def __init__(self, data, input_dims=None, output_dims=None):
"""Initialize a Chi 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
chi_mat = _to_chi(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, np.ndarray)):
chi_mat = np.array(data, dtype=complex)
# Determine input and output dimensions
dim_l, dim_r = chi_mat.shape
if dim_l != dim_r:
raise QiskitError('Invalid Choi-matrix input.')
if input_dims:
input_dim = np.product(input_dims)
if output_dims:
output_dim = np.product(input_dims)
if output_dims is None and input_dims is None:
output_dim = int(np.sqrt(dim_l))
input_dim = dim_l // output_dim
elif input_dims is None:
input_dim = dim_l // output_dim
elif output_dims is None:
output_dim = dim_l // input_dim
# Check dimensions
if input_dim * output_dim != dim_l:
raise QiskitError("Invalid shape for Chi-matrix input.")
else:
raise QiskitError("Invalid input data format for Chi")
nqubits = int(np.log2(input_dim))
if 2**nqubits != input_dim:
raise QiskitError("Input is not an n-qubit Chi matrix.")
# 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)
super().__init__('Chi', chi_mat, input_dims, output_dims)
def __init__(self, data, input_dims=None, output_dims=None):
"""Initialize a quantum channel Chi-matrix 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 is not an N-qubit channel or
cannot be initialized as a Chi-matrix.
Additional Information
----------------------
If the input or output dimensions are None, they will be
automatically determined from the input data. The Chi matrix
representation is only valid for N-qubit channels.
"""
# If the input is a raw list or matrix we assume that it is
# already a Chi matrix.
if isinstance(data, (list, np.ndarray)):
# Initialize from raw numpy or list matrix.
chi_mat = np.array(data, dtype=complex)
# Determine input and output dimensions
dim_l, dim_r = chi_mat.shape
if dim_l != dim_r:
raise QiskitError('Invalid Chi-matrix input.')
if input_dims:
input_dim = np.product(input_dims)
if output_dims:
output_dim = np.product(input_dims)
if output_dims is None and input_dims is None:
output_dim = int(np.sqrt(dim_l))
input_dim = dim_l // output_dim
elif input_dims is None:
input_dim = dim_l // output_dim
elif output_dims is None:
output_dim = dim_l // input_dim
# Check dimensions
if input_dim * output_dim != dim_l:
raise QiskitError("Invalid shape for Chi-matrix input.")
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._instruction_to_superop(data)
else:
# We use the QuantumChannel init transform to intialize
# other objects into a QuantumChannel or Operator object.
data = self._init_transformer(data)
input_dim, output_dim = data.dim
# Now that the input is an operator we convert it to a Chi object
chi_mat = _to_chi(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()
# Check input is N-qubit channel
n_qubits = int(np.log2(input_dim))
if 2**n_qubits != input_dim:
raise QiskitError("Input is not an n-qubit Chi matrix.")
# 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)
super().__init__('Chi', chi_mat, input_dims, output_dims)
