def marginal_counts(result, indices=None, inplace=False):
"""Marginalize counts from an experiment over some indices of interest.
Args:
result (dict or Result): result to be marginalized
(a Result object or a dict of counts).
indices (list(int) or None): The bit positions of interest
to marginalize over. If None, do not marginalize at all.
inplace (bool): Default: False. Operates on the original Result
argument if True, leading to loss of original Job Result.
It has not effect if result is dict.
Returns:
Result or dict[str:int]: a dictionary with the observed counts,
marginalized to only account for frequency of observations
of bits of interest.
Raises:
QiskitError: in case of invalid indices to marginalize over.
"""
if isinstance(result, Result):
if not inplace:
result = deepcopy(result)
for i, experiment_result in enumerate(result.results):
counts = result.get_counts(i)
new_counts = _marginalize(counts, indices)
new_counts_hex = {}
for k, v in new_counts.items():
new_counts_hex[_bin_to_hex(k)] = v
experiment_result.data.counts = new_counts_hex
experiment_result.header.memory_slots = len(indices)
else:
result = _marginalize(result, indices)
return result
def marginal_counts(result, indices=None):
"""Marginalize counts from an experiment over some indices of interest.
Args:
result (dict or Result): result to be marginalized
(a Result object or a dict of counts).
indices (list(int) or None): The bit positions of interest
to marginalize over. If None, do not marginalize at all.
Returns:
Result or dict[str:int]: a dictionary with the observed counts,
marginalized to only account for frequency of observations
of bits of interest.
Raises:
QiskitError: in case of invalid indices to marginalize over.
"""
from qiskit.result.result import Result
from qiskit.result.postprocess import _bin_to_hex
if isinstance(result, Result):
for i, experiment_result in enumerate(result.results):
counts = result.get_counts(i)
new_counts = _marginalize(counts, indices)
new_counts_hex = {}
for k, v in new_counts.items():
new_counts_hex[_bin_to_hex(k)] = v
experiment_result.data.counts = new_counts_hex
experiment_result.header.memory_slots = len(indices)
else:
counts = result
new_counts = _marginalize(counts, indices)
result = new_counts
return result
def marginal_counts(result,
indices=None,
inplace=False,
format_marginal=False):
"""Marginalize counts from an experiment over some indices of interest.
Args:
result (dict or Result): result to be marginalized
(a Result object or a dict(str, int) of counts).
indices (list(int) or None): The bit positions of interest
to marginalize over. If ``None`` (default), do not marginalize at all.
inplace (bool): Default: False. Operates on the original Result
argument if True, leading to loss of original Job Result.
It has no effect if ``result`` is a dict.
format_marginal (bool): Default: False. If True, takes the output of
marginalize and formats it with placeholders between cregs and
for non-indices.
Returns:
Result or dict(str, int): A Result object or a dictionary with
the observed counts, marginalized to only account for frequency
of observations of bits of interest.
Raises:
QiskitError: in case of invalid indices to marginalize over.
"""
if isinstance(result, Result):
if not inplace:
result = deepcopy(result)
for i, experiment_result in enumerate(result.results):
counts = result.get_counts(i)
new_counts = _marginalize(counts, indices)
new_counts_hex = {}
for k, v in new_counts.items():
new_counts_hex[_bin_to_hex(k)] = v
experiment_result.data.counts = new_counts_hex
experiment_result.header.memory_slots = len(indices)
csize = experiment_result.header.creg_sizes
experiment_result.header.creg_sizes = _adjust_creg_sizes(
csize, indices)
return result
else:
marg_counts = _marginalize(result, indices)
if format_marginal and indices is not None:
marg_counts = _format_marginal(result, marg_counts, indices)
return marg_counts
def marginal_counts(
result: Union[dict, Result],
indices: Optional[List[int]] = None,
inplace: bool = False,
format_marginal: bool = False,
marginalize_memory: Optional[bool] = True,
) -> Union[Dict[str, int], Result]:
"""Marginalize counts from an experiment over some indices of interest.
Args:
result: result to be marginalized
(a Result object or a dict(str, int) of counts).
indices: The bit positions of interest
to marginalize over. If ``None`` (default), do not marginalize at all.
inplace: Default: False. Operates on the original Result
argument if True, leading to loss of original Job Result.
It has no effect if ``result`` is a dict.
format_marginal: Default: False. If True, takes the output of
marginalize and formats it with placeholders between cregs and
for non-indices.
marginalize_memory: If True, then also marginalize the memory field (if present).
If False, remove the memory field from the result.
If None, leave the memory field as is.
Returns:
Result or dict(str, int): A Result object or a dictionary with
the observed counts, marginalized to only account for frequency
of observations of bits of interest.
Raises:
QiskitError: in case of invalid indices to marginalize over.
"""
if isinstance(result, Result):
if not inplace:
result = deepcopy(result)
for i, experiment_result in enumerate(result.results):
counts = result.get_counts(i)
new_counts = _marginalize(counts, indices)
new_counts_hex = {}
for k, v in new_counts.items():
new_counts_hex[_bin_to_hex(k)] = v
experiment_result.data.counts = new_counts_hex
if indices is not None:
experiment_result.header.memory_slots = len(indices)
csize = getattr(experiment_result.header, "creg_sizes", None)
if csize is not None:
experiment_result.header.creg_sizes = _adjust_creg_sizes(csize, indices)
if getattr(experiment_result.data, "memory", None) is not None and indices is not None:
if marginalize_memory is False:
delattr(experiment_result.data, "memory")
elif marginalize_memory is None:
pass # leave as is
else:
sorted_indices = sorted(
indices, reverse=True
) # same convention as for the counts
experiment_result.data.memory = results_rs.marginal_memory(
experiment_result.data.memory, sorted_indices, return_hex=True
)
return result
else:
marg_counts = _marginalize(result, indices)
if format_marginal and indices is not None:
marg_counts = _format_marginal(result, marg_counts, indices)
return marg_counts