def test_init_multiple_digits(self):
"""Test __init__ for sparse label with multiple digits"""
actual = SpinOp([("X_10^20", 1 + 2j), ("X_12^34", 56)],
Fraction(5, 2),
register_length=13)
desired = [("X_10^20", 1 + 2j), ("X_12^34", 56)]
self.assertListEqual(actual.to_list(), desired)
def test_init_label(self, label, pre_processing):
"""Test __init__"""
spin = SpinOp(pre_processing(label), register_length=len(label) // 3)
expected_label = " ".join(lb for lb in label.split() if lb[0] != "I")
if not expected_label:
expected_label = f"I_{len(label) // 3 - 1}"
self.assertListEqual(spin.to_list(), [(expected_label, 1)])
self.assertSpinEqual(eval(repr(spin)), spin) # pylint: disable=eval-used
def map(self, second_q_op: SpinOp) -> PauliSumOp:
"""Map spins to qubits using the Logarithmic encoding.
Args:
second_q_op: Spins mapped to qubits.
Returns:
Qubit operators generated by the Logarithmic encoding
"""
qubit_ops_list: List[PauliSumOp] = []
# get logarithmic encoding of the general spin matrices.
spinx, spiny, spinz, identity = self._logarithmic_encoding(
second_q_op.spin)
for idx, (_, coeff) in enumerate(second_q_op.to_list()):
operatorlist: List[PauliSumOp] = []
for n_x, n_y, n_z in zip(second_q_op.x[idx], second_q_op.y[idx],
second_q_op.z[idx]):
operator_on_spin_i: List[PauliSumOp] = []
if n_x > 0:
operator_on_spin_i.append(
reduce(operator.matmul, [spinx] * int(n_x)))
if n_y > 0:
operator_on_spin_i.append(
reduce(operator.matmul, [spiny] * int(n_y)))
if n_z > 0:
operator_on_spin_i.append(
reduce(operator.matmul, [spinz] * int(n_z)))
if operator_on_spin_i:
single_operator_on_spin_i = reduce(operator.matmul,
operator_on_spin_i)
operatorlist.append(single_operator_on_spin_i)
else:
# If n_x=n_y=n_z=0, simply add the embedded Identity operator.
operatorlist.append(identity)
# Now, we can tensor all operators in this list
qubit_ops_list.append(coeff *
reduce(operator.xor, reversed(operatorlist)))
qubit_op = reduce(operator.add, qubit_ops_list)
return qubit_op
def map(self, second_q_op: SpinOp) -> PauliSumOp:
qubit_ops_list: List[PauliSumOp] = []
# get linear encoding of the general spin matrices
spinx, spiny, spinz, identity = self._linear_encoding(second_q_op.spin)
for idx, (_, coeff) in enumerate(second_q_op.to_list()):
operatorlist: List[PauliSumOp] = []
for n_x, n_y, n_z in zip(second_q_op.x[idx], second_q_op.y[idx],
second_q_op.z[idx]):
operator_on_spin_i: List[PauliSumOp] = []
if n_x > 0:
operator_on_spin_i.append(
reduce(operator.matmul, [spinx] * int(n_x)))
if n_y > 0:
operator_on_spin_i.append(
reduce(operator.matmul, [spiny] * int(n_y)))
if n_z > 0:
operator_on_spin_i.append(
reduce(operator.matmul, [spinz] * int(n_z)))
if np.any([n_x, n_y, n_z]) > 0:
single_operator_on_spin_i = reduce(operator.matmul,
operator_on_spin_i)
operatorlist.append(single_operator_on_spin_i.reduce())
else:
# If n_x=n_y=n_z=0, simply add the embedded Identity operator.
operatorlist.append(identity)
# Now, we can tensor all operators in this list
# NOTE: in Qiskit's opflow the `XOR` (i.e. `^`) operator does the tensor product
qubit_ops_list.append(coeff *
reduce(operator.xor, reversed(operatorlist)))
qubit_op = reduce(operator.add, qubit_ops_list)
return qubit_op