def test_global_phase(self, num_ctrl_qubits):
"""test global phase"""
mat1 = np.array([[0, 1], [1, 0]], dtype=float)
mat2 = np.exp(1j) * mat1
gate1 = UnitaryGate(mat1)
gate2 = UnitaryGate(mat2)
cgate1 = gate1.q_if(num_ctrl_qubits)
cgate2 = gate2.q_if(num_ctrl_qubits)
cop_mat1 = _compute_control_matrix(mat1, num_ctrl_qubits)
cop_mat2 = _compute_control_matrix(mat2, num_ctrl_qubits, phase=1)
self.assertTrue(is_unitary_matrix(mat1))
self.assertTrue(is_unitary_matrix(mat2))
self.assertTrue(is_unitary_matrix(cop_mat1))
self.assertTrue(is_unitary_matrix(cop_mat2))
self.assertTrue(Operator(cgate1).equiv(Operator(cgate2)))
self.assertTrue(matrix_equal(cop_mat1, cop_mat2, ignore_phase=True))
def get_stinespring_unitary(choi, target_choi, target_unitary, n_qubits):
""" get the Stinespring dilation unitary given the Choi matrix choi"""
unitary = Stinespring(Choi(choi)).data
# this fixes some numerical issues
if np.linalg.norm(choi.data - target_choi) < 1e-8:
unitary = target_unitary
if type(unitary) == tuple:
assert np.linalg.norm(unitary[0] - unitary[1]) < 1e-8
unitary = unitary[0]
assert unitary.shape[1] == 2**n_qubits
# increase hilbert space size to be a power of two
next_pow_2 = int(np.power(2,
np.ceil(np.log(unitary.shape[0]) / np.log(2))))
if unitary.shape[0] != next_pow_2:
diff = next_pow_2 - unitary.shape[0]
unitary = np.vstack([unitary, np.zeros((diff, 2**n_qubits))])
assert unitary.shape[0] == next_pow_2
assert unitary.shape[1] == 2**n_qubits
num_anc = int(np.log2(unitary.shape[0])) - n_qubits
completed = complete_unitary(unitary)
# due to numerical issues, the matrix is not always perfectly unitary
if not is_unitary_matrix(completed, atol=1e-12):
completed, _ = scipy.linalg.polar(completed)
return completed, num_anc
def make_unitary_instruction(mat, qubits, standard_gates=True):
"""Return a qobj instruction for a unitary matrix gate.
Args:
mat (matrix): A square or diagonal unitary matrix.
qubits (list[int]): The qubits the matrix is applied to.
standard_gates (bool): Check if the matrix instruction is a
standard instruction.
Returns:
dict: The qobj instruction object.
Raises:
NoiseError: if the input is not a unitary matrix.
"""
warnings.warn(
'make_unitary_instruction has been deprecated as of qiskit-aer 0.10.0'
' and will be removed no earlier than 3 months from that release date.',
DeprecationWarning, stacklevel=2)
if not is_unitary_matrix(mat):
raise NoiseError("Input matrix is not unitary.")
if isinstance(qubits, int):
qubits = [qubits]
instruction = {"name": "unitary", "qubits": qubits, "params": [mat]}
if standard_gates:
return standard_gate_instruction(instruction)
else:
return [instruction]
def __init__(self, data, label=None):
"""Create a gate from a numeric unitary matrix.
Args:
data (matrix or Operator): unitary operator.
label (str): unitary name for backend [Default: None].
Raises:
ExtensionError: if input data is not an N-qubit unitary operator.
"""
if hasattr(data, 'to_matrix'):
# If input is Gate subclass or some other class object that has
# a to_matrix method this will call that method.
data = data.to_matrix()
elif hasattr(data, 'to_operator'):
# If input is a BaseOperator subclass this attempts to convert
# the object to an Operator so that we can extract the underlying
# numpy matrix from `Operator.data`.
data = data.to_operator().data
# Convert to numpy array in case not already an array
data = numpy.array(data, dtype=complex)
# Check input is unitary
if not is_unitary_matrix(data):
raise ExtensionError("Input matrix is not unitary.")
# Check input is N-qubit matrix
input_dim, output_dim = data.shape
num_qubits = int(numpy.log2(input_dim))
if input_dim != output_dim or 2**num_qubits != input_dim:
raise ExtensionError("Input matrix is not an N-qubit operator.")
self._qasm_name = None
self._qasm_definition = None
self._qasm_def_written = False
# Store instruction params
super().__init__('unitary', num_qubits, [data], label=label)
def is_unitary(self, atol=None, rtol=None):
"""Return True if operator is a unitary matrix."""
if atol is None:
atol = self.atol
if rtol is None:
rtol = self.rtol
return is_unitary_matrix(self._data, rtol=rtol, atol=atol)
def make_unitary_instruction(mat, qubits, standard_gates=True):
"""Return a qobj instruction for a unitary matrix gate.
Args:
mat (matrix): A square or diagonal unitary matrix.
qubits (list[int]): The qubits the matrix is applied to.
standard_gates (bool): Check if the matrix instruction is a
standard instruction.
Returns:
dict: The qobj instruction object.
Raises:
NoiseError: if the input is not a unitary matrix.
"""
if not is_unitary_matrix(mat):
raise NoiseError("Input matrix is not unitary.")
if isinstance(qubits, int):
qubits = [qubits]
instruction = {"name": "unitary", "qubits": qubits, "params": [mat]}
if standard_gates:
return standard_gate_instruction(instruction)
else:
return [instruction]
def __call__(self, unitary, simplify=True, atol=DEFAULT_ATOL):
"""Decompose single qubit gate into a circuit.
Args:
unitary (Operator or Gate or array): 1-qubit unitary matrix
simplify (bool): reduce gate count in decomposition [Default: True].
atol (float): absolute tolerance for checking angles when simplifying
returned circuit [Default: 1e-12].
Returns:
QuantumCircuit: the decomposed single-qubit gate circuit
Raises:
QiskitError: if input is invalid or synthesis fails.
"""
if hasattr(unitary, "to_operator"):
# If input is a BaseOperator subclass this attempts to convert
# the object to an Operator so that we can extract the underlying
# numpy matrix from `Operator.data`.
unitary = unitary.to_operator().data
elif hasattr(unitary, "to_matrix"):
# If input is Gate subclass or some other class object that has
# a to_matrix method this will call that method.
unitary = unitary.to_matrix()
# Convert to numpy array in case not already an array
unitary = np.asarray(unitary, dtype=complex)
# Check input is a 2-qubit unitary
if unitary.shape != (2, 2):
raise QiskitError(
"OneQubitEulerDecomposer: expected 2x2 input matrix")
if not is_unitary_matrix(unitary):
raise QiskitError(
"OneQubitEulerDecomposer: input matrix is not unitary.")
return self._decompose(unitary, simplify=simplify, atol=atol)
def addCustomGates():
receivedDictionary=request.get_json()
matrix=receivedDictionary["matrix"]
matrix=f.strToComplex(matrix)
isUnitary=is_unitary_matrix(matrix)
if isUnitary:
matrix=c.reversedMatrix(matrix,int(log2(len(matrix))))
c.gatesObjects[receivedDictionary["gateName"]]=f.matrixToGateObject(matrix,receivedDictionary["gateName"])
return jsonify({"isUnitary":isUnitary})
def __call__(self, target, basis_fidelity=None):
"""Decompose a two-qubit unitary over fixed basis + SU(2) using the best approximation given
that each basis application has a finite fidelity.
"""
basis_fidelity = basis_fidelity or self.basis_fidelity
if hasattr(target, 'to_operator'):
# If input is a BaseOperator subclass this attempts to convert
# the object to an Operator so that we can extract the underlying
# numpy matrix from `Operator.data`.
target = target.to_operator().data
if hasattr(target, 'to_matrix'):
# If input is Gate subclass or some other class object that has
# a to_matrix method this will call that method.
target = target.to_matrix()
# Convert to numpy array incase not already an array
target = np.asarray(target, dtype=complex)
# Check input is a 2-qubit unitary
if target.shape != (4, 4):
raise QiskitError(
"TwoQubitBasisDecomposer: expected 4x4 matrix for target")
if not is_unitary_matrix(target):
raise QiskitError(
"TwoQubitBasisDecomposer: target matrix is not unitary.")
target_decomposed = TwoQubitWeylDecomposition(target)
traces = self.traces(target_decomposed)
expected_fidelities = [
trace_to_fid(traces[i]) * basis_fidelity**i for i in range(4)
]
best_nbasis = np.argmax(expected_fidelities)
decomposition = self.decomposition_fns[best_nbasis](target_decomposed)
decomposition_euler = [
self._decomposer1q._decompose(x) for x in decomposition
]
q = QuantumRegister(2)
return_circuit = QuantumCircuit(q)
return_circuit.global_phase = target_decomposed.global_phase
return_circuit.global_phase -= best_nbasis * self.basis.global_phase
if best_nbasis == 2:
return_circuit.global_phase += np.pi
for i in range(best_nbasis):
return_circuit.compose(decomposition_euler[2 * i], [q[0]],
inplace=True)
return_circuit.compose(decomposition_euler[2 * i + 1], [q[1]],
inplace=True)
return_circuit.append(self.gate, [q[0], q[1]])
return_circuit.compose(decomposition_euler[2 * best_nbasis], [q[0]],
inplace=True)
return_circuit.compose(decomposition_euler[2 * best_nbasis + 1],
[q[1]],
inplace=True)
return return_circuit
def __init__(self, u, mode="ZYZ", up_to_diagonal=False):
if mode != "ZYZ":
raise QiskitError("The decomposition mode is not known.")
# Check if the matrix u has the right dimensions and if it is a unitary
if not u.shape == (2, 2):
raise QiskitError("The dimension of the input matrix is not equal to (2,2).")
if not is_unitary_matrix(u):
raise QiskitError("The 2*2 matrix is not unitary.")
self.mode = mode
self.up_to_diagonal = up_to_diagonal
# Create new gate
super().__init__("unitary", 1, [u])
def mixed_unitary_error(noise_ops, standard_gates=True):
"""
Mixed unitary quantum error channel.
The input should be a list of pairs (U[j], p[j]), where `U[j]` is a
unitary matrix and `p[j]` is a probability. All probabilities must
sum to 1 for the input ops to be valid.
Args:
noise_ops (list[pair[matrix, double]]): unitary error matricies.
standard_gates (bool): Check if input matrices are standard gates.
Returns:
QuantumError: The quantum error object.
Raises:
NoiseError: if error parameters are invalid.
"""
# Error checking
if not isinstance(noise_ops, (list, tuple, zip)):
raise NoiseError("Input noise ops is not a list.")
# Convert to numpy arrays
noise_ops = [(np.array(op, dtype=complex), p) for op, p in noise_ops]
if not noise_ops:
raise NoiseError("Input noise list is empty.")
# Check for identity unitaries
prob_identity = 0.
instructions = []
instructions_probs = []
num_qubits = qubits_from_mat(noise_ops[0][0])
qubits = list(range(num_qubits))
for unitary, prob in noise_ops:
# Check unitary
if qubits_from_mat(unitary) != num_qubits:
raise NoiseError("Input matrices different size.")
if not is_unitary_matrix(unitary):
raise NoiseError("Input matrix is not unitary.")
if is_identity_matrix(unitary):
prob_identity += prob
else:
instr = make_unitary_instruction(unitary,
qubits,
standard_gates=standard_gates)
instructions.append(instr)
instructions_probs.append(prob)
if prob_identity > 0:
instructions.append([{"name": "id", "qubits": [0]}])
instructions_probs.append(prob_identity)
return QuantumError(zip(instructions, instructions_probs))
def __init__(self, unitary_matrix, mode='ZYZ', up_to_diagonal=False, u=None):
"""Create a new single qubit gate based on the unitary ``u``."""
if mode not in ['ZYZ']:
raise QiskitError("The decomposition mode is not known.")
# Check if the matrix u has the right dimensions and if it is a unitary
if unitary_matrix.shape != (2, 2):
raise QiskitError("The dimension of the input matrix is not equal to (2,2).")
if not is_unitary_matrix(unitary_matrix):
raise QiskitError("The 2*2 matrix is not unitary.")
self.mode = mode
self.up_to_diagonal = up_to_diagonal
self._diag = None
# Create new gate
super().__init__("unitary", 1, [unitary_matrix])
def unitary_from_hermitian(hermitian):
"""Generates a unitary matrix from a hermitian matrix.
The formula is U = e^(i*H).
Args:
hermitian: A hermitian matrix.
Returns:
unitary: The resulting unitarian matrix.
Raises:
AssertionError: If the resulting matrix is not unitarian.
"""
unitary = np.matrix(expm(1j * hermitian))
assert is_unitary_matrix(unitary)
return unitary
def test_controlled_unitary(self, num_ctrl_qubits):
"""test controlled unitary"""
num_target = 1
q_target = QuantumRegister(num_target)
qc1 = QuantumCircuit(q_target)
# for h-rx(pi/2)
theta, phi, lamb = 1.57079632679490, 0.0, 4.71238898038469
qc1.u3(theta, phi, lamb, q_target[0])
base_gate = qc1.to_gate()
# get UnitaryGate version of circuit
base_op = Operator(qc1)
base_mat = base_op.data
cgate = base_gate.control(num_ctrl_qubits)
test_op = Operator(cgate)
cop_mat = _compute_control_matrix(base_mat, num_ctrl_qubits)
self.assertTrue(is_unitary_matrix(base_mat))
self.assertTrue(matrix_equal(cop_mat, test_op.data, ignore_phase=True))
def subCircuitCustomGate():
if request.method=='POST':
receivedDictionary=request.get_json()
c2=Circuit()
c2.gatesObjects=c.gatesObjects
c2.subCircuitSetter(receivedDictionary)
try:
circuit=c2.createDraggableCircuit()
except Exception as e:
return jsonify({"conditionalLoopError":str(e)})
r=Results(circuit)
matrix=r.matrixRepresentation()
complexMatrix=f.strToComplex(matrix)
isUnitary=is_unitary_matrix(complexMatrix)
if isUnitary:
c.gatesObjects[receivedDictionary["gateName"]]=f.matrixToGateObject(complexMatrix,receivedDictionary["gateName"])
return jsonify({"isUnitary":isUnitary})
def __init__(self, gate_list, up_to_diagonal=False):
"""UCGate Gate initializer.
Args:
gate_list (list[ndarray]): list of two qubit unitaries [U_0,...,U_{2^k-1}],
where each single-qubit unitary U_i is given as a 2*2 numpy array.
up_to_diagonal (bool): determines if the gate is implemented up to a diagonal.
or if it is decomposed completely (default: False).
If the UCGate u is decomposed up to a diagonal d, this means that the circuit
implements a unitary u' such that d.u'=u.
Raises:
QiskitError: in case of bad input to the constructor
"""
# check input format
if not isinstance(gate_list, list):
raise QiskitError(
"The single-qubit unitaries are not provided in a list.")
for gate in gate_list:
if not gate.shape == (2, 2):
raise QiskitError(
"The dimension of a controlled gate is not equal to (2,2)."
)
if not gate_list:
raise QiskitError("The gate list cannot be empty.")
# Check if number of gates in gate_list is a positive power of two
num_contr = math.log2(len(gate_list))
if num_contr < 0 or not num_contr.is_integer():
raise QiskitError(
"The number of controlled single-qubit gates is not a "
"non-negative power of 2.")
# Check if the single-qubit gates are unitaries
for gate in gate_list:
if not is_unitary_matrix(gate, _EPS):
raise QiskitError("A controlled gate is not unitary.")
# Create new gate.
super().__init__("multiplexer", int(num_contr) + 1, gate_list)
self.up_to_diagonal = up_to_diagonal
def __call__(self, unitary_mat, simplify=True, atol=DEFAULT_ATOL):
"""Decompose single qubit gate into a circuit.
Args:
unitary_mat (array_like): 1-qubit unitary matrix
simplify (bool): remove zero-angle rotations [Default: True]
atol (float): absolute tolerance for checking angles zero.
Returns:
QuantumCircuit: the decomposed single-qubit gate circuit
Raises:
QiskitError: if input is invalid or synthesis fails.
"""
if hasattr(unitary_mat, 'to_operator'):
# If input is a BaseOperator subclass this attempts to convert
# the object to an Operator so that we can extract the underlying
# numpy matrix from `Operator.data`.
unitary_mat = unitary_mat.to_operator().data
if hasattr(unitary_mat, 'to_matrix'):
# If input is Gate subclass or some other class object that has
# a to_matrix method this will call that method.
unitary_mat = unitary_mat.to_matrix()
# Convert to numpy array incase not already an array
unitary_mat = np.asarray(unitary_mat, dtype=complex)
# Check input is a 2-qubit unitary
if unitary_mat.shape != (2, 2):
raise QiskitError("OneQubitEulerDecomposer: "
"expected 2x2 input matrix")
if not is_unitary_matrix(unitary_mat):
raise QiskitError("OneQubitEulerDecomposer: "
"input matrix is not unitary.")
circuit = self._circuit(unitary_mat, simplify=simplify, atol=atol)
# Check circuit is correct
if not Operator(circuit).equiv(Operator(unitary_mat)):
raise QiskitError("OneQubitEulerDecomposer: "
"synthesis failed within required accuracy.")
return circuit
def _make_unitary_instruction(mat, qubits, standard_gates=True):
"""Temporary function to create Instruction objects from a unitary matrix,
which is necessary for creating a new QuantumError with the deprecated
standard_gates option. Note that the type of returned object is different from
the deprecated make_unitary_instruction.
TODO: to be removed after deprecation period.
Args:
mat (matrix): A square or diagonal unitary matrix.
qubits (list[int]): The qubits the matrix is applied to.
standard_gates (bool): Check if the matrix instruction is a
standard instruction.
Returns:
list: The list of instructions.
Raises:
NoiseError: if the input is not a unitary matrix.
"""
if not is_unitary_matrix(mat):
raise NoiseError("Input matrix is not unitary.")
if isinstance(qubits, int):
qubits = [qubits]
instruction = {"name": "unitary", "qubits": qubits, "params": [mat]}
if standard_gates:
if isinstance(instruction, dict):
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
category=DeprecationWarning,
module="qiskit.providers.aer.noise.errors.errorutils")
res = _standard_gate_instruction(instruction)
else:
res = [instruction]
return res
else:
return [instruction]
def kraus2instructions(kraus_ops, standard_gates, atol=ATOL_DEFAULT):
"""
Convert a list of Kraus matrices into qobj circuits.
If any Kraus operators are a unitary matrix they will be converted
into unitary qobj instructions. Identity unitary matrices will also be
converted into identity qobj instructions.
Args:
kraus_ops (list[matrix]): A list of Kraus matrices for a CPTP map.
standard_gates (bool): Check if the matrix instruction is a
standard instruction (default: True).
atol (double): Threshold for testing if probabilities are zero.
Returns:
list: A list of pairs (p, circuit) where `circuit` is a list of qobj
instructions, and `p` is the probability of that circuit for the
given error.
Raises:
NoiseError: If the input Kraus channel is not CPTP.
"""
# Check threshold
if atol < 0:
raise NoiseError("atol cannot be negative")
if atol > 1e-5:
raise NoiseError(
"atol value is too large. It should be close to zero.")
# Check CPTP
if not Kraus(kraus_ops).is_cptp(atol=atol):
raise NoiseError("Input Kraus channel is not CPTP.")
# Get number of qubits
num_qubits = int(np.log2(len(kraus_ops[0])))
if len(kraus_ops[0]) != 2**num_qubits:
raise NoiseError("Input Kraus channel is not a multi-qubit channel.")
# Check if each matrix is a:
# 1. scaled identity matrix
# 2. scaled non-identity unitary matrix
# 3. a non-unitary Kraus operator
# Probabilities
prob_identity = 0
prob_unitary = 0 # total probability of all unitary ops (including id)
prob_kraus = 0 # total probability of non-unitary ops
probabilities = [] # initialize with probability of Identity
# Matrices
unitaries = [] # non-identity unitaries
non_unitaries = [] # non-unitary Kraus matrices
for mat in kraus_ops:
# Get the value of the maximum diagonal element
# of op.H * op for rescaling
prob = abs(max(np.diag(np.conj(np.transpose(mat)).dot(mat))))
if prob > 0.0:
if abs(prob - 1) > 0.0:
# Rescale the operator by square root of prob
rescaled_mat = np.array(mat) / np.sqrt(prob)
else:
rescaled_mat = mat
# Check if identity operator
if is_identity_matrix(rescaled_mat, ignore_phase=True):
prob_identity += prob
prob_unitary += prob
# Check if unitary
elif is_unitary_matrix(rescaled_mat):
probabilities.append(prob)
prob_unitary += prob
unitaries.append(rescaled_mat)
# Non-unitary op
else:
non_unitaries.append(mat)
# Check probabilities
prob_kraus = 1 - prob_unitary
if prob_unitary - 1 > atol:
raise NoiseError("Invalid kraus matrices: unitary probability "
"{} > 1".format(prob_unitary))
if prob_unitary < -atol:
raise NoiseError("Invalid kraus matrices: unitary probability "
"{} < 1".format(prob_unitary))
if prob_identity - 1 > atol:
raise NoiseError("Invalid kraus matrices: identity probability "
"{} > 1".format(prob_identity))
if prob_identity < -atol:
raise NoiseError("Invalid kraus matrices: identity probability "
"{} < 1".format(prob_identity))
if prob_kraus - 1 > atol:
raise NoiseError("Invalid kraus matrices: non-unitary probability "
"{} > 1".format(prob_kraus))
if prob_kraus < -atol:
raise NoiseError("Invalid kraus matrices: non-unitary probability "
"{} < 1".format(prob_kraus))
# Build qobj instructions
instructions = []
qubits = list(range(num_qubits))
# Add unitary instructions
for unitary in unitaries:
instructions.append(
make_unitary_instruction(
unitary, qubits, standard_gates=standard_gates))
# Add identity instruction
if prob_identity > atol:
if abs(prob_identity - 1) < atol:
probabilities.append(1)
else:
probabilities.append(prob_identity)
instructions.append([{"name": "id", "qubits": [0]}])
# Add Kraus
if prob_kraus < atol:
# No Kraus operators
return zip(instructions, probabilities)
if prob_kraus < 1:
# Rescale kraus operators by probabilities
non_unitaries = [
np.array(op) / np.sqrt(prob_kraus) for op in non_unitaries
]
instructions.append(make_kraus_instruction(non_unitaries, qubits))
probabilities.append(prob_kraus)
# Normalize probabilities to account for any rounding errors
probabilities = list(np.array(probabilities) / np.sum(probabilities))
return zip(instructions, probabilities)
def mixed_unitary_error(noise_ops, standard_gates=None):
"""
Return a mixed unitary quantum error channel.
The input should be a list of pairs ``(U[j], p[j])``, where
``U[j]`` is a unitary matrix and ``p[j]`` is a probability. All
probabilities must sum to 1 for the input ops to be valid.
Args:
noise_ops (list[pair[matrix, double]]): unitary error matrices.
standard_gates (bool): DEPRECATED, Check if input matrices are standard gates.
Returns:
QuantumError: The quantum error object.
Raises:
NoiseError: if error parameters are invalid.
"""
if standard_gates is not None:
warnings.warn(
'"standard_gates" option has been deprecated as of qiskit-aer 0.10.0'
' and will be removed no earlier than 3 months from that release date.'
' Use directly init e.g. QuantumError([(IGate(), prob1), (ZGate(), prob2)]) instead.',
DeprecationWarning,
stacklevel=2)
# Error checking
if not isinstance(noise_ops, (list, tuple, zip)):
raise NoiseError("Input noise ops is not a list.")
# Convert to numpy arrays
noise_ops = [(np.array(op, dtype=complex), p) for op, p in noise_ops]
if not noise_ops:
raise NoiseError("Input noise list is empty.")
# Check for identity unitaries
prob_identity = 0.
instructions = []
instructions_probs = []
num_qubits = int(np.log2(noise_ops[0][0].shape[0]))
if noise_ops[0][0].shape != (2**num_qubits, 2**num_qubits):
raise NoiseError(
"A unitary matrix in input noise_ops is not a multi-qubit matrix.")
for unitary, prob in noise_ops:
# Check unitary
if unitary.shape != noise_ops[0][0].shape:
raise NoiseError("Input matrices different size.")
if not is_unitary_matrix(unitary):
raise NoiseError("Input matrix is not unitary.")
if is_identity_matrix(unitary):
prob_identity += prob
else:
if standard_gates: # TODO: to be removed after deprecation period
qubits = list(range(num_qubits))
instr = _make_unitary_instruction(
unitary, qubits, standard_gates=standard_gates)
else:
instr = UnitaryGate(unitary)
instructions.append(instr)
instructions_probs.append(prob)
if prob_identity > 0:
if standard_gates: # TODO: to be removed after deprecation period
instructions.append([{"name": "id", "qubits": [0]}])
else:
instructions.append(IGate())
instructions_probs.append(prob_identity)
return QuantumError(zip(instructions, instructions_probs))
def is_unitary(self):
"""Return True if operator is a unitary matrix."""
return is_unitary_matrix(self._data, rtol=self._rtol, atol=self._atol)
def two_qubit_kak(unitary):
"""Decompose a two-qubit gate over SU(2)+CNOT using the KAK decomposition.
Args:
unitary (Operator): a 4x4 unitary operator to decompose.
Returns:
QuantumCircuit: a circuit implementing the unitary over SU(2)+CNOT
Raises:
QiskitError: input not a unitary, or error in KAK decomposition.
"""
if hasattr(unitary, 'to_operator'):
# If input is a BaseOperator subclass this attempts to convert
# the object to an Operator so that we can extract the underlying
# numpy matrix from `Operator.data`.
unitary = unitary.to_operator().data
if hasattr(unitary, 'to_matrix'):
# If input is Gate subclass or some other class object that has
# a to_matrix method this will call that method.
unitary = unitary.to_matrix()
# Convert to numpy array incase not already an array
unitary_matrix = np.array(unitary, dtype=complex)
# Check input is a 2-qubit unitary
if unitary_matrix.shape != (4, 4):
raise QiskitError("two_qubit_kak: Expected 4x4 matrix")
if not is_unitary_matrix(unitary_matrix):
raise QiskitError("Input matrix is not unitary.")
phase = la.det(unitary_matrix)**(-1.0 / 4.0)
# Make it in SU(4), correct phase at the end
U = phase * unitary_matrix
# B changes to the Bell basis
B = (1.0 / math.sqrt(2)) * np.array(
[[1, 1j, 0, 0], [0, 0, 1j, 1], [0, 0, 1j, -1], [1, -1j, 0, 0]],
dtype=complex)
# We also need B.conj().T below
Bdag = B.conj().T
# U' = Bdag . U . B
Uprime = Bdag.dot(U.dot(B))
# M^2 = trans(U') . U'
M2 = Uprime.T.dot(Uprime)
# Diagonalize M2
# Must use diagonalization routine which finds a real orthogonal matrix P
# when M2 is real.
D, P = la.eig(M2)
D = np.diag(D)
# If det(P) == -1 then in O(4), apply a swap to make P in SO(4)
if abs(la.det(P) + 1) < 1e-5:
swap = np.array(
[[1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1]],
dtype=complex)
P = P.dot(swap)
D = swap.dot(D.dot(swap))
Q = np.sqrt(D) # array from elementwise sqrt
# Want to take square root so that Q has determinant 1
if abs(la.det(Q) + 1) < 1e-5:
Q[0, 0] = -Q[0, 0]
# Q^-1*P.T = P' -> QP' = P.T (solve for P' using Ax=b)
Pprime = la.solve(Q, P.T)
# K' now just U' * P * P'
Kprime = Uprime.dot(P.dot(Pprime))
K1 = B.dot(Kprime.dot(P.dot(Bdag)))
A = B.dot(Q.dot(Bdag))
K2 = B.dot(P.T.dot(Bdag))
# KAK = K1 * A * K2
KAK = K1.dot(A.dot(K2))
# Verify decomp matches input unitary.
if la.norm(KAK - U) > 1e-6:
raise QiskitError("two_qubit_kak: KAK decomposition " +
"does not return input unitary.")
# Compute parameters alpha, beta, gamma so that
# A = exp(i * (alpha * XX + beta * YY + gamma * ZZ))
xx = np.array([[0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 0, 0]],
dtype=complex)
yy = np.array([[0, 0, 0, -1], [0, 0, 1, 0], [0, 1, 0, 0], [-1, 0, 0, 0]],
dtype=complex)
zz = np.array([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]],
dtype=complex)
A_real_tr = A.real.trace()
alpha = math.atan2(A.dot(xx).imag.trace(), A_real_tr)
beta = math.atan2(A.dot(yy).imag.trace(), A_real_tr)
gamma = math.atan2(A.dot(zz).imag.trace(), A_real_tr)
# K1 = kron(U1, U2) and K2 = kron(V1, V2)
# Find the matrices U1, U2, V1, V2
# Find a block in K1 where U1_ij * [U2] is not zero
L = K1[0:2, 0:2]
if la.norm(L) < 1e-9:
L = K1[0:2, 2:4]
if la.norm(L) < 1e-9:
L = K1[2:4, 2:4]
# Remove the U1_ij prefactor
Q = L.dot(L.conj().T)
U2 = L / math.sqrt(Q[0, 0].real)
# Now grab U1 given we know U2
R = K1.dot(np.kron(np.identity(2), U2.conj().T))
U1 = np.zeros((2, 2), dtype=complex)
U1[0, 0] = R[0, 0]
U1[0, 1] = R[0, 2]
U1[1, 0] = R[2, 0]
U1[1, 1] = R[2, 2]
# Repeat K1 routine for K2
L = K2[0:2, 0:2]
if la.norm(L) < 1e-9:
L = K2[0:2, 2:4]
if la.norm(L) < 1e-9:
L = K2[2:4, 2:4]
Q = np.dot(L, np.transpose(L.conjugate()))
V2 = L / np.sqrt(Q[0, 0])
R = np.dot(K2, np.kron(np.identity(2), np.transpose(V2.conjugate())))
V1 = np.zeros_like(U1)
V1[0, 0] = R[0, 0]
V1[0, 1] = R[0, 2]
V1[1, 0] = R[2, 0]
V1[1, 1] = R[2, 2]
if la.norm(np.kron(U1, U2) - K1) > 1e-4:
raise QiskitError("two_qubit_kak: K1 != U1 x U2")
if la.norm(np.kron(V1, V2) - K2) > 1e-4:
raise QiskitError("two_qubit_kak: K2 != V1 x V2")
test = la.expm(1j * (alpha * xx + beta * yy + gamma * zz))
if la.norm(A - test) > 1e-4:
raise QiskitError("two_qubit_kak: " +
"Matrix A does not match xx,yy,zz decomposition.")
# Circuit that implements K1 * A * K2 (up to phase), using
# Vatan and Williams Fig. 6 of quant-ph/0308006v3
# Include prefix and suffix single-qubit gates into U2, V1 respectively.
V2 = np.array([[np.exp(1j * np.pi / 4), 0], [0, np.exp(-1j * np.pi / 4)]],
dtype=complex).dot(V2)
U1 = U1.dot(
np.array([[np.exp(-1j * np.pi / 4), 0], [0, np.exp(1j * np.pi / 4)]],
dtype=complex))
# Corrects global phase: exp(ipi/4)*phase'
U1 = U1.dot(
np.array([[np.exp(1j * np.pi / 4), 0], [0, np.exp(1j * np.pi / 4)]],
dtype=complex))
U1 = phase.conjugate() * U1
# Test
g1 = np.kron(V1, V2)
g2 = np.array([[1, 0, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0]],
dtype=complex)
theta = 2 * gamma - np.pi / 2
Ztheta = np.array(
[[np.exp(1j * theta / 2), 0], [0, np.exp(-1j * theta / 2)]],
dtype=complex)
kappa = np.pi / 2 - 2 * alpha
Ykappa = np.array(
[[math.cos(kappa / 2), math.sin(kappa / 2)],
[-math.sin(kappa / 2), math.cos(kappa / 2)]],
dtype=complex)
g3 = np.kron(Ztheta, Ykappa)
g4 = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0]],
dtype=complex)
zeta = 2 * beta - np.pi / 2
Yzeta = np.array(
[[math.cos(zeta / 2), math.sin(zeta / 2)],
[-math.sin(zeta / 2), math.cos(zeta / 2)]],
dtype=complex)
g5 = np.kron(np.identity(2), Yzeta)
g6 = g2
g7 = np.kron(U1, U2)
V = g2.dot(g1)
V = g3.dot(V)
V = g4.dot(V)
V = g5.dot(V)
V = g6.dot(V)
V = g7.dot(V)
if la.norm(V - U * phase.conjugate()) > 1e-6:
raise QiskitError("two_qubit_kak: " +
"sequence incorrect, unknown error")
v1_param = euler_angles_1q(V1)
v2_param = euler_angles_1q(V2)
u1_param = euler_angles_1q(U1)
u2_param = euler_angles_1q(U2)
v1_gate = U3Gate(v1_param[0], v1_param[1], v1_param[2])
v2_gate = U3Gate(v2_param[0], v2_param[1], v2_param[2])
u1_gate = U3Gate(u1_param[0], u1_param[1], u1_param[2])
u2_gate = U3Gate(u2_param[0], u2_param[1], u2_param[2])
q = QuantumRegister(2)
return_circuit = QuantumCircuit(q)
return_circuit.append(v1_gate, [q[1]])
return_circuit.append(v2_gate, [q[0]])
return_circuit.append(CnotGate(), [q[0], q[1]])
gate = U3Gate(0.0, 0.0, -2.0 * gamma + np.pi / 2.0)
return_circuit.append(gate, [q[1]])
gate = U3Gate(-np.pi / 2.0 + 2.0 * alpha, 0.0, 0.0)
return_circuit.append(gate, [q[0]])
return_circuit.append(CnotGate(), [q[1], q[0]])
gate = U3Gate(-2.0 * beta + np.pi / 2.0, 0.0, 0.0)
return_circuit.append(gate, [q[0]])
return_circuit.append(CnotGate(), [q[0], q[1]])
return_circuit.append(u1_gate, [q[1]])
return_circuit.append(u2_gate, [q[0]])
return return_circuit
def is_cptp(self):
"""Return True if completely-positive trace-preserving."""
# If the matrix is a unitary matrix the channel is CPTP
return is_unitary_matrix(self._data, rtol=self.rtol, atol=self.atol)