def __init__(
self,
interleaved_element: Union[QuantumCircuit, Instruction, Clifford],
qubits: Sequence[int],
lengths: Iterable[int],
backend: Optional[Backend] = None,
num_samples: int = 3,
seed: Optional[Union[int, SeedSequence, BitGenerator,
Generator]] = None,
full_sampling: bool = False,
):
"""Initialize an interleaved randomized benchmarking experiment.
Args:
interleaved_element: The element to interleave,
given either as a group element or as an instruction/circuit
qubits: list of physical qubits for the experiment.
lengths: A list of RB sequences lengths.
backend: The backend to run the experiment on.
num_samples: Number of samples to generate for each
sequence length
seed: Optional, seed used to initialize ``numpy.random.default_rng``.
when generating circuits. The ``default_rng`` will be initialized
with this seed value everytime :meth:`circuits` is called.
full_sampling: If True all Cliffords are independently sampled for
all lengths. If False for sample of lengths longer
sequences are constructed by appending additional
Clifford samples to shorter sequences.
"""
self._set_interleaved_element(interleaved_element)
super().__init__(
qubits,
lengths,
backend=backend,
num_samples=num_samples,
seed=seed,
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 _default_options(cls):
"""Return the default analysis options."""
default_options = super()._default_options()
default_options.data_processor = dp.DataProcessor(
input_key="counts",
data_actions=[dp.Probability("1"), dp.BasisExpectationValue()],
)
default_options.curve_plotter = "mpl_multiv_canvas"
default_options.xlabel = "Flat top width"
default_options.ylabel = "<X(t)>,<Y(t)>,<Z(t)>"
default_options.xval_unit = "s"
default_options.style = curve.visualization.PlotterStyle(
figsize=(8, 10),
legend_loc="lower right",
fit_report_rpos=(0.28, -0.10),
)
default_options.ylim = (-1, 1)
return default_options
def __init__(
self,
qubits: Sequence[int],
lengths: Iterable[int],
backend: Optional[Backend] = None,
num_samples: int = 3,
seed: Optional[Union[int, SeedSequence, BitGenerator,
Generator]] = None,
full_sampling: Optional[bool] = False,
):
"""Initialize a standard randomized benchmarking experiment.
Args:
qubits: list of physical qubits for the experiment.
lengths: A list of RB sequences lengths.
backend: The backend to run the experiment on.
num_samples: Number of samples to generate for each sequence length.
seed: Optional, seed used to initialize ``numpy.random.default_rng``.
when generating circuits. The ``default_rng`` will be initialized
with this seed value everytime :meth:`circuits` is called.
full_sampling: If True all Cliffords are independently sampled for
all lengths. If False for sample of lengths longer
sequences are constructed by appending additional
Clifford samples to shorter sequences.
The default is False.
"""
# Initialize base experiment
super().__init__(qubits, analysis=RBAnalysis(), backend=backend)
self._verify_parameters(lengths, num_samples)
# Set configurable options
self.set_experiment_options(lengths=list(lengths),
num_samples=num_samples,
seed=seed)
self.analysis.set_options(data_processor=dp.DataProcessor(
input_key="counts",
data_actions=[dp.Probability(outcome="0" * self.num_qubits)],
))
# Set fixed options
self._full_sampling = full_sampling
self._clifford_utils = CliffordUtils()
def _default_options(cls):
"""Return the default analysis options."""
default_options = super()._default_options()
default_options.curve_drawer.set_options(
subplots=(3, 1),
xlabel="Flat top width",
ylabel=[
r"$\langle$X(t)$\rangle$",
r"$\langle$Y(t)$\rangle$",
r"$\langle$Z(t)$\rangle$",
],
xval_unit="s",
figsize=(8, 10),
legend_loc="lower right",
fit_report_rpos=(0.28, -0.10),
ylim=(-1, 1),
)
default_options.data_processor = dp.DataProcessor(
input_key="counts",
data_actions=[dp.Probability("1"),
dp.BasisExpectationValue()],
)
return default_options