`` on the parent experiment should be performed + before attempting to access results in the component experiment data + containers to ensure the component analysis callbacks + have been initialized. + - | + The :class:`~qiskit_experiments.framework.ExperimentData` class + can now store child ``ExperimentData`` containers. + Child data can either be added at initialization using the + ``child_data`` kwarg or added later using the + :meth:`~qiskit_experiments.framework.ExperimentData.add_child_data` + method. Child ``ExperimentData`` can be accessed using the + :meth:`~qiskit_experiments.framework.ExperimentData.child_data` + method. + - | + Added a :meth:`~qiskit_experiments.framework.ExperimentData.copy` + method to :class:`~qiskit_experiments.framework.ExperimentData` which + allows making a copy of an experiment data container with a new + experiment ID, new result IDs, and new figure names, generated for + the copy. + + This method has a kwarg option ``copy_results`` that can be set to + ``False`` to only copy the experiment + :meth:`~qiskit_experiments.framework.ExperimentData.data` and + metadata, but not the analysis results and figures. + - | + Adds methods + :meth:`~qiskit_experiments.framework.ExperimentData.add_tags_recursive` and + :meth:`~qiskit_experiments.framework.ExperimentData.remove_tags_recursive` + to :class:`qiskit_experiments.framework.ExperimentData` for adding and + removing tags of an experiment data object and all its + :meth:`~qiskit_experiments.framework.ExperimentData.child_data`. + - | + Adds support for saving and loading + :class:`qiskit_experiments.framework.ParallelExperiment` + and :class:`qiskit_experiments.framework.BatchExperiment` experiment data + and all component experiment data and results from the IBM experiments + database service. Changing the share level of the parent composite + experiment will also change the share level of all component experiments. + + When saving composite experiments each component experiment analysis + results and figures will be saved under a unique experiment ID. Note that + these component experiments do not save any of the marginalized circuit + result data. The unmarginalized circuit result data is saved in the parent + componsite experiments. +upgrade: + - | + The ``CompositeExperimentData`` class has been removed and its + functionality integrated into the + :class:`~qiskit_experiments.framework.ExperimentData` class. + A composite :class:`~qiskit_experiments.framework.ExperimentData` + can now be created by initializing with a list of child + ``ExperimentData`` containers using the ``child_data`` kwarg. + - | + :class:`~qiskit_experiments.framework.ParallelExperiment` and + :class:`~qiskit_experiments.framework.BatchExperiment` now return + a :class:`~qiskit_experiments.framework.ExperimentData` object + which no longer contains a ``component_experiment_data`` method. + This method has been replaced by the + :meth:`~qiskit_experiments.framework.ExperimentData.child_data` + method. + - | + The :meth:`qiskit_experiments.framework.ExperimentData.analysis_results` + method has been changed to block on analysis callbacks finishing by + default, this means it is no longer necessary to call the + :meth:`~qiskit_experiments.framework.ExperimentData.block_for_results` + method first before accessing analysis results. + + To disable blocking this can be set to run with ``block=False``. + This should be used + :meth:`~qiskit_experiments.framework.ExperimentData.analysis_results` + needs to be called during another analysis callback to prevent that + callback from blocking. + - | + The ``callback`` and ``**kwarg`` arguments have been removed from + :meth:`~qiskit_experiments.framwork.ExperimentData.add_data` + To add a callback function to run after experiment jobs have finished + executing use the + :meth:`~qiskit_experiments.framework.ExperimentData.add_analysis_callback` + method instead. +fixes: + - | + Fixes a bug where the + :meth:`qiskit_experiments.framework.ExperimentData.load` method + would return an + :class:`~qiskit_experiments.database_service.DbExperimentDataV1` object + instead of a :class:`~qiskit_experiments.framework.ExperimentData` object. + - | + Fixed bug in :class:`qiskit_experiments.framework.ExperimentData` where + trying to load saved job data from a backend using + ``ExperimentData(backend=backend, job_ids=job_ids)`` resulted in an error. diff --git a/releasenotes/notes/0.2/library-26e6cf3dfbc3acb3.yaml b/releasenotes/notes/0.2/library-26e6cf3dfbc3acb3.yaml new file mode 100644 index 0000000000..d332f06d29 --- /dev/null +++ b/releasenotes/notes/0.2/library-26e6cf3dfbc3acb3.yaml @@ -0,0 +1,159 @@ +--- +features: + - | + Adds a collection of experiments for performing single-qubit gate + :mod:`~qiskit_experiments.library.characterization` and + :mod:`~qiskit_experiments.library.calibration`. The new experiments are + + * :class:`~qiskit_experiments.library.characterization.Rabi`: + This experiment scans the amplitude of a pulse and measures the qubit + population. This allows us to determine the amplitude that creates, + for example, an ``X`` gate and/or a ``SX`` gate. + + * :class:`~qiskit_experiments.library.calibration.RoughXSXAmplitudeCal`: + The calibration version of :class:`~qiskit_experiments.library.characterization.Rabi`. + It extracts the amplitudes needed to implement an ``X`` gate an a ``SX`` gate. + This type of calibration is a rough amplitude calibration since the resulting + parameter value is typically not very precises. + + * :class:`~qiskit_experiments.library.characterization.FineAmplitude`: This experiment repeats + a rotation a variable number of times to amplify over- and under-rotations. + The resulting ping-pong pattern in the qubit population is fit to determine + the error in the rotation angle. + This experiment has specializations for X + (:class:`~qiskit_experiments.library.characterization.FineXAmplitude`) and SX + (:class:`~qiskit_experiments.library.characterization.FineSXAmplitude`) gates. + + * :class:`~qiskit_experiments.library.calibration.FineAmplitudeCal`: The calibration version + of :class:`~qiskit_experiments.library.characterization.FineAmplitude`. + It will update the amplitude of the pulse according to the measurred deviation. + This experiment has specializations for X + (:class:`~qiskit_experiments.library.calibration.FineXAmplitudeCal`) and SX + (:class:`~qiskit_experiments.library.calibration.FineSXAmplitudeCal`) gates. + + * :class:`~qiskit_experiments.library.characterization.RoughDrag`: + This experiment scans the DRAG parameter of a repeated + sequence of rotation and anti-rotation. If the DRAG parameter does not have + the correct value phase errors will accumulate and the repeated sequece of + gates will not return the qubit to the ground state. + + * :class:`~qiskit_experiments.library.calibration.RoughDragCal`: The calibration version of + :class:`~qiskit_experiments.library.characterization.RoughDrag`. + + * :class:`~qiskit_experiments.library.characterization.FineDrag`: This experiment iterates the + gate sequence Rp - Rm where Rp is a rotation around an axis and Rm is the same + rotation but in the opposite direction. This sequence amplifies phase errors due + to the presence of higher excited states. + This experiment has specializations for X + (:class:`~qiskit_experiments.library.characterization.FineXDrag`) and SX + (:class:`~qiskit_experiments.library.characterization.FineSXDrag`) gates. + + * :class:`~qiskit_experiments.library.calibration.FineDragCal`: The calibration version of + :class:`~qiskit_experiments.library.characterization.FineDrag`. + This will update the DRAG parameter in the instance of the + :class:`:class:`~qiskit_experiments.calibration_management.Calibrations` class. + This experiment has specializations for X + (:class:`~qiskit_experiments.library.calibration.FineXDragCal`) and SX + (:class:`~qiskit_experiments.library.calibration.FineSXDragCal`) gates. + + * :class:`~qiskit_experiments.library.characterization.QubitSpectroscopy`: This experiment + performs spectroscopy by applying a frequency shift to a long pulse. This experiment + is typically used to identify the resonance frequency of the qubit. + + * :class:`~qiskit_experiments.library.calibration.RoughFrequencyCal` the calibration + version of :class:`~qiskit_experiments.library.characterization.QubitSpectroscopy`. + This gives us a first rough estimate of the qubits frequency. + + * :class:`~qiskit_experiments.library.characterization.RamseyXY`: This experiment + performs a Ramsey-XY experiment which allows us to measure the frequency of the qubit. + This experiment is sensitive to the sign of the frequency offset from the main transition. + It is a more precise measurement than spectroscopy. + + * :class:`~qiskit_experiments.library.calibration.FrequencyCal`: This is the calibration + version of :class:`~qiskit_experiments.library.characterization.RamseyXY`. + + * :class:`~qiskit_experiments.library.characterization.FineFrequency`: + This experiment performs an error amplifying sequence to measure the frequency of the qubit. + This is done with delay instructions with a variable length and RZ gates. + + * :class:`~qiskit_experiments.library.calibration.FineFrequencyCal`: + This is the calibration version of + :class:`~qiskit_experiments.library.characterization.FineFrequency`. + + * :class:`~qiskit_experiments.library.characterization.HalfAngle`: + This experiment measures the amount by which the SX and X gates are not parallel. + Such errors can occur due to phase errors. For example, + the non-linearities in the mixer's skew for :math:`\pi/2` pulses may be + different from the :math:`\pi` pulse. + + * :class:`~qiskit_experiments.library.calibration.HalfAngleCal`: + This is the calibration version of + :class:`~qiskit_experiments.library.characterization.HalfAngle`. + - | + Two cross-resonance Hamiltonian tomography experiments have been + added to the :mod:`qiskit_experiments.library`. + + * :class:`qiskit_experiments.library.CrossResonanceHamiltonian` + * :class:`qiskit_experiments.library.EchoedCrossResonanceHamiltonian` + + These experiments estimates the IX, IY, IZ, ZX, ZY, ZZ Hamiltonian term + coefficients of the cross-resonance Hamiltonian, using either a single-tone + cross-resonance gate + (:class:`~qiskit_experiments.library.CrossResonanceHamiltonian`) + or an echoed cross-resonance gate + (:class:`~qiskit_experiments.library.EchoedResonanceHamiltonian`). + - | + Adds a :class:`qiskit_experiments.library.ReadoutAngle` characterization + experiment. This experiment computes the average of the angles of the IQ + clusters of the ground and excited states. + - | + :class:`~qiskit_experiments.library.StandardRb` and + :class:`~qiskit_experiments.library.InterleavedRb` experiments now + compute error as part of the error-per-gate computation. + +upgrade: + - | + All :mod:`qiskit_experiments.library` experiments have been changed to work + with fixed SI units: `Hz` for frequency, `seconds` for delays, and backend + `dt` for pulse widths and durations. + Previous experiments with ``unit`` kwargs in their init functions have had + this kwarg removed. + - | + The ``qubits`` intitialization argument for + :class:`~qiskit_experiments.library.StandardRb`, + :class:`~qiskit_experiments.library.InterleavedRb`, + :class:`~qiskit_experiments.library.QuantumVolume`, + :class:`~qiskit_experiments.library.StateTomography`, + and + :class:`~qiskit_experiments.library.ProcessTomography` + no longer accepts interger values for specifying a range of qubits. + and must now contain an explicit sequence of qubits. + - | + The behavior of the ``seed`` initialization kwarg of the + :class:`~qiskit_experiments.library.StandardRB`, + :class:`~qiskit_experiments.library.InterleavedStandardRB`, + :class:`~qiskit_experiments.library.QuantumVolume` experiments has + been modified. + + In the new version the ``seed`` value is used as to initialize a Numpy + random number generator object as ``numpy.random.default_rng(seed=seed)`` + each time the experiments ``circuits`` method is called. This change means + that using a fixed seed value will result in the same circuits being + generated each time an experiment is run, if no other + experiment options are changed. + + To generate different new random circuits each time an experiment is run + the (default) value of ``seed=None`` should be used. To reproduce + equivalent functionality to the previous version behavior of differnet + circuits being generated each time ``run`` is called with a fixed seed + you must now set a new fixed seed value between each call to ``run`` + using ``experiment.set_experiment_options(seed=value)``. +fixes: + - | + Fixes a bug in :class:`~qiskit_experiments.library.StandardRB` and + :class:`~qiskit_experiments.library.InterleavedRB` where the variance in + the estimated error per Clifford did not scale correctly with the number + of sampled RB sequences. + + See `Issue 428 `_ + for details. diff --git a/releasenotes/notes/add-analysis-callback-018da6edf8a5a27f.yaml b/releasenotes/notes/add-analysis-callback-018da6edf8a5a27f.yaml deleted file mode 100644 index 133b14d81e..0000000000 --- a/releasenotes/notes/add-analysis-callback-018da6edf8a5a27f.yaml +++ /dev/null @@ -1,16 +0,0 @@ ---- -features: - - | - Adds - :meth:`~qiskit_experiments.database_service.DbExperimentData.add_analysis_callback` - method to :class:`~qiskit_experiments.database_service.DbExperimentData` - for adding a post-processing analysis function to run as a callback after - currently executing experiment jobs are finished. -upgrade: - - | - The ``callback`` and ``**kwarg`` arguments have been removed from - :meth:`~qiskit_experiments.database_services.DbExperimentData.add_data` - To add a callback function to run after experiment jobs have finished - executing use the - :meth:`~qiskit_experiments.database_services.DbExperimentData.add_analysis_callback` - method instead. diff --git a/releasenotes/notes/add-aux-unit-support-curve-analysis-beb4150ef5de283b.yaml b/releasenotes/notes/add-aux-unit-support-curve-analysis-beb4150ef5de283b.yaml deleted file mode 100644 index 92ee97413c..0000000000 --- a/releasenotes/notes/add-aux-unit-support-curve-analysis-beb4150ef5de283b.yaml +++ /dev/null @@ -1,13 +0,0 @@ ---- -developer: - - | - Tick labels and fit parameters shown in the fit result plot generated by - :py:class:`~qiskit_experiments.curve_analysis.curve_analysis.CurveAnalysis` subclasses - are nicely formatted with unit with prefactor scalings. - For example, 1e7 might be now displayed as 10 MHz. - - To enable this change, all third party experiment classes based on - :py:class:`~qiskit_experiments.curve_analysis.curve_analysis.CurveAnalysis` - need to set new analysis options ``xval_unit`` and ``yval_unit``. - These options are likely set by the class itself rather than end-users, - and the fit plots are immediately updated once these options are provided. diff --git a/releasenotes/notes/add-error-bounds-to-epg-816007dbaa46a73d.yaml b/releasenotes/notes/add-error-bounds-to-epg-816007dbaa46a73d.yaml deleted file mode 100644 index 61df3ae46f..0000000000 --- a/releasenotes/notes/add-error-bounds-to-epg-816007dbaa46a73d.yaml +++ /dev/null @@ -1,6 +0,0 @@ ---- -other: - - | - Error bounds are now computed as part of the error-per-gate computation - in the randomized benchmarking experiment. The result is now given as - a `FitVal` instead of `float`. diff --git a/releasenotes/notes/add-fit-models-e9b82e9d6c2aef43.yaml b/releasenotes/notes/add-fit-models-e9b82e9d6c2aef43.yaml deleted file mode 100644 index 1d31886ef4..0000000000 --- a/releasenotes/notes/add-fit-models-e9b82e9d6c2aef43.yaml +++ /dev/null @@ -1,10 +0,0 @@ ---- -developer: - - | - ``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). - - Developer of the curve analysis subclass can use this field to bind information of - fit model to the fit result entry as a part of the extra information. diff --git a/releasenotes/notes/add-new-xy-mean-strategy-45cf11e86c0919c5.yaml b/releasenotes/notes/add-new-xy-mean-strategy-45cf11e86c0919c5.yaml deleted file mode 100644 index 053b051c38..0000000000 --- a/releasenotes/notes/add-new-xy-mean-strategy-45cf11e86c0919c5.yaml +++ /dev/null @@ -1,8 +0,0 @@ ---- -features: - - | - ``shots_weighted`` options is added to :py:func:`~qiskit_experiments.curve_analysis.\ - data_processing.mean_xy_data` and :py:func:`~qiskit_experiments.curve_analysis.\ - data_processing.multi_mean_xy_data`. This options will change averaging stategy to - compute value and sigma of y data by weighted average and weighed sum of variances, - respectively. The number of shots used to obtain the data point is used as a weight. diff --git a/releasenotes/notes/add-remove-tags-9fa798ccad0f5c6a.yaml b/releasenotes/notes/add-remove-tags-9fa798ccad0f5c6a.yaml deleted file mode 100644 index b3713b84af..0000000000 --- a/releasenotes/notes/add-remove-tags-9fa798ccad0f5c6a.yaml +++ /dev/null @@ -1,4 +0,0 @@ ---- -features: - - | - New methods `add_tags_recursive` and `remove_tags_recursive` for adding and removing tags of an experiment data object and recursively all its descendants (if it is composite). diff --git a/releasenotes/notes/block-result-bc065ba7ffb883bc.yaml b/releasenotes/notes/block-result-bc065ba7ffb883bc.yaml deleted file mode 100644 index 1b2991abe4..0000000000 --- a/releasenotes/notes/block-result-bc065ba7ffb883bc.yaml +++ /dev/null @@ -1,32 +0,0 @@ ---- -features: - - | - Added a ``block`` kwarg with default value ``block=True`` to the - :meth:`qiskit_experiments.framework.ExperimentData.analysis_results` - method. If this is True then calling - :meth:`~qiskit_experiments.framework.ExperimentData.analysis_results` - will block to wait for all running analysis callbacks to finish before - returning results. This prevents issues where trying to retrieve analysis - results before analysis was finished would raise an error that the result - could not be found. - - Note that in the case of - :class:`~qiskit-experiments.framework.ParallelExperiment` and - :class:`~qiskit-experiments.framework.BatchExperiment` blocking or - calling ``analysis_results`` on the parent experiment should be performed - before attempting to access results in the component experiment data - containers to ensure the component analysis callbacks - have been initialized. -upgrade: - - | - The :meth:`qiskit_experiments.framework.ExperimentData.analysis_results` - method has been changed to block on analysis callbacks finishing by - default, this means it is no longer necessary to call the - :meth:`~qiskit_experiments.framework.ExperimentData.block_for_results` - method first before accessing analysis results. - - To disable blocking this can be set to run with ``block=False``. - This should be used - :meth:`~qiskit_experiments.framework.ExperimentData.analysis_results` - needs to be called during another analysis callback to prevent that - callback from blocking. diff --git a/releasenotes/notes/cr-ham-tomo-experiment-7cb5cca271c11f16.yaml b/releasenotes/notes/cr-ham-tomo-experiment-7cb5cca271c11f16.yaml deleted file mode 100644 index 1e40f47048..0000000000 --- a/releasenotes/notes/cr-ham-tomo-experiment-7cb5cca271c11f16.yaml +++ /dev/null @@ -1,13 +0,0 @@ ---- -features: - - | - Cross resonance Hamiltonian tomography experiment is added to the library. - This experiment estimates IX, IY, IZ, ZX, ZY, ZZ term coefficient of - the cross resonance Hamiltonian. - - For single tone cross resonance, :py:class:`~qiskit_experiments.library.characterization.\ - cr_hamiltonian.CrossResonanceHamiltonian` class is available. - For echoed cross resonance, :py:class:`~qiskit_experiments.library.characterization.\ - cr_hamiltonian.EchoedCrossResonanceHamiltonian` class is available. - - See experiment class documentation for details. diff --git a/releasenotes/notes/exp-backend-691201fd10046566.yaml b/releasenotes/notes/exp-backend-691201fd10046566.yaml deleted file mode 100644 index f941d03737..0000000000 --- a/releasenotes/notes/exp-backend-691201fd10046566.yaml +++ /dev/null @@ -1,22 +0,0 @@ ---- -features: - - | - Add ``backend`` as an optional ``__init__`` kwarg for all experiments to - alow setting the backend at initialization. The backand can also be set - and retrieved from the experiment object after construction using the - :meth:`~qiskit_experiments.framework.BaseExperiment.backend` - property and setter. - - When using the ``backend`` kwarg of - :meth:`~qiskit_experiments.framework.BaseExperiment.run` to specify - a backend this will temporarily override any currently set backends - for that single execution. -developer: - - | - Added a ``_set_backend`` method to - :class:`~qiskit_experiments.framework.BaseExperiment` that is called - when a backend is set via initalization or the ``backend`` setter. This - can be overridden in experiment subclasses if required. For example this - could be used to extract any needed configuration or properties from the - specified backend, or to update experiment options of configuration based - on the backend. diff --git a/releasenotes/notes/exp-config-82b596887baaafdb.yaml b/releasenotes/notes/exp-config-82b596887baaafdb.yaml deleted file mode 100644 index 4d83ae9e9f..0000000000 --- a/releasenotes/notes/exp-config-82b596887baaafdb.yaml +++ /dev/null @@ -1,10 +0,0 @@ ---- -features: - - | - Add :class:`~qiskit_experiments.framework.ExperimentConfig` dataclass - for storing the configuration of an experiment. This configuration can - be obtained by using the - :meth:`~qiskit_experiments.framework.BaseExperiment.config` property. - Experiments can also be reconstructed from their configuration using - the :meth:`~qiskit_experiments.framework.BaseExperiment.from_config` - class method. diff --git a/releasenotes/notes/fix-expdata-load-b7c6569bfbe67bec.yaml b/releasenotes/notes/fix-expdata-load-b7c6569bfbe67bec.yaml deleted file mode 100644 index 4aa9251974..0000000000 --- a/releasenotes/notes/fix-expdata-load-b7c6569bfbe67bec.yaml +++ /dev/null @@ -1,8 +0,0 @@ ---- -fixes: - - | - Fixes bug where the - :meth:`~qiskit_experiments.framework.ExperimentData.load` method of - :class:`~qiskit_experiments.framework.ExperimentData` would return a - :class:`~qiskit_experiments.database_service.DbExperimentDataV1` object - instead of a :class:`~qiskit_experiments.framework.ExperimentData` object. diff --git a/releasenotes/notes/fix-nested-comp-66a2b8b6e3b404be.yaml b/releasenotes/notes/fix-nested-comp-66a2b8b6e3b404be.yaml new file mode 100644 index 0000000000..8840e94d49 --- /dev/null +++ b/releasenotes/notes/fix-nested-comp-66a2b8b6e3b404be.yaml @@ -0,0 +1,5 @@ +--- +fixes: + - | + Fixes bug in :class:`.CompositeAnalysis` where analysis of nested + composite experiments could raise a RuntimeError. diff --git a/releasenotes/notes/fix-rb-variance-4f53df27ec63afe0.yaml b/releasenotes/notes/fix-rb-variance-4f53df27ec63afe0.yaml deleted file mode 100644 index f7ee37a5e5..0000000000 --- a/releasenotes/notes/fix-rb-variance-4f53df27ec63afe0.yaml +++ /dev/null @@ -1,10 +0,0 @@ ---- -fixes: - - | - Fixes a bug in :class:`~qiskit_experiments.library.StandardRB` and - :class:`~qiskit_experiments.library.InterleavedRB` where the variance in - the estimated error per Clifford did not scale correctly with the number - of sampled RB sequences. - - See issue https://github.com/Qiskit/qiskit-experiments/issues/428 - for details. diff --git a/releasenotes/notes/fix-retrieve-data-c93c8406ff4f2e7e.yaml b/releasenotes/notes/fix-retrieve-data-c93c8406ff4f2e7e.yaml deleted file mode 100644 index e144e391b1..0000000000 --- a/releasenotes/notes/fix-retrieve-data-c93c8406ff4f2e7e.yaml +++ /dev/null @@ -1,6 +0,0 @@ ---- -fixes: - - | - Fixed bug in :class:`~qiskit_experiments.framework.ExperimentData` where - trying to load saved job data from a backend using - ``ExperimentData(backend=backend, job_ids=job_ids)`` resulted in an error. diff --git a/releasenotes/notes/job-splitting-775dc9aed9cf20c2.yaml b/releasenotes/notes/job-splitting-775dc9aed9cf20c2.yaml deleted file mode 100644 index cac2e8eada..0000000000 --- a/releasenotes/notes/job-splitting-775dc9aed9cf20c2.yaml +++ /dev/null @@ -1,9 +0,0 @@ ---- -features: - - | - Adds automatic job splitting to - :class:`~qiskit_experiments.framework.BaseExperiment` for execution of - experiments with a larger number of circuits than can be run in a single - job on the target backend. This enables running large experiments on - legacy and non-IBM backends that don't handle job splitting - automatically. diff --git a/releasenotes/notes/json-encode-5619d65e421428b8.yaml b/releasenotes/notes/json-encode-5619d65e421428b8.yaml deleted file mode 100644 index f430de6afb..0000000000 --- a/releasenotes/notes/json-encode-5619d65e421428b8.yaml +++ /dev/null @@ -1,10 +0,0 @@ ---- -developer: - - | - Adds hooks for serialization and deserialization of custom classes to - the JSON :class:`~qiskit_experiments.database_service.ExperimentEncoder` - and :class:`~qiskit_experiments.database_service.ExperimentDecoder`. - To enable serialization classes must implement a method - ``__json_encode__(self) -> Any`` that returns a JSON serializable object, - and a class method ``_json_decode__(cls, value: Any)__ -> cls`` that can - reconstruct the object from the JSON deserialized value. diff --git a/releasenotes/notes/kids-in-analysis-df7b4dcbeb5b3125.yaml b/releasenotes/notes/kids-in-analysis-df7b4dcbeb5b3125.yaml new file mode 100644 index 0000000000..272ba7b0dc --- /dev/null +++ b/releasenotes/notes/kids-in-analysis-df7b4dcbeb5b3125.yaml @@ -0,0 +1,7 @@ +--- +upgrade: + - | + The :class:`.ParallelExperiment` and :class:`.BatchExperiment` composite experiments + have been changed to no longer return analysis results containing information about + sub-experiments. Instead, use the :meth:`~.ParallelExperiment.child_data` method to + retrieve sub-experiments of a given composite experiment. diff --git a/releasenotes/notes/matplotlib-backend-1e1033ce1815901f.yaml b/releasenotes/notes/matplotlib-backend-1e1033ce1815901f.yaml deleted file mode 100644 index e803f9a8ff..0000000000 --- a/releasenotes/notes/matplotlib-backend-1e1033ce1815901f.yaml +++ /dev/null @@ -1,16 +0,0 @@ ---- -features: - - | - You can now change the default ``matplotlib`` backend used for generated - figures by setting - ``qiskit_experiments.framework.matplotlib.default_figure_canvas`` to the - desired canvas. Note that it has to be a canvas for one of the - `non-interactive backend - `_. - For example, you can set ``default_figure_canvas`` to - :class:`~matplotlib.backends.backend_agg.FigureCanvasAgg` to use the - ``AGG`` backend. -upgrade: - - | - The default ``matplotlib`` backend used for generated figures was changed - from `AGG` to `SVG`. diff --git a/releasenotes/notes/note-for-imperfect-analysis-dc49f2c07de327f4.yaml b/releasenotes/notes/note-for-imperfect-analysis-dc49f2c07de327f4.yaml deleted file mode 100644 index 2264da58b4..0000000000 --- a/releasenotes/notes/note-for-imperfect-analysis-dc49f2c07de327f4.yaml +++ /dev/null @@ -1,10 +0,0 @@ ---- -issues: - - | - Curve analysis may have imperfection in the uncertainty propagation computation. - Fit paramters consist of the nominal part and standard error, however, - the correlation of these paramters are not precisely taken into account. - This sometimes result in the overestimation of the confidence interval of fit curves, - or overestimation of the standard error of some analysis values computed with - multiple fitting parameters. This issue will be solved in the version 0.3. - See [qiskit-experiments/#551](https://github.com/Qiskit/qiskit-experiments/pull/551) for details. diff --git a/releasenotes/notes/qubits-as-a-list-13d9a9c59586d325.yaml b/releasenotes/notes/qubits-as-a-list-13d9a9c59586d325.yaml deleted file mode 100644 index 5ede423abf..0000000000 --- a/releasenotes/notes/qubits-as-a-list-13d9a9c59586d325.yaml +++ /dev/null @@ -1,8 +0,0 @@ ---- -upgrade: - - | - For randomized benchmarking, tomography, and quantum volume experiments the - ``qubits`` parameter must contain the list of physical qubits. The option to specify - ``qubits`` as an integer, as a shortcut to the range of qubits from 0 to `qubits-1`, - has been removed. This is true also for the class - :py:class:`~qiskit_experiments.framework.base_experiment_BaseExperiment`. diff --git a/releasenotes/notes/readout-angle-4d3be1d584ba5ac1.yaml b/releasenotes/notes/readout-angle-4d3be1d584ba5ac1.yaml deleted file mode 100644 index 5e0bd32925..0000000000 --- a/releasenotes/notes/readout-angle-4d3be1d584ba5ac1.yaml +++ /dev/null @@ -1,4 +0,0 @@ ---- -features: - - | - A new readout angle experiment. The experiment computes the average of the angles of the IQ clusters of the ground and excited states. diff --git a/releasenotes/notes/readout-mitigation-experiment-4ea5392ee955a54c.yaml b/releasenotes/notes/readout-mitigation-experiment-4ea5392ee955a54c.yaml new file mode 100644 index 0000000000..d630cd3637 --- /dev/null +++ b/releasenotes/notes/readout-mitigation-experiment-4ea5392ee955a54c.yaml @@ -0,0 +1,5 @@ +--- +features: + - | + Added a new experiment (`qiskit_experiments.library import ReadoutMitigationExperiment`) + for calibrating readout mitigators. diff --git a/releasenotes/notes/remove-units-78db311686213a58.yaml b/releasenotes/notes/remove-units-78db311686213a58.yaml deleted file mode 100644 index eba3737936..0000000000 --- a/releasenotes/notes/remove-units-78db311686213a58.yaml +++ /dev/null @@ -1,4 +0,0 @@ ---- -other: - - | - All the experiments work only with predefined units: Hz for frequency, seconds for delays, dt for pulse widths and durations. Unit arguments were removed from experiment class constructors and options. diff --git a/releasenotes/notes/rng-seed-b99aeaea673afada.yaml b/releasenotes/notes/rng-seed-b99aeaea673afada.yaml deleted file mode 100644 index 410109ad9f..0000000000 --- a/releasenotes/notes/rng-seed-b99aeaea673afada.yaml +++ /dev/null @@ -1,22 +0,0 @@ ---- -upgrade: - - | - The behavior of the ``seed`` initialization kwarg of the - :class:`~qiskit_experiments.library.StandardRB`, - :class:`~qiskit_experiments.library.InterleavedStandardRB`, - :class:`~qiskit_experiments.library.QuantumVolume` experiments has - been modified. - - In the new version the ``seed`` value is used as to initialize a Numpy - random number generator object as ``numpy.random.default_rng(seed=seed)`` - each time the experiments ``circuits`` method is called. This change means - that using a fixed seed value will result in the same circuits being - generated each time an experiment is run, if no other - experiment options are changed. - - To generate different new random circuits each time an experiment is run - the (default) value of ``seed=None`` should be used. To reproduce - equivalent functionality to the previous version behavior of differnet - circuits being generated each time ``run`` is called with a fixed seed - you must now set a new fixed seed value between each call to ``run`` - using ``experiment.set_experiment_options(seed=value)``. diff --git a/releasenotes/notes/save-load-composite-cb882d855dd669be.yaml b/releasenotes/notes/save-load-composite-cb882d855dd669be.yaml deleted file mode 100644 index a52bec5b50..0000000000 --- a/releasenotes/notes/save-load-composite-cb882d855dd669be.yaml +++ /dev/null @@ -1,12 +0,0 @@ -features: - - | - Adds support for saving and loading :class:`~qiskit_experiments.framework.ParallelExperiment` - and :class:`~qiskit_experiments.framework.BatchExperiment` experiment data, - including all their component experiment data and results, to the IBM experiments database service. - - When saving one of these experiments, each component experiment will be saved under a unique experiment ID, - which references the original parent composite experiment, - via the :meth:`~qiskit_Expeirments.framework.ExperimentData.parent_id`. - - Note that changing the share level of the parent composite experiment will also change the share level of all component experiments. - diff --git a/releasenotes/notes/update-curve-analysis-init-guesses-3dc9727ddca08802.yaml b/releasenotes/notes/update-curve-analysis-init-guesses-3dc9727ddca08802.yaml deleted file mode 100644 index 0f09723fa1..0000000000 --- a/releasenotes/notes/update-curve-analysis-init-guesses-3dc9727ddca08802.yaml +++ /dev/null @@ -1,16 +0,0 @@ ---- - -developer: - - | - The handling of user provided initial guesses and fit boundaries in the - :py:class:`~qiskit_experiments.curve_analysis.curve_analysis.CurveAnalysis` has been changed. - A curve analysis subclass doesn't need to worry about user provided options, since - the logic is updated so that propagation of user values in the computation is - implicitly managed by :py:class:`qiskit_experiments.curve_analysis.curve_data.OptionsDict`. - - Note that the method name to provide these initial guesses is also updated from - ``_setup_fitting`` to ``_generate_fit_guesses`` to reflect the new behavior of the method. - Please update your curve analysis subclasses to conform to the new method name, - otherwise the method will be never called. - You can also remove the conventional code to manage user guesses and bounds from the method. - See the documentation of :meth:`_generate_fit_guesses` for more details. diff --git a/releasenotes/notes/update-curve-analysis-pre-process-9cc2aefc449d2bb0.yaml b/releasenotes/notes/update-curve-analysis-pre-process-9cc2aefc449d2bb0.yaml deleted file mode 100644 index c9a175a6af..0000000000 --- a/releasenotes/notes/update-curve-analysis-pre-process-9cc2aefc449d2bb0.yaml +++ /dev/null @@ -1,8 +0,0 @@ ---- -other: - - | - Default data pre-processing for fit data is added to the :py:class:`~qiskit_experiments.\ - curve_analysis.curve_analysis.CurveAnalysis`. The input data to the analysis are - averaged over the same x values and sorted by x values. This processing is necessary to - correctly analyze an experimental data which has multiple same x values, e.g. run experiment - with the previous experimental data. diff --git a/releasenotes/notes/update_expdata-ab3576f3bdd5057a.yaml b/releasenotes/notes/update_expdata-ab3576f3bdd5057a.yaml deleted file mode 100644 index 2ea40587e1..0000000000 --- a/releasenotes/notes/update_expdata-ab3576f3bdd5057a.yaml +++ /dev/null @@ -1,38 +0,0 @@ ---- -upgrade: - - | - The ``CompositeExperimentData`` class has been removed and its - functionality integrated into the - :class:`~qiskit_experiments.framework.ExperimentData` class. - A composite :class:`~qiskit_experiments.framework.ExperimentData` - can now be created by initializing with a list of child - ``ExperimentData`` containers using the ``child_data`` kwarg. - - | - :class:`~qiskit_experiments.framework.ParallelExperiment` and - :class:`~qiskit_experiments.framework.BatchExperiment` now return - a :class:`~qiskit_experiments.framework.ExperimentData` object - which no longer contains a ``component_experiment_data`` method. - This method has been replaced by the - :meth:`~qiskit_experiments.framework.ExperimentData.child_data` - method. -features: - - | - The :class:`~qiskit_experiments.framework.ExperimentData` class - can now store child ``ExperimentData`` containers. - Child data can either be added at initialization using the - ``child_data`` kwarg or added later using the - :meth:`~qiskit_experiments.framework.ExperimentData.add_child_data` - method. Child ``ExperimentData`` can be accessed using the - :meth:`~qiskit_experiments.framework.ExperimentData.child_data` - method. - - | - Added a :meth:`~qiskit_experiments.framework.ExperimentData.copy` - method to :class:`~qiskit_experiments.framework.ExperimentData` which - allows making a copy of an experiment data container with a new - experiment ID, new result IDs, and new figure names, generated for - the copy. - - This method has a kwarg option ``copy_results`` that can be set to - ``False`` to only copy the experiment - :meth:`~qiskit_experiments.framework.ExperimentData.data` and - metadata, but not the analysis results and figures. diff --git a/releasenotes/notes/upgrade-data-processor-30204e10e1958c30.yaml b/releasenotes/notes/upgrade-data-processor-30204e10e1958c30.yaml deleted file mode 100644 index 94de41cc90..0000000000 --- a/releasenotes/notes/upgrade-data-processor-30204e10e1958c30.yaml +++ /dev/null @@ -1,6 +0,0 @@ ---- -developer: - - | - Data format used in the :py:class:`~qiskit_experiments.data_processing.data_processor.\ - DataProcessor` is upgraded from `Tuple[Any, Any]` to `np.ndarray`. Uncertainty propagation - computation is offloaded to uncertainties package. See module documentation for details. diff --git a/releasenotes/notes/upgrade-probability-computation-6c7539a52e3d4484.yaml b/releasenotes/notes/upgrade-probability-computation-6c7539a52e3d4484.yaml deleted file mode 100644 index 01623961b7..0000000000 --- a/releasenotes/notes/upgrade-probability-computation-6c7539a52e3d4484.yaml +++ /dev/null @@ -1,14 +0,0 @@ ---- -fixes: - - | - Update the :class:`~qiskit_experiments.data_processing.Probability` - data processing node to compute the estimated mean and standard deviation - of a measurement outcome probability using a Bayesian update of a - a Beta distribution prior given the observed measurement outcomes. - - The default uninformative prior assumes ignorance about the probability - to be estimated and will prevent the estimated mean from being exactly - 0 or 1, and prevent the estimated standard deviation from being 0, which - could cause issues with computing weights during curve fitting. A custom - prior can also be provided if prior information about the probability is - know. diff --git a/requirements-dev.txt b/requirements-dev.txt index 234b3e5c6f..ed205638fe 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -11,6 +11,5 @@ reno>=3.4.0 sphinx-panels nbsphinx arxiv - ddt~=1.4.2 -qiskit-aer>=0.8.0 +qiskit-aer>=0.10.0 diff --git a/test/curve_analysis/test_curve_fit.py b/test/curve_analysis/test_curve_fit.py index 70ad3fb8b4..c151eab275 100644 --- a/test/curve_analysis/test_curve_fit.py +++ b/test/curve_analysis/test_curve_fit.py @@ -177,21 +177,9 @@ def test_cannot_create_invalid_fixed_parameter(self): fixed_params=["not_existing_parameter"], # this parameter is not defined ) - def test_arg_parse_and_get_option(self): - """Test if option parsing works correctly.""" - user_option = {"x_key": "test_value", "test_key1": "value1", "test_key2": "value2"} - - # argument not defined in default option should be returned as extra option - extra_option = self.analysis._arg_parse(**user_option) - ref_option = {"test_key1": "value1", "test_key2": "value2"} - self.assertDictEqual(extra_option, ref_option) - - # default option value is stored as class variable - self.assertEqual(self.analysis._get_option("x_key"), "test_value") - def test_data_extraction(self): """Test data extraction method.""" - self.analysis._arg_parse(x_key="xval") + self.analysis.set_options(x_key="xval") # data to analyze test_data0 = simulate_output_data( @@ -268,7 +256,7 @@ def test_get_subset(self): def _processor(datum): return datum["data"], datum["data"] * 2 - self.analysis._arg_parse(x_key="xval") + self.analysis.set_options(x_key="xval") self.analysis._extract_curves(expdata, data_processor=_processor) filt_data = self.analysis._data(series_name="curve1") diff --git a/test/randomized_benchmarking/rb_interleaved_1qubit_output_analysis.json b/test/randomized_benchmarking/rb_interleaved_1qubit_output_analysis.json index a95abf4238..04c3a29e3b 100644 --- a/test/randomized_benchmarking/rb_interleaved_1qubit_output_analysis.json +++ b/test/randomized_benchmarking/rb_interleaved_1qubit_output_analysis.json @@ -1 +1 @@ -[{"name": "@Parameters_InterleavedRBAnalysis", "value": {"__type__": "object", "__value__": {"class": {"__type__": "type", "__value__": {"name": "FitVal", "module": "qiskit_experiments.database_service.db_fitval", "version": "0.2.0.dev0+3563f43"}}, "settings": {"value": {"__type__": "ndarray", "__value__": {"__type__": "b64encoded", "__value__": {"encoded": "eJyb7BfqGxDJyFDGUK2eklqcXKRupaBuk2ahrqOgnpZfVFKUmBefX5SSChJ3S8wpTgWKF2ckFqQC+RomOpo6CrUKFACuY5JTr+s4mthpnnrqLLQ8047hgRbP8840uxm6l+w7k0/ZAwCeFyj5", "compressed": true}}}, "stderr": {"__type__": "ndarray", "__value__": {"__type__": "b64encoded", "__value__": {"encoded": "eJyb7BfqGxDJyFDGUK2eklqcXKRupaBuk2ahrqOgnpZfVFKUmBefX5SSChJ3S8wpTgWKF2ckFqQC+RomOpo6CrUKFACuryf9mxPsvB33Wp7+8khV0pEBCiyDC3a95F9iDwCVWibA", "compressed": true}}}, "unit": null}, "version": 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181]], [[[1], "x"], [50, 181]], [[[0], "measure"], [1, 181]], [[[1], "measure"], [1, 181]]], "experiment_type": "InterleavedRB", "group": "Clifford", "interleaved": true, "physical_qubits": [0, 1], "xval": 181}, "shots": 1024, "meas_level": 2}]] \ No newline at end of file diff --git a/test/test_composite.py b/test/test_composite.py index c894a9fbd6..09a7784829 100644 --- a/test/test_composite.py +++ b/test/test_composite.py @@ -161,6 +161,20 @@ def test_composite_copy(self): self.check_attributes(new_instance) self.assertEqual(new_instance.parent_id, None) + def test_nested_composite(self): + """ + Test nested parallel experiments. + """ + exp1 = FakeExperiment([0, 2]) + exp2 = FakeExperiment([1, 3]) + exp3 = ParallelExperiment([exp1, exp2]) + exp4 = BatchExperiment([exp3, exp1]) + exp5 = ParallelExperiment([exp4, FakeExperiment([4])]) + nested_exp = BatchExperiment([exp5, exp3]) + expdata = nested_exp.run(FakeBackend()).block_for_results() + status = expdata.status() + self.assertEqual(status, "DONE") + def test_analysis_replace_results_true(self): """ Test replace results when analyzing composite experiment data @@ -216,7 +230,6 @@ def test_composite_tags(self): expdata.tags = ["a", "c", "a"] data1.tags = ["b"] - print(expdata.tags) self.assertEqual(sorted(expdata.tags), ["a", "c"]) self.assertEqual(sorted(data1.tags), ["b"]) self.assertEqual(sorted(data2.tags), []) diff --git a/test/test_mitigation.py b/test/test_mitigation.py new file mode 100644 index 0000000000..92acaa5a7b --- /dev/null +++ b/test/test_mitigation.py @@ -0,0 +1,133 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2022. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. + +""" +A Tester for the Readout Mitigation experiment +""" + + +import unittest +from test.base import QiskitExperimentsTestCase +import numpy as np +from qiskit.quantum_info.operators.predicates import matrix_equal +from qiskit_experiments.library import ReadoutMitigationExperiment +from qiskit_experiments.framework import ExperimentData + + +class TestMitigation(QiskitExperimentsTestCase): + """Test ReadoutMitigationExperiment""" + + def test_local_analysis(self): + """Tests local mitigator generation from experimental data""" + qubits = [0, 1, 2] + run_data = [ + { + "counts": {"000": 986, "010": 10, "100": 16, "001": 12}, + "metadata": {"label": "000"}, + "shots": 1024, + }, + { + "counts": {"111": 930, "110": 39, "011": 24, "101": 29, "010": 1, "100": 1}, + "metadata": {"label": "111"}, + "shots": 1024, + }, + ] + expected_assignment_matrices = [ + np.array([[0.98828125, 0.04003906], [0.01171875, 0.95996094]]), + np.array([[0.99023438, 0.02929688], [0.00976562, 0.97070312]]), + np.array([[0.984375, 0.02441406], [0.015625, 0.97558594]]), + ] + run_meta = {"physical_qubits": qubits} + expdata = ExperimentData() + expdata.add_data(run_data) + expdata._metadata = run_meta + exp = ReadoutMitigationExperiment(qubits) + result = exp.analysis.run(expdata) + mitigator = result.analysis_results(0).value + + self.assertEqual(len(qubits), mitigator._num_qubits) + self.assertEqual(qubits, mitigator._qubits) + self.assertTrue(matrix_equal(expected_assignment_matrices, mitigator._assignment_mats)) + + def test_correlated_analysis(self): + """Tests correlated mitigator generation from experimental data""" + qubits = [0, 2, 3] + run_data = [ + { + "counts": {"000": 989, "010": 12, "100": 7, "001": 15, "101": 1}, + "metadata": {"label": "000"}, + "shots": 1024, + }, + { + "counts": {"001": 971, "101": 15, "000": 36, "011": 2}, + "metadata": {"label": "001"}, + "shots": 1024, + }, + { + "counts": {"000": 30, "010": 965, "110": 15, "011": 11, "001": 2, "100": 1}, + "metadata": {"label": "010"}, + "shots": 1024, + }, + { + "counts": {"011": 955, "111": 15, "010": 26, "001": 27, "110": 1}, + "metadata": {"label": "011"}, + "shots": 1024, + }, + { + "counts": {"100": 983, "101": 8, "110": 13, "000": 20}, + "metadata": {"label": "100"}, + "shots": 1024, + }, + { + "counts": {"101": 947, "001": 34, "100": 32, "111": 11}, + "metadata": {"label": "101"}, + "shots": 1024, + }, + { + "counts": {"100": 26, "110": 965, "010": 21, "111": 11, "000": 1}, + "metadata": {"label": "110"}, + "shots": 1024, + }, + { + "counts": {"111": 938, "011": 23, "110": 35, "101": 27, "100": 1}, + "metadata": {"label": "111"}, + "shots": 1024, + }, + ] + + expected_assignment_matrix = np.array( + [ + [0.96582031, 0.03515625, 0.02929688, 0.0, 0.01953125, 0.0, 0.00097656, 0.0], + [0.01464844, 0.94824219, 0.00195312, 0.02636719, 0.0, 0.03320312, 0.0, 0.0], + [0.01171875, 0.0, 0.94238281, 0.02539062, 0.0, 0.0, 0.02050781, 0.0], + [0.0, 0.00195312, 0.01074219, 0.93261719, 0.0, 0.0, 0.0, 0.02246094], + [0.00683594, 0.0, 0.00097656, 0.0, 0.95996094, 0.03125, 0.02539062, 0.00097656], + [0.00097656, 0.01464844, 0.0, 0.0, 0.0078125, 0.92480469, 0.0, 0.02636719], + [0.0, 0.0, 0.01464844, 0.00097656, 0.01269531, 0.0, 0.94238281, 0.03417969], + [0.0, 0.0, 0.0, 0.01464844, 0.0, 0.01074219, 0.01074219, 0.91601562], + ] + ) + run_meta = {"physical_qubits": qubits} + expdata = ExperimentData() + expdata.add_data(run_data) + expdata._metadata = run_meta + exp = ReadoutMitigationExperiment(qubits, method="correlated") + result = exp.analysis.run(expdata) + mitigator = result.analysis_results(0).value + + self.assertEqual(len(qubits), mitigator._num_qubits) + self.assertEqual(qubits, mitigator._qubits) + self.assertTrue(matrix_equal(expected_assignment_matrix, mitigator.assignment_matrix())) + + +if __name__ == "__main__": + unittest.main()