def __init__(self, data, input_dims=None, output_dims=None):
"""Initialize a Stinespring quantum channel operator."""
if issubclass(data.__class__, BaseOperator):
# If not a channel we use `to_operator` method to get
# the unitary-representation matrix for input
if not issubclass(data.__class__, QuantumChannel):
data = data.to_operator()
input_dim, output_dim = data.dim
stine = _to_stinespring(data.rep, data._data, input_dim,
output_dim)
if input_dims is None:
input_dims = data.input_dims()
if output_dims is None:
output_dims = data.output_dims()
elif isinstance(data, (list, tuple, np.ndarray)):
if not isinstance(data, tuple):
# Convert single Stinespring set to length 1 tuple
stine = (np.array(data, dtype=complex), None)
if isinstance(data, tuple) and len(data) == 2:
if data[1] is None:
stine = (np.array(data[0], dtype=complex), None)
else:
stine = (np.array(data[0], dtype=complex),
np.array(data[1], dtype=complex))
dim_left, dim_right = stine[0].shape
# If two stinespring matrices check they are same shape
if stine[1] is not None:
if stine[1].shape != (dim_left, dim_right):
raise QiskitError("Invalid Stinespring input.")
input_dim = dim_right
if output_dims:
output_dim = np.product(output_dims)
else:
output_dim = input_dim
if dim_left % output_dim != 0:
raise QiskitError("Invalid output_dim")
else:
raise QiskitError("Invalid input data format for Stinespring")
# Check and format input and output dimensions
input_dims = self._automatic_dims(input_dims, input_dim)
output_dims = self._automatic_dims(output_dims, output_dim)
# Initialize either single or general Stinespring
if stine[1] is None or (stine[1] == stine[0]).all():
# Standard Stinespring map
super().__init__(
'Stinespring', (stine[0], None),
input_dims=input_dims,
output_dims=output_dims)
else:
# General (non-CPTP) Stinespring map
super().__init__(
'Stinespring',
stine,
input_dims=input_dims,
output_dims=output_dims)
def __init__(self, data, input_dims=None, output_dims=None):
"""Initialize a quantum channel Stinespring operator.
Args:
data (QuantumCircuit or
Instruction or
BaseOperator or
matrix): data to initialize superoperator.
input_dims (tuple): the input subsystem dimensions.
[Default: None]
output_dims (tuple): the output subsystem dimensions.
[Default: None]
Raises:
QiskitError: if input data cannot be initialized as a
a list of Kraus matrices.
Additional Information:
If the input or output dimensions are None, they will be
automatically determined from the input data. This can fail for the
Stinespring operator if the output dimension cannot be automatically
determined.
"""
# If the input is a list or tuple we assume it is a pair of general
# Stinespring matrices. If it is a numpy array we assume that it is
# a single Stinespring matrix.
if isinstance(data, (list, tuple, np.ndarray)):
if not isinstance(data, tuple):
# Convert single Stinespring set to length 1 tuple
stine = (np.asarray(data, dtype=complex), None)
if isinstance(data, tuple) and len(data) == 2:
if data[1] is None:
stine = (np.asarray(data[0], dtype=complex), None)
else:
stine = (np.asarray(data[0], dtype=complex),
np.asarray(data[1], dtype=complex))
dim_left, dim_right = stine[0].shape
# If two Stinespring matrices check they are same shape
if stine[1] is not None:
if stine[1].shape != (dim_left, dim_right):
raise QiskitError("Invalid Stinespring input.")
input_dim = dim_right
if output_dims:
output_dim = np.product(output_dims)
else:
output_dim = input_dim
if dim_left % output_dim != 0:
raise QiskitError("Invalid output_dim")
else:
# Otherwise we initialize by conversion from another Qiskit
# object into the QuantumChannel.
if isinstance(data, (QuantumCircuit, Instruction)):
# If the input is a Terra QuantumCircuit or Instruction we
# convert it to a SuperOp
data = SuperOp._init_instruction(data)
else:
# We use the QuantumChannel init transform to intialize
# other objects into a QuantumChannel or Operator object.
data = self._init_transformer(data)
data = self._init_transformer(data)
input_dim, output_dim = data.dim
# Now that the input is an operator we convert it to a
# Stinespring operator
rep = getattr(data, '_channel_rep', 'Operator')
stine = _to_stinespring(rep, data._data, input_dim, output_dim)
if input_dims is None:
input_dims = data.input_dims()
if output_dims is None:
output_dims = data.output_dims()
# Check and format input and output dimensions
input_dims = self._automatic_dims(input_dims, input_dim)
output_dims = self._automatic_dims(output_dims, output_dim)
# Initialize either single or general Stinespring
if stine[1] is None or (stine[1] == stine[0]).all():
# Standard Stinespring map
super().__init__((stine[0], None),
input_dims=input_dims,
output_dims=output_dims,
channel_rep='Stinespring')
else:
# General (non-CPTP) Stinespring map
super().__init__(stine,
input_dims=input_dims,
output_dims=output_dims,
channel_rep='Stinespring')
