From 08c7af74b049e56e66ce18226fa84fb4d0ff1472 Mon Sep 17 00:00:00 2001 From: Cameron Booker Date: Mon, 5 Aug 2024 10:25:39 +0100 Subject: [PATCH] tie up lose ends --- .../examples/qcvv/qcvv_css.ipynb | 45 ++++++++++------ .../examples/qcvv/qcvv_irb_css.ipynb | 39 +++++++------- .../supermarq/qcvv/__init__.py | 5 ++ .../supermarq/qcvv/base_experiment.py | 28 +++++----- .../supermarq/qcvv/base_experiment_test.py | 22 +++++--- .../supermarq}/qcvv/irb.py | 54 +++++++++++-------- .../supermarq}/qcvv/irb_test.py | 15 ++++-- 7 files changed, 124 insertions(+), 84 deletions(-) rename {cirq-superstaq/cirq_superstaq => supermarq-benchmarks/supermarq}/qcvv/irb.py (86%) rename {cirq-superstaq/cirq_superstaq => supermarq-benchmarks/supermarq}/qcvv/irb_test.py (94%) diff --git a/supermarq-benchmarks/examples/qcvv/qcvv_css.ipynb b/supermarq-benchmarks/examples/qcvv/qcvv_css.ipynb index 5b302d37e..e53561e37 100644 --- a/supermarq-benchmarks/examples/qcvv/qcvv_css.ipynb +++ b/supermarq-benchmarks/examples/qcvv/qcvv_css.ipynb @@ -29,9 +29,18 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -40,11 +49,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "from supermarq.qcvv.base_experiment import BenchmarkingExperiment, Sample, QCVVResults\n", + "from supermarq.qcvv.base_experiment import BenchmarkingExperiment, Sample, BenchmarkingResults\n", "from dataclasses import dataclass\n", "from collections.abc import Sequence\n", "from typing import Iterable\n", @@ -60,10 +69,12 @@ "\n", "\n", "@dataclass(frozen=True)\n", - "class NaiveExperimentResult(QCVVResults):\n", + "class NaiveExperimentResult(BenchmarkingResults):\n", " gate_fidelity: float\n", " gate_error: float\n", "\n", + " experiment_name = \"NaiveExperiment\"\n", + "\n", "\n", "class NaiveExperiment(BenchmarkingExperiment):\n", " def __init__(self):\n", @@ -137,13 +148,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "01239511c7da4cdba676345288a220b5", + "model_id": "2b929a60c931400bad916bd49f04cfa4", "version_major": 2, "version_minor": 0 }, @@ -157,7 +168,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "55291d63fe3343e0b345fddcd7c341c4", + "model_id": "323eb643e2ea42c19f600e1032df0bb8", "version_major": 2, "version_minor": 0 }, @@ -178,19 +189,19 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "NaiveExperimentResult(experiment_name='Naive Experiment', target='Local simulator', total_circuits=30, gate_fidelity=0.9862489774018681, gate_error=0.013751022598131879)\n" + "NaiveExperimentResult(experiment_name='Naive Experiment', target='Local simulator', total_circuits=30, gate_fidelity=0.985976601929455, gate_error=0.014023398070544979)\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -214,14 +225,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.01031326694859891\n" + "0.010517548552908734\n" ] } ], @@ -249,13 +260,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0adc5543d95b43eda6eed66876983e8e", + "model_id": "6d9aa2b922b5447489b23be4cd881556", "version_major": 2, "version_minor": 0 }, @@ -275,13 +286,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f8275dbc8a124d7aa5304baf65bd290e", + "model_id": "00b371e0af09461db1dbb00cc66578f9", "version_major": 2, "version_minor": 0 }, diff --git a/supermarq-benchmarks/examples/qcvv/qcvv_irb_css.ipynb b/supermarq-benchmarks/examples/qcvv/qcvv_irb_css.ipynb index 674786cbb..b36fad3d3 100644 --- a/supermarq-benchmarks/examples/qcvv/qcvv_irb_css.ipynb +++ b/supermarq-benchmarks/examples/qcvv/qcvv_irb_css.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -49,13 +49,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "253cdd27dffd41e5ad90044691819d29", + "model_id": "ec38b4dfc33a45c9b55ae2b910ddeffd", "version_major": 2, "version_minor": 0 }, @@ -69,7 +69,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3c600ff3289f4d7c91efc43656e458d1", + "model_id": "537c739c0d674193830576aeb1cdafa9", "version_major": 2, "version_minor": 0 }, @@ -82,30 +82,27 @@ } ], "source": [ - "from cirq_superstaq.qcvv import IRB\n", + "from supermarq.qcvv import IRB\n", "\n", "experiment = IRB()\n", - "experiment.run(100, [1, 10, 25, 50, 75, 100], target=target)" + "experiment.run_with_simulator(100, [1, 10, 25, 50, 75, 100], target=target)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "IRBResults(rb_layer_fidelity=0.9933663340897436, rb_layer_fidelity_std=5.841769504420766e-05, irb_layer_fidelity=0.9866314142548303, irb_layer_fidelity_std=0.00016249287958307833, average_interleaved_gate_error=0.003389947697938045, average_interleaved_gate_error_std=5.928307527842737e-07)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "IRBResults(target=['Local simulator'], total_circuits=1200, experiment_name='IRB', rb_layer_fidelity=0.9933653437478775, rb_layer_fidelity_std=5.652788049644502e-05, irb_layer_fidelity=0.9868154592201566, irb_layer_fidelity_std=0.00018582763901399624, average_interleaved_gate_error=0.003296815501439232, average_interleaved_gate_error_std=6.485519397757938e-07)\n" + ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -115,12 +112,14 @@ } ], "source": [ - "experiment.analyse_results()" + "if experiment.collect_data():\n", + " results = experiment.analyse_results(plot_results=True)\n", + " print(results)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -128,7 +127,7 @@ "output_type": "stream", "text": [ "Expected gate error: 0.0033375209644599946\n", - "Measured gate error: 0.003389947697938045\n" + "Measured gate error: 0.003296815501439232\n" ] } ], diff --git a/supermarq-benchmarks/supermarq/qcvv/__init__.py b/supermarq-benchmarks/supermarq/qcvv/__init__.py index 4c0a5dc54..9887ede35 100644 --- a/supermarq-benchmarks/supermarq/qcvv/__init__.py +++ b/supermarq-benchmarks/supermarq/qcvv/__init__.py @@ -1 +1,6 @@ """A toolkit of QCVV routines.""" + +from .base_experiment import BenchmarkingExperiment, BenchmarkingResults, Sample +from .irb import IRB, IRBResults + +__all__ = ["BenchmarkingExperiment", "BenchmarkingResults", "Sample", "IRB", "IRBResults"] diff --git a/supermarq-benchmarks/supermarq/qcvv/base_experiment.py b/supermarq-benchmarks/supermarq/qcvv/base_experiment.py index 5e0510169..4dda61453 100644 --- a/supermarq-benchmarks/supermarq/qcvv/base_experiment.py +++ b/supermarq-benchmarks/supermarq/qcvv/base_experiment.py @@ -62,17 +62,18 @@ def target(self) -> str: @dataclass(frozen=True) -class QCVVResults: +class BenchmarkingResults(ABC): """A dataclass for storing the results of the experiment. Requires subclassing for each new experiment type""" - experiment_name: str - """The name of the experiment.""" target: str """The target device that was used.""" total_circuits: int """The total number of circuits used in the experiment.""" + experiment_name: str = field(init=False) + """The name of the experiment""" + class BenchmarkingExperiment(ABC): """Base class for gate benchmarking experiments. @@ -106,7 +107,7 @@ class BenchmarkingExperiment(ABC): results = experiment.analyse_results(<>) #. The final results of the experiment will be stored in the :code:`results` attribute as a - :class:`QCVVResults` of values, while all the data from the experiment will be + :class:`BenchmarkingResults` of values, while all the data from the experiment will be stored in the :code:`raw_data` attribute as a :class:`~pandas.DataFrame`. Some experiments may include additional data attributes for data generated during the analysis. @@ -130,14 +131,13 @@ class BenchmarkingExperiment(ABC): into a :class:`pandas.DataFrame`. #. :meth:`analyse_results`: Analyse the data in the :attr:`raw_data` dataframe and return a - :class:`QCVVResults` object containing the results of the experiment. + :class:`BenchmarkingResults` object containing the results of the experiment. #. :meth:`plot_results`: Produce any relevant plots that are useful for understanding the results of the experiment. - Additionally the :class:`QCVVResults` dataclass needs subclassing to hold the specific results - of the new experiment. - + Additionally the :class:`BenchmarkingResults` dataclass needs subclassing to hold the specific + results of the new experiment. """ def __init__( @@ -156,7 +156,7 @@ def __init__( self._raw_data: pd.DataFrame | None = None "The data generated during the experiment" - self._results: QCVVResults | None = None + self._results: BenchmarkingResults | None = None """The attribute to store the results in.""" self._samples: Sequence[Sample] | None = None @@ -166,7 +166,7 @@ def __init__( """The superstaq service for submitting jobs.""" @property - def results(self) -> QCVVResults: + def results(self) -> BenchmarkingResults: """The results from the most recently run experiment. Raises: @@ -218,8 +218,10 @@ def sample_targets(self) -> list[str]: def _validate_circuits(self) -> None: """Checks that all circuits contain a terminal measurement of all qubits.""" for sample in self.samples: + if not sample.circuit.has_measurements(): + raise ValueError("QCVV experiment circuits must contain measurements.") if not sample.circuit.are_all_measurements_terminal(): - raise ValueError("QCVV experiment circuits can only contain terminal measurements") + raise ValueError("QCVV experiment circuits can only contain terminal measurements.") if not sorted(sample.circuit[-1].qubits) == sorted(self.qubits): raise ValueError( "The terminal measurement in QCVV experiment circuits must measure all qubits." @@ -432,7 +434,7 @@ def build_circuits( cycle_depths: An iterable of the different cycle depths to use during the experiment. Returns: - The list of circuit objects + The list of experiment samples. """ @abstractmethod @@ -452,7 +454,7 @@ def plot_results(self) -> None: """Plot the results of the experiment""" @abstractmethod - def analyse_results(self, plot_results: bool = True) -> QCVVResults: + def analyse_results(self, plot_results: bool = True) -> BenchmarkingResults: """Perform the experiment analysis and store the results in the `results` attribute Args: diff --git a/supermarq-benchmarks/supermarq/qcvv/base_experiment_test.py b/supermarq-benchmarks/supermarq/qcvv/base_experiment_test.py index 8022e5b95..e274d5280 100644 --- a/supermarq-benchmarks/supermarq/qcvv/base_experiment_test.py +++ b/supermarq-benchmarks/supermarq/qcvv/base_experiment_test.py @@ -27,7 +27,7 @@ import pandas as pd import pytest -from supermarq.qcvv.base_experiment import BenchmarkingExperiment, QCVVResults, Sample +from supermarq.qcvv.base_experiment import BenchmarkingExperiment, BenchmarkingResults, Sample @pytest.fixture(scope="session", autouse=True) @@ -50,16 +50,18 @@ def sample_circuits() -> list[Sample]: circuit=cirq.Circuit(cirq.CZ(*qubits), cirq.CZ(*qubits), cirq.measure(*qubits)), data={"circuit": 1}, ), - Sample(circuit=cirq.Circuit(cirq.CX(*qubits)), data={"circuit": 2}), + Sample(circuit=cirq.Circuit(cirq.CX(*qubits), cirq.measure(*qubits)), data={"circuit": 2}), ] @dataclass(frozen=True) -class ExampleResults(QCVVResults): +class ExampleResults(BenchmarkingResults): """NamedTuple instance to use for testing""" example: float + experiment_name = "Example results" + def test_sample_target_property() -> None: sample = Sample(circuit=MagicMock(), data={}) @@ -80,9 +82,7 @@ def test_benchmarking_experiment_init(abc_experiment: BenchmarkingExperiment) -> assert abc_experiment._samples is None abc_experiment._raw_data = pd.DataFrame([{"Example": 0.1}]) - abc_experiment._results = ExampleResults( - experiment_name="Example", target="Some target", total_circuits=2, example=5.0 - ) + abc_experiment._results = ExampleResults(target="Some target", total_circuits=2, example=5.0) pd.testing.assert_frame_equal(abc_experiment.raw_data, abc_experiment._raw_data) assert abc_experiment.results == abc_experiment._results @@ -325,7 +325,7 @@ def test_validate_circuits( # Add a gate so not all measurements are terminal abc_experiment._samples[0].circuit += cirq.X(abc_experiment.qubits[0]) with pytest.raises( - ValueError, match="QCVV experiment circuits can only contain terminal measurements" + ValueError, match="QCVV experiment circuits can only contain terminal measurements." ): abc_experiment._validate_circuits() @@ -339,6 +339,14 @@ def test_validate_circuits( ): abc_experiment._validate_circuits() + # Remove all measurements + abc_experiment._samples[0].circuit = abc_experiment._samples[0].circuit[:-2] + with pytest.raises( + ValueError, + match="QCVV experiment circuits must contain measurements.", + ): + abc_experiment._validate_circuits() + def test_process_device_counts(abc_experiment: BenchmarkingExperiment) -> None: counts = { diff --git a/cirq-superstaq/cirq_superstaq/qcvv/irb.py b/supermarq-benchmarks/supermarq/qcvv/irb.py similarity index 86% rename from cirq-superstaq/cirq_superstaq/qcvv/irb.py rename to supermarq-benchmarks/supermarq/qcvv/irb.py index d17dfbc04..868f7de72 100644 --- a/cirq-superstaq/cirq_superstaq/qcvv/irb.py +++ b/supermarq-benchmarks/supermarq/qcvv/irb.py @@ -17,7 +17,7 @@ import random from collections.abc import Iterable, Sequence -from typing import NamedTuple, cast +from dataclasses import dataclass import cirq import numpy as np @@ -26,10 +26,11 @@ from scipy.stats import linregress from tqdm.contrib.itertools import product -from cirq_superstaq.qcvv.base_experiment import BenchmarkingExperiment, Sample +from supermarq.qcvv.base_experiment import BenchmarkingExperiment, Sample, BenchmarkingResults -class IRBResults(NamedTuple): +@dataclass(frozen=True) +class IRBResults(BenchmarkingResults): """Data structure for the IRB experiment results.""" rb_layer_fidelity: float @@ -45,6 +46,8 @@ class IRBResults(NamedTuple): average_interleaved_gate_error_std: float """Standard deviation of the estimate for the interleaving gate error.""" + experiment_name = "IRB" + class IRB(BenchmarkingExperiment): r"""Interleaved random benchmarking (IRB) experiment. @@ -72,7 +75,7 @@ class IRB(BenchmarkingExperiment): .. math:: - e_{\mathcal{C}^*} = 1 - \frac{\tilde{\alpha}}{\alpha} + e_{\mathcal{C}^*} = \frac{1}{2} \left(1 - \frac{\tilde{\alpha}}{\alpha}\right) """ @@ -94,11 +97,6 @@ def __init__( self.interleaved_gate = interleaved_gate """The gate being interleaved""" - @property - def results(self) -> IRBResults: - """The results from the most recently run experiment""" - return cast("IRBResults", super().results) - @staticmethod def _reduce_clifford_seq( gate_seq: list[cirq.ops.SingleQubitCliffordGate], @@ -159,20 +157,18 @@ def _invert_clifford_circuit(self, circuit: cirq.Circuit) -> cirq.Circuit: clifford_gates.append(inv_element) return cirq.Circuit(*[gate(*self.qubits) for gate in clifford_gates]) # type: ignore[misc] - def build_circuits(self, num_circuits: int, layers: Iterable[int]) -> Sequence[Sample]: + def build_circuits(self, num_circuits: int, cycle_depths: Iterable[int]) -> Sequence[Sample]: """Build a list of randomised circuits required for the IRB experiment. - These circuits do not include the interleaving gate or the final inverse - gate, instead these are added in the :meth:`sample_circuit` function. Args: num_circuits: Number of circuits to generate. - layers: TODO + cycle_depths: An iterable of the different cycle depths to use during the experiment. Returns: - TODO + The list of experiment samples. """ samples = [] - for _, depth in product(range(num_circuits), layers, desc="Building circuits"): + for _, depth in product(range(num_circuits), cycle_depths, desc="Building circuits"): base_circuit = cirq.Circuit( *[self._random_single_qubit_clifford()(*self.qubits) for _ in range(depth)] ) @@ -182,7 +178,7 @@ def build_circuits(self, num_circuits: int, layers: Iterable[int]) -> Sequence[S ) samples += [ Sample( - circuit=rb_circuit, + circuit=rb_circuit + cirq.measure(sorted(rb_circuit.all_qubits())), data={ "num_cycles": depth, "circuit_depth": len(rb_circuit), @@ -190,7 +186,7 @@ def build_circuits(self, num_circuits: int, layers: Iterable[int]) -> Sequence[S }, ), Sample( - circuit=irb_circuit, + circuit=irb_circuit + cirq.measure(sorted(irb_circuit.all_qubits())), data={ "num_cycles": depth, "circuit_depth": len(irb_circuit), @@ -201,14 +197,19 @@ def build_circuits(self, num_circuits: int, layers: Iterable[int]) -> Sequence[S return samples - def process_probabilities(self) -> None: + def process_probabilities(self, samples: Sequence[Sample]) -> pd.DataFrame: """Processes the probabilities generated by sampling the circuits into the data structures needed for analyzing the results. + + Args: + samples: The list of samples to process the results from. + + Returns: + A data frame of the full results needed to analyse the experiment. """ - super().process_probabilities() records = [] - for sample in self.samples: + for sample in samples: records.append( { "clifford_depth": sample.data["num_cycles"], @@ -218,7 +219,7 @@ def process_probabilities(self) -> None: } ) - self._raw_data = pd.DataFrame(records) + return pd.DataFrame(records) def plot_results(self) -> None: """Plot the exponential decay of the circuit fidelity with @@ -237,7 +238,14 @@ def plot_results(self) -> None: ax.set_title(r"Exponential decay of circuit fidelity", fontsize=15) def analyse_results(self, plot_results: bool = True) -> IRBResults: - """Analyse the experiment results and estimate the interleaved gate error.""" + """Analyse the experiment results and estimate the interleaved gate error. + + Args: + plot_results: Whether to generate plots of the results. Defaults to False. + + Returns: + A named tuple of the final results from the experiment. + """ self.raw_data["fidelity"] = 2 * self.raw_data["0"] - 1 self.raw_data["log_fidelity"] = np.log(self.raw_data["fidelity"]) @@ -264,6 +272,8 @@ def analyse_results(self, plot_results: bool = True) -> IRBResults: ) self._results = IRBResults( + target=self.sample_targets, + total_circuits=len(self.samples), rb_layer_fidelity=rb_layer_fidelity, rb_layer_fidelity_std=rb_layer_fidelity_std, irb_layer_fidelity=irb_layer_fidelity, diff --git a/cirq-superstaq/cirq_superstaq/qcvv/irb_test.py b/supermarq-benchmarks/supermarq/qcvv/irb_test.py similarity index 94% rename from cirq-superstaq/cirq_superstaq/qcvv/irb_test.py rename to supermarq-benchmarks/supermarq/qcvv/irb_test.py index 00145c7e4..cf43e5bbc 100644 --- a/cirq-superstaq/cirq_superstaq/qcvv/irb_test.py +++ b/supermarq-benchmarks/supermarq/qcvv/irb_test.py @@ -11,6 +11,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # pylint: disable=missing-function-docstring +# pylint: disable=missing-return-doc # mypy: disable-error-code=method-assign from __future__ import annotations @@ -21,8 +22,8 @@ import pandas as pd import pytest -from cirq_superstaq.qcvv.base_experiment import Sample -from cirq_superstaq.qcvv.irb import IRB +from supermarq.qcvv.base_experiment import Sample +from supermarq.qcvv.irb import IRB @pytest.fixture(scope="session", autouse=True) @@ -90,9 +91,8 @@ def test_irb_process_probabilities(irb_experiment: IRB) -> None: ) ] samples[0].probabilities = {"00": 0.1, "01": 0.2, "10": 0.3, "11": 0.4} - irb_experiment._samples = samples - irb_experiment.process_probabilities() + data = irb_experiment.process_probabilities(samples) expected_data = pd.DataFrame( [ @@ -108,7 +108,7 @@ def test_irb_process_probabilities(irb_experiment: IRB) -> None: ] ) - pd.testing.assert_frame_equal(expected_data, irb_experiment.raw_data) + pd.testing.assert_frame_equal(expected_data, data) def test_irb_build_circuit(irb_experiment: IRB) -> None: @@ -131,6 +131,7 @@ def test_irb_build_circuit(irb_experiment: IRB) -> None: cirq.ops.SingleQubitCliffordGate.Z(*irb_experiment.qubits), cirq.ops.SingleQubitCliffordGate.Z(*irb_experiment.qubits), cirq.ops.SingleQubitCliffordGate.Z(*irb_experiment.qubits), + cirq.measure(irb_experiment.qubits), ] ), data={ @@ -149,6 +150,7 @@ def test_irb_build_circuit(irb_experiment: IRB) -> None: cirq.ops.SingleQubitCliffordGate.Z(*irb_experiment.qubits), cirq.ops.SingleQubitCliffordGate.Z(*irb_experiment.qubits), cirq.ops.SingleQubitCliffordGate.I(*irb_experiment.qubits), + cirq.measure(irb_experiment.qubits), ] ), data={ @@ -164,6 +166,7 @@ def test_irb_build_circuit(irb_experiment: IRB) -> None: cirq.ops.SingleQubitCliffordGate.X(*irb_experiment.qubits), cirq.ops.SingleQubitCliffordGate.X(*irb_experiment.qubits), cirq.ops.SingleQubitCliffordGate.X(*irb_experiment.qubits), + cirq.measure(irb_experiment.qubits), ] ), data={ @@ -182,6 +185,7 @@ def test_irb_build_circuit(irb_experiment: IRB) -> None: cirq.ops.SingleQubitCliffordGate.X(*irb_experiment.qubits), cirq.ops.SingleQubitCliffordGate.Z(*irb_experiment.qubits), cirq.ops.SingleQubitCliffordGate.Y(*irb_experiment.qubits), + cirq.measure(irb_experiment.qubits), ] ), data={ @@ -205,6 +209,7 @@ def test_irb_build_circuit(irb_experiment: IRB) -> None: def test_analyse_results(irb_experiment: IRB) -> None: + irb_experiment._samples = MagicMock() irb_experiment._raw_data = pd.DataFrame( [ {