def simple_est_rb(data,
interleaved=False,
p_min=0.0,
p_max=1.0,
n_particles=8000,
return_all=False):
r"""
Estimates the fidelity of a gateset from a standard or interleaved randomized benchmarking
experiment.
:param data: Data to be used in estimating the gateset fidelity.
:type data: see :ref:`simple_est_data_arg`
:param float p_min: Minimum value of the parameter :math:`p`
to consider feasible.
:param float p_max: Minimum value of the parameter :math:`p`
to consider feasible.
:param int n_particles: The number of particles to be used in estimating
the randomized benchmarking model.
:param bool return_all: Controls whether additional return
values are provided, such as the updater.
:column counts (int): How many sequences of length :math:`m` were observed to
survive.
:column m (int): How many gates were used for sequences in this row of the data.
:column n_shots (int): How many different sequences of length :math:`m`
were measured.
:column reference (bool): `True` if this row represents reference sequences, or
`False` if the gate of interest is interleaved. Note that this column is omitted
if ``interleaved`` is `False`.
:return mean: Bayesian mean estimator for the model vector
:math:`(p, A, B)`, or :math:`(\tilde{p}, p_{\text{ref}}, A, B)`
for the interleaved case.
:return var: Variance of the final posterior over RB model vectors.
:return extra: See :ref:`simple_est_extra_return`. Only returned
if ``return_all`` is `True`.
"""
model = BinomialModel(RandomizedBenchmarkingModel(interleaved=interleaved))
prior = PostselectedDistribution(
UniformDistribution(
[[p_min, p_max], [0, 1], [0, 1]] if not interleaved else
[[p_min, p_max], [p_min, p_max], [0, 1], [0, 1]]), model)
data = load_data_or_txt(data, [('counts', 'uint'), ('m', 'uint'),
('n_shots', 'uint')] +
([('reference', 'uint')] if interleaved else []))
cols_expparams = {'m': (1, 'm'), 'n_meas': (2, 'n_shots')}
if interleaved:
cols_expparams['reference'] = (3, 'reference')
outcomes, expparams = data_to_params(data,
model.expparams_dtype,
cols_expparams=cols_expparams)
return do_update(model, n_particles, prior, outcomes, expparams,
return_all)
def simple_est_prec(data, freq_min=0.0, freq_max=1.0, n_particles=6000, return_all=False):
"""
Estimates a simple precession (cos²) from experimental data.
Note that this model is mainly for testing purposes, as it does not
consider the phase or amplitude of precession, leaving only the frequency.
:param data: Data to be used in estimating the precession frequency.
:type data: see :ref:`simple_est_data_arg`
:param float freq_min: The minimum feasible frequency to consider.
:param float freq_max: The maximum feasible frequency to consider.
:param int n_particles: The number of particles to be used in estimating
the precession frequency.
:param bool return_all: Controls whether additional return
values are provided, such as the updater.
:column counts (int): How many counts were observed at the sampled
time.
:column t (float): The evolutions time at which the samples
were collected.
:column n_shots (int): How many samples were collected at the
given evolution time.
:return mean: Bayesian mean estimator for the precession frequency.
:return var: Variance of the final posterior over frequency.
:return extra: See :ref:`simple_est_extra_return`. Only returned
if ``return_all`` is `True`.
"""
model = BinomialModel(SimplePrecessionModel(freq_min))
prior = UniformDistribution([0, freq_max])
data = load_data_or_txt(data, [
('counts', 'uint'),
('t', float),
('n_shots', 'uint')
])
outcomes, expparams = data_to_params(data,
model.expparams_dtype,
cols_expparams={
'x': (1, 't'),
'n_meas': (2, 'n_shots')
}
)
return do_update(
model, n_particles, prior, outcomes, expparams,
return_all
)
def run_estimate(data, freq,
n_particles=10000, return_all=False):
"""this is a copy of qinfer.simple_
Args:
data:
n_particles:
return_all:
Returns:
"""
model = BinomialModel(AmplitudeEstimator(freq))
# prior = PostselectedDistribution(
# UniformDistribution(
# [
# [0.30, 0.5],
# [0.30, 0.5]
# ]),
# model,
# maxiters=10000
# )
prior = MyDistribution()
data = load_data_or_txt(data, [
('counts', 'uint'),
('t', float),
('n_shots', 'uint')
])
outcomes, expparams = data_to_params(data,
model.expparams_dtype,
cols_expparams={
't': (1, 't'),
'n_meas': (2, 'n_shots')
}
)
return do_update(
model, n_particles, prior, outcomes, expparams,
return_all
)