# Code source de qiskit.ignis.verification.tomography.data

```
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2019.
#
# 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.
"""
Quantum tomography data
"""
# Needed for functions
from itertools import combinations
from functools import reduce
from re import match
from typing import Dict, Union, List
import numpy as np
###########################################################################
# Data formats for converting from counts to fitter data
#
# TODO: These should be moved to a terra.tools module
###########################################################################
[docs]def marginal_counts(counts: Dict[str, int],
meas_qubits: Union[bool, List[int]] = True,
pad_zeros: bool = False
) -> Dict[str, int]:
"""
Compute marginal counts from a counts dictionary.
Args:
counts: a counts dictionary.
meas_qubits: (default: True) the qubits to NOT be marinalized over
if this is True meas_qubits will be all measured qubits.
pad_zeros: (default: False) Include zero count outcomes in return dict.
Returns:
A counts dictionary for the specified qubits. The returned dictionary
will have any whitespace trimmed from the input counts keys. Thus if
meas_qubits=True the returned dictionary will have the same values as
the input dictionary, but with whitespace trimmed from the keys.
"""
# Extract total number of qubits from first count key
# We trim the whitespace seperating classical registers
# and count the number of digits
num_qubits = len(next(iter(counts)).replace(' ', ''))
# Check if we do not need to marginalize. In this case we just trim
# whitespace from count keys
if (meas_qubits is True) or (num_qubits == len(meas_qubits)):
ret = {}
for key, val in counts.items():
key = key.replace(' ', '')
ret[key] = val
return ret
# Sort the measured qubits into decending order
# Since bitstrings have qubit-0 as least significant bit
if meas_qubits is True:
meas_qubits = range(num_qubits) # All measured
qubits = sorted(meas_qubits, reverse=True)
# Generate bitstring keys for measured qubits
meas_keys = count_keys(len(qubits))
# Get regex match strings for summing outcomes of other qubits
rgx = []
for key in meas_keys:
def helper(x, y):
if y in qubits:
return key[qubits.index(y)] + x
return '\\d' + x
rgx.append(reduce(helper, range(num_qubits), ''))
# Build the return list
meas_counts = []
for m in rgx:
c = 0
for key, val in counts.items():
if match(m, key.replace(' ', '')):
c += val
meas_counts.append(c)
# Return as counts dict on measured qubits only
if pad_zeros is True:
return dict(zip(meas_keys, meas_counts))
ret = {}
for key, val in zip(meas_keys, meas_counts):
if val != 0:
ret[key] = val
return ret
[docs]def count_keys(num_qubits: int) -> List[str]:
"""Return ordered count keys.
Args:
num_qubits: The number of qubits in the generated list.
Returns:
The strings of all 0/1 combinations of the given number of qubits
Example:
>>> count_keys(3)
['000', '001', '010', '011', '100', '101', '110', '111']
"""
return [bin(j)[2:].zfill(num_qubits)
for j in range(2 ** num_qubits)]
[docs]def combine_counts(counts1: Dict[str, int],
counts2: Dict[str, int]
) -> Dict[str, int]:
"""Combine two counts dictionaries.
Args:
counts1: One of the count dictionaries to combine.
counts2: One of the count dictionaries to combine.
Returns:
A dict containing the **sum** of entries in counts1 and counts2
where a nonexisting entry is treated as 0
Example:
>>> counts1 = {'00': 3, '01': 5}
>>> counts2 = {'00': 4, '10': 7}
>>> combine_counts(counts1, counts2)
{'00': 7, '01': 5, '10': 7}
"""
ret = counts1
for key, val in counts2.items():
if key in ret:
ret[key] += val
else:
ret[key] = val
return ret
[docs]def expectation_counts(counts: Dict[str, int]) -> Dict[str, int]:
"""Converts count dict to an expectation counts dict.
The returned dictionary is also a counts dictionary but the keys
correspond to the which subsystems the operators are acting on
and the counts are the un-normalized expectation values. The counts
can be converted to expectation values by dividing by the value of the
all '0's entry. The '0's key is the expectation value of the identity
operator, and its value is equal to the number of shots.
Args:
counts: a counts dictionary.
Returns:
A new counts dictionary where the counts are un-normalized
expectation values for the subsystem measurement operators.
Consider a input counts dictionary for `s` shots of measurement of
the two-qubit operator XZ (X on qubit-1, Z on qubit-0). The
dictionary returned will have keys corresponding to:
* ``00``: :math:`s * <II>`,
* ``01``: :math:`s * <IZ>`,
* ``10``: :math:`s * <XI>`,
* ``11``: :math:`s * <XZ>`
"""
# Get total shots for data set
shots = np.sum(list(counts.values()))
# Compute measured operators subsets in a data set
numq = len(list(counts.keys())[0])
# Get operator subsets
subsets = []
for r in range(numq):
subsets += list(combinations(range(numq), r + 1))
# Compute expectation values
exp_data = {'00': shots}
for subset in subsets:
exp_counts = 0
exp_op = numq * ['0']
# Get expectation operator
for qubit in subset:
exp_op[qubit] = '1'
# Get expectation value
for key, val in marginal_counts(counts,
subset,
pad_zeros=True).items():
exp_counts += (-1) ** (key.count('1')) * val
exp_data[''.join(exp_op)] = exp_counts
return exp_data
```