if not isinstance(obj.definition, QuantumCircuit):
raise QiskitError('Instruction "{}" '
'definition is {} but expected QuantumCircuit.'.format(
obj.name, type(obj.definition)))
if obj.definition.global_phase:
dimension = 2 ** obj.num_qubits
op = self.compose(
ScalarOp(dimension, np.exp(1j * float(obj.definition.global_phase))),
qargs=qargs)
self._data = op.data
flat_instr = obj.definition
bit_indices = {bit: index
for bits in [flat_instr.qubits, flat_instr.clbits]
for index, bit in enumerate(bits)}
for instr, qregs, cregs in flat_instr:
if cregs:
raise QiskitError(
'Cannot apply instruction with classical registers: {}'.format(
instr.name))
# Get the integer position of the flat register
if qargs is None:
new_qargs = [bit_indices[tup] for tup in qregs]
else:
new_qargs = [qargs[bit_indices[tup]] for tup in qregs]
self._append_instruction(instr, qargs=new_qargs)
# Update docstrings for API docs
generate_apidocs(Operator)
return self.obj._to_label(self.obj.array[key])
return LabelIterator(self)
def matrix_iter(self, sparse=False):
"""Return a matrix representation iterator.
This is a lazy iterator that converts each row into the Pauli matrix
representation only as it is used. To convert the entire table to
matrices use the :meth:`to_matrix` method.
Args:
sparse (bool): optionally return sparse CSR matrices if True,
otherwise return Numpy array matrices
(Default: False)
Returns:
MatrixIterator: matrix iterator object for the PauliTable.
"""
class MatrixIterator(CustomIterator):
"""Matrix representation iteration and item access."""
def __repr__(self):
return "<PauliTable_matrix_iterator at {}>".format(hex(id(self)))
def __getitem__(self, key):
return self.obj._to_matrix(self.obj.array[key], sparse=sparse)
return MatrixIterator(self)
# Update docstrings for API docs
generate_apidocs(PauliTable)
# ---------------------------------------------------------------------
# Label parsing helper functions
# ---------------------------------------------------------------------
def _split_pauli_label(label):
"""Split Pauli label into unsigned group label and coefficient label"""
span = re.search(r'[IXYZ]+', label).span()
pauli = label[span[0]:]
coeff = label[:span[0]]
if span[1] != len(label):
invalid = set(re.sub(r'[IXYZ]+', '', label[span[0]:]))
raise QiskitError("Pauli string contains invalid characters "
"{} ∉ ['I', 'X', 'Y', 'Z']".format(invalid))
return pauli, coeff
def _phase_from_label(label):
"""Return the phase from a label"""
# Returns None if label is invalid
label = label.replace('+', '', 1).replace('1', '', 1).replace('j', 'i', 1)
phases = {'': 0, '-i': 1, '-': 2, 'i': 3}
if label not in phases:
raise QiskitError("Invalid Pauli phase label '{}'".format(label))
return phases.get(label)
# Update docstrings for API docs
generate_apidocs(Pauli)
else:
# If the instruction doesn't have a matrix defined we use its
# circuit decomposition definition if it exists, otherwise we
# cannot compose this gate and raise an error.
if obj.definition is None:
raise QiskitError("Cannot apply Instruction: {}".format(
obj.name))
if not isinstance(obj.definition, QuantumCircuit):
raise QiskitError("{} instruction definition is {}; "
"expected QuantumCircuit".format(
obj.name, type(obj.definition)))
qubit_indices = {
bit: idx
for idx, bit in enumerate(obj.definition.qubits)
}
for instr, qregs, cregs in obj.definition.data:
if cregs:
raise QiskitError(
"Cannot apply instruction with classical registers: {}"
.format(instr.name))
# Get the integer position of the flat register
if qargs is None:
new_qargs = [qubit_indices[tup] for tup in qregs]
else:
new_qargs = [qargs[qubit_indices[tup]] for tup in qregs]
self._append_instruction(instr, qargs=new_qargs)
# Update docstrings for API docs
generate_apidocs(SuperOp)
qubit_wise (bool): whether the commutation rule is applied to the whole operator,
or on a per-qubit basis. For example:
.. code-block:: python
>>> op = SparsePauliOp.from_list([("XX", 2), ("YY", 1), ("IZ",2j), ("ZZ",1j)])
>>> op.group_commuting()
[SparsePauliOp(["IZ", "ZZ"], coeffs=[0.+2.j, 0.+1j]),
SparsePauliOp(["XX", "YY"], coeffs=[2.+0.j, 1.+0.j])]
>>> op.group_commuting(qubit_wise=True)
[SparsePauliOp(['XX'], coeffs=[2.+0.j]),
SparsePauliOp(['YY'], coeffs=[1.+0.j]),
SparsePauliOp(['IZ', 'ZZ'], coeffs=[0.+2.j, 0.+1.j])]
Returns:
List[SparsePauliOp]: List of SparsePauliOp where each SparsePauliOp contains
commuting Pauli operators.
"""
graph = self._create_graph(qubit_wise)
# Keys in coloring_dict are nodes, values are colors
coloring_dict = rx.graph_greedy_color(graph)
groups = defaultdict(list)
for idx, color in coloring_dict.items():
groups[color].append(idx)
return [self[group] for group in groups.values()]
# Update docstrings for API docs
generate_apidocs(SparsePauliOp)
# Contract Choi matrices for composition
data = np.reshape(np.einsum('iAjB,AkBl->ikjl', first, second),
(input_dim * output_dim, input_dim * output_dim))
ret = Choi(data)
ret._op_shape = new_shape
return ret
def tensor(self, other):
if not isinstance(other, Choi):
other = Choi(other)
return self._tensor(self, other)
def expand(self, other):
if not isinstance(other, Choi):
other = Choi(other)
return self._tensor(other, self)
@classmethod
def _tensor(cls, a, b):
ret = copy.copy(a)
ret._op_shape = a._op_shape.tensor(b._op_shape)
ret._data = _bipartite_tensor(a._data,
b.data,
shape1=a._bipartite_shape,
shape2=b._bipartite_shape)
return ret
# Update docstrings for API docs
generate_apidocs(Choi)
ret.table.phase ^= np.mod(
np.sum(ret.table.X & ret.table.Z, axis=1), 2).astype(bool)
return ret
def _pad_with_identity(self, clifford, qargs):
"""Pad Clifford with identities on other subsystems."""
if qargs is None:
return clifford
padded = Clifford(StabilizerTable(
np.eye(2 * self.num_qubits, dtype=bool)),
validate=False)
inds = list(qargs) + [self.num_qubits + i for i in qargs]
# Pad Pauli array
pauli = clifford.table.array
for i, pos in enumerate(qargs):
padded.table.array[inds, pos] = pauli[:, i]
padded.table.array[inds, self.num_qubits +
pos] = pauli[:, clifford.num_qubits + i]
# Pad phase
padded.table.phase[inds] = clifford.table.phase
return padded
# Update docstrings for API docs
generate_apidocs(Clifford)
def _add(self, other, qargs=None):
# Since we cannot directly add two channels in the Kraus
# representation we try and use the other channels method
# or convert to the Choi representation
return Kraus(Choi(self)._add(other, qargs=qargs))
def _multiply(self, other):
if not isinstance(other, Number):
raise QiskitError("other is not a number")
ret = copy.copy(self)
# If the number is complex we need to convert to general
# kraus channel so we multiply via Choi representation
if isinstance(other, complex) or other < 0:
# Convert to Choi-matrix
ret._data = Kraus(Choi(self)._multiply(other))._data
return ret
# If the number is real we can update the Kraus operators
# directly
val = np.sqrt(other)
kraus_r = None
kraus_l = [val * k for k in self._data[0]]
if self._data[1] is not None:
kraus_r = [val * k for k in self._data[1]]
ret._data = (kraus_l, kraus_r)
return ret
# Update docstrings for API docs
generate_apidocs(Kraus)
# Since we cannot directly add two channels in the Stinespring
# representation we convert to the Choi representation
return Stinespring(Choi(self)._add(other, qargs=qargs))
def _multiply(self, other):
if not isinstance(other, Number):
raise QiskitError("other is not a number")
ret = copy.copy(self)
# If the number is complex or negative we need to convert to
# general Stinespring representation so we first convert to
# the Choi representation
if isinstance(other, complex) or other < 1:
# Convert to Choi-matrix
ret._data = Stinespring(Choi(self)._multiply(other))._data
return ret
# If the number is real we can update the Kraus operators
# directly
num = np.sqrt(other)
stine_l, stine_r = self._data
stine_l = num * self._data[0]
stine_r = None
if self._data[1] is not None:
stine_r = num * self._data[1]
ret._data = (stine_l, stine_r)
return ret
# Update docstrings for API docs
generate_apidocs(Stinespring)
def matrix_iter(self, sparse=False):
"""Return a matrix representation iterator.
This is a lazy iterator that converts each row into the Pauli matrix
representation only as it is used. To convert the entire table to
matrices use the :meth:`to_matrix` method.
Args:
sparse (bool): optionally return sparse CSR matrices if True,
otherwise return Numpy array matrices
(Default: False)
Returns:
MatrixIterator: matrix iterator object for the StabilizerTable.
"""
class MatrixIterator(CustomIterator):
"""Matrix representation iteration and item access."""
def __repr__(self):
return f"<StabilizerTable_matrix_iterator at {hex(id(self))}>"
def __getitem__(self, key):
return self.obj._to_matrix(self.obj.array[key],
self.obj.phase[key],
sparse=sparse)
return MatrixIterator(self)
# Update docstrings for API docs
generate_apidocs(StabilizerTable)
ScalarOp: the scaled identity operator other * self.
Raises:
QiskitError: if other is not a valid complex number.
"""
if not isinstance(other, Number):
raise QiskitError("other ({}) is not a number".format(other))
ret = self.copy()
ret._coeff = other * self.coeff
return ret
@staticmethod
def _pad_with_identity(current, other, qargs=None):
"""Pad another operator with identities.
Args:
current (BaseOperator): current operator.
other (BaseOperator): other operator.
qargs (None or list): qargs
Returns:
BaseOperator: the padded operator.
"""
if qargs is None:
return other
return ScalarOp(current.input_dims()).compose(other, qargs=qargs)
# Update docstrings for API docs
generate_apidocs(ScalarOp)
return result
def _is_valid(self):
"""Return True if input is a CNOTDihedral element."""
if (self.poly.weight_0 != 0
or len(self.poly.weight_1) != self.num_qubits
or len(self.poly.weight_2) != int(self.num_qubits *
(self.num_qubits - 1) / 2)
or len(self.poly.weight_3) != int(self.num_qubits *
(self.num_qubits - 1) *
(self.num_qubits - 2) / 6)):
return False
if ((self.linear).shape != (self.num_qubits, self.num_qubits)
or len(self.shift) != self.num_qubits or not np.allclose(
(np.linalg.det(self.linear) % 2), 1)):
return False
if (not (set(self.poly.weight_1.flatten())).issubset(
{0, 1, 2, 3, 4, 5, 6, 7}) or
not (set(self.poly.weight_2.flatten())).issubset({0, 2, 4, 6})
or not (set(self.poly.weight_3.flatten())).issubset({0, 4})):
return False
if not (set(self.shift.flatten())).issubset({0, 1}) or not (set(
self.linear.flatten())).issubset({0, 1}):
return False
return True
# Update docstrings for API docs
generate_apidocs(CNOTDihedral)
input_dims = new_shape.dims_r()
output_dims = new_shape.dims_l()
if front:
data = np.dot(self._data, other.data)
else:
data = np.dot(other.data, self._data)
ret = PTM(data, input_dims, output_dims)
ret._op_shape = new_shape
return ret
def tensor(self, other):
if not isinstance(other, PTM):
other = PTM(other)
return self._tensor(self, other)
def expand(self, other):
if not isinstance(other, PTM):
other = PTM(other)
return self._tensor(other, self)
@classmethod
def _tensor(cls, a, b):
ret = copy.copy(a)
ret._op_shape = a._op_shape.tensor(b._op_shape)
ret._data = np.kron(a._data, b.data)
return ret
# Update docstrings for API docs
generate_apidocs(PTM)
def compose(self, other, qargs=None, front=False):
if qargs is None:
qargs = getattr(other, "qargs", None)
if qargs is not None:
return Chi(SuperOp(self).compose(other, qargs=qargs, front=front))
# If no qargs we compose via Choi representation to avoid an additional
# representation conversion to SuperOp and then convert back to Chi
return Chi(Choi(self).compose(other, front=front))
def tensor(self, other):
if not isinstance(other, Chi):
other = Chi(other)
return self._tensor(self, other)
def expand(self, other):
if not isinstance(other, Chi):
other = Chi(other)
return self._tensor(other, self)
@classmethod
def _tensor(cls, a, b):
ret = copy.copy(a)
ret._op_shape = a._op_shape.tensor(b._op_shape)
ret._data = np.kron(a._data, b.data)
return ret
# Update docstrings for API docs
generate_apidocs(Chi)
