# Copyright 2018 The Cirq Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Defines trial results."""
from typing import Iterable, Callable, Tuple, TypeVar, Dict, Any
import collections
import numpy as np
from cirq import value
from cirq.study import resolver
T = TypeVar('T')
def _tuple_of_big_endian_int(bit_groups: Tuple[np.ndarray, ...]
) -> Tuple[int, ...]:
"""Returns the big-endian integers specified by groups of bits.
Args:
bit_groups: Groups of descending bits, each specifying a big endian
integer with the 1s bit at the end.
Returns:
A tuple containing the integer for each group.
"""
return tuple(_big_endian_int(bits) for bits in bit_groups)
def _big_endian_int(bits: np.ndarray) -> int:
"""Returns the big-endian integer specified by the given bits.
For example, [True, False, False, True, False] becomes binary 10010 which
is 18 in decimal.
Args:
bits: Descending bits of the integer, with the 1s bit at the end.
Returns:
The integer.
"""
result = 0
for e in bits:
result <<= 1
if e:
result |= 1
return result
def _bitstring(vals: Iterable[Any]) -> str:
return ''.join('1' if v else '0' for v in vals)
def _keyed_repeated_bitstrings(vals: Dict[str, np.ndarray]
) -> str:
keyed_bitstrings = []
for key in sorted(vals.keys()):
reps = vals[key]
n = 0 if len(reps) == 0 else len(reps[0])
all_bits = ', '.join([_bitstring(reps[:, i])
for i in range(n)])
keyed_bitstrings.append('{}={}'.format(key, all_bits))
return '\n'.join(keyed_bitstrings)
[docs]@value.value_equality(unhashable=True)
class TrialResult:
"""The results of multiple executions of a circuit with fixed parameters.
Attributes:
params: A ParamResolver of settings used when sampling result.
measurements: A dictionary from measurement gate key to measurement
results. Measurement results are a list of lists (a numpy ndarray),
the first list corresponding to the repetition, and the second is
the actual boolean measurement results (ordered by the qubits acted
the measurement gate.)
repetitions: The number of times a circuit was sampled to get these
results.
"""
[docs] def __init__(self, *, # Forces keyword args.
params: resolver.ParamResolver,
measurements: Dict[str, np.ndarray],
repetitions: int) -> None:
"""
Args:
params: A ParamResolver of settings used for this result.
measurements: A dictionary from measurement gate key to measurement
results. The value for each key is a 2-D array of booleans,
with the first index running over the repetitions, and the
second index running over the qubits for the corresponding
measurements.
repetitions: The number of times the circuit was sampled.
"""
self.params = params
self.measurements = measurements
self.repetitions = repetitions
# Reason for 'type: ignore': https://github.com/python/mypy/issues/5273
[docs] def multi_measurement_histogram( # type: ignore
self, *, # Forces keyword args.
keys: Iterable[str],
fold_func: Callable[[Tuple[np.ndarray, ...]],
T] = _tuple_of_big_endian_int
) -> collections.Counter:
"""Counts the number of times combined measurement results occurred.
This is a more general version of the 'histogram' method. Instead of
only counting how often results occurred for one specific measurement,
this method tensors multiple measurement results together and counts
how often the combined results occurred.
For example, suppose that:
- fold_func is not specified
- keys=['abc', 'd']
- the measurement with key 'abc' measures qubits a, b, and c.
- the measurement with key 'd' measures qubit d.
- the circuit was sampled 3 times.
- the sampled measurement values were:
1. a=1 b=0 c=0 d=0
2. a=0 b=1 c=0 d=1
3. a=1 b=0 c=0 d=0
Then the counter returned by this method will be:
collections.Counter({
(0b100, 0): 2,
(0b010, 1): 1
})
Where '0b100' is binary for '4' and '0b010' is binary for '2'. Notice
that the bits are combined in a big-endian way by default, with the
first measured qubit determining the highest-value bit.
Args:
fold_func: A function used to convert sampled measurement results
into countable values. The input is a tuple containing the
list of bits measured by each measurement specified by the
keys argument. If this argument is not specified, it defaults
to returning tuples of integers, where each integer is the big
endian interpretation of the bits a measurement sampled.
keys: Keys of measurements to include in the histogram.
Returns:
A counter indicating how often measurements sampled various
results.
"""
fixed_keys = tuple(keys)
samples = zip(*[self.measurements[sub_key]
for sub_key in fixed_keys]) # type: Iterable[Any]
if len(fixed_keys) == 0:
samples = [()] * self.repetitions
c = collections.Counter() # type: collections.Counter
for sample in samples:
c[fold_func(sample)] += 1
return c
# Reason for 'type: ignore': https://github.com/python/mypy/issues/5273
[docs] def histogram(self, # type: ignore
*, # Forces keyword args.
key: str,
fold_func: Callable[[np.ndarray], T] = _big_endian_int
) -> collections.Counter:
"""Counts the number of times a measurement result occurred.
For example, suppose that:
- fold_func is not specified
- key='abc'
- the measurement with key 'abc' measures qubits a, b, and c.
- the circuit was sampled 3 times.
- the sampled measurement values were:
1. a=1 b=0 c=0
2. a=0 b=1 c=0
3. a=1 b=0 c=0
Then the counter returned by this method will be:
collections.Counter({
0b100: 2,
0b010: 1
})
Where '0b100' is binary for '4' and '0b010' is binary for '2'. Notice
that the bits are combined in a big-endian way by default, with the
first measured qubit determining the highest-value bit.
Args:
positional_args: Never specified. Forces keyword arguments.
key: Keys of measurements to include in the histogram.
fold_func: A function used to convert a sampled measurement result
into a countable value. The input is a list of bits sampled
together by a measurement. If this argument is not specified,
it defaults to interpreting the bits as a big endian
integer.
Returns:
A counter indicating how often a measurement sampled various
results.
"""
return self.multi_measurement_histogram(
keys=[key],
fold_func=lambda e: fold_func(e[0]))
def __repr__(self):
return ('cirq.TrialResult(params={!r}, '
'repetitions={!r}, '
'measurements={!r})').format(self.params,
self.repetitions,
self.measurements)
def __str__(self):
return _keyed_repeated_bitstrings(self.measurements)
def _value_equality_values_(self):
return self.measurements, self.repetitions, self.params