# Source code for qinfer.simple_est

#!/usr/bin/python
# -*- coding: utf-8 -*-
##
# simple_est.py: Simplified estimation functions for common experiments.
##
# © 2017, Chris Ferrie ([email protected]) and
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
#     1. Redistributions of source code must retain the above copyright
#        notice, this list of conditions and the following disclaimer.
#
#     2. Redistributions in binary form must reproduce the above copyright
#        notice, this list of conditions and the following disclaimer in the
#        documentation and/or other materials provided with the distribution.
#
#     3. Neither the name of the copyright holder nor the names of its
#        contributors may be used to endorse or promote products derived from
#        this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
##

## FEATURES ###################################################################

from __future__ import absolute_import
from __future__ import division

## EXPORTS ###################################################################

__all__ = [
'simple_est_prec',
'simple_est_rb',
# TODO:
# 'simple_est_rabi'
]

## IMPORTS ###################################################################

import numpy as np

from qinfer.smc import SMCUpdater
from qinfer.test_models import SimplePrecessionModel
from qinfer.rb import RandomizedBenchmarkingModel
from qinfer.derived_models import BinomialModel
from qinfer.distributions import UniformDistribution, PostselectedDistribution
from qinfer.resamplers import LiuWestResampler

# We want to be able to support Pandas without requiring it, so a conditional
# import is required.
try:
import pandas as pd
except:
pd = None

## FUNCTIONS #################################################################

def data_to_params(data,
expparams_dtype,
col_outcomes=(0, 'counts'),
cols_expparams=None
):
"""
Given data as a NumPy array, separates out each column either as
the outcomes, or as a field of an expparams array. Columns may be specified
either as indices into a two-axis scalar array, or as field names for a one-axis
record array.

Since scalar arrays are homogenous in type, this may result in loss of precision
due to casting between data types.
"""
BY_IDX, BY_NAME = range(2)

is_exp_scalar = np.issctype(expparams_dtype)
is_data_scalar = np.issctype(data.dtype) and not data.dtype.fields

s_ = (
(lambda idx: np.s_[..., idx[BY_IDX]])
if is_data_scalar else
(lambda idx: np.s_[idx[BY_NAME]])
)

outcomes = data[s_(col_outcomes)].astype(int)

# mk new slicer t

expparams = np.empty(outcomes.shape, dtype=expparams_dtype)
if is_exp_scalar:
expparams[:] = data[s_(cols_expparams)]
else:
for expparams_key, column in cols_expparams.items():
expparams[expparams_key] = data[s_(column)]

return outcomes, expparams

if isinstance(data, np.ndarray):
return data
elif pd is not None and isinstance(data, pd.DataFrame):
return data.to_records(index=False)

elif hasattr(data, 'read') or isinstance(data, str):
return data
else:
raise TypeError("Expected a filename, an array or a file-like object.")

def do_update(model, n_particles, prior, outcomes, expparams, return_all, resampler=None):
updater = SMCUpdater(model, n_particles, prior,
resampler=resampler
)
updater.batch_update(outcomes, expparams, resample_interval=1)

mean = updater.est_mean()
cov = updater.est_covariance_mtx()

if model.n_modelparams == 1:
mean = mean[0]
cov = cov[0, 0]

if not return_all:
return mean, cov
else:
return mean, cov, {
'updater': updater
}

[docs]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])

('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
)

[docs]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
)

('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
)