def number_operator(fer_op, mode_number=None):
"""Find the qubit operator for the number operator in bravyi_kitaev_fast representation.
Args:
fer_op (FermionicOperator): the fermionic operator in the second quanitzed form
mode_number (int): index, it corresponds to the mode for which number operator is required.
Returns:
Operator: the qubit operator
"""
modes = fer_op.h1.modes
edge_list = bravyi_kitaev_fast_edge_list(fer_op)
num_qubits = edge_list.shape[1]
num_operator = Operator(paulis=[[1.0, Pauli.from_label('I' * num_qubits)]])
if mode_number is None:
for i in range(modes):
num_operator -= edge_operator_bi(edge_list, i)
num_operator += Operator(
paulis=[[1.0 *
modes, Pauli.from_label('I' * num_qubits)]])
else:
num_operator += (
Operator(paulis=[[1.0, Pauli.from_label('I' * num_qubits)]]) -
edge_operator_bi(edge_list, mode_number))
num_operator.scaling_coeff(0.5)
return num_operator
def number_operator(fer_op, mode_number=None):
"""Find the number operator in BKSF representation.
This operator can be used to examine the number of particle in
a given eigenstate.
If `mode_number` is None, it checks how many particles in the eigenstate.
If `mode_number` is not None, it will only check whether or not
that particle at `mode_number` in the eigenstate.
Args:
fer_op (FermionicOperator): the fermionic operator in the second quanitzed form
mode_number (int): index, it corresponds to the mode for which number operator is required.
Returns:
Operator: the qubit operator
"""
modes = fer_op.h1.modes
edge_list = bksf_edge_list(fer_op)
num_qubits = edge_list.shape[1]
num_operator = Operator(paulis=[[1.0, label_to_pauli('I' * num_qubits)]])
if mode_number is None:
for i in range(modes):
num_operator -= edge_operator_bi(edge_list, i)
num_operator += Operator(
paulis=[[1.0 *
modes, label_to_pauli('I' * num_qubits)]])
else:
num_operator += (
Operator(paulis=[[1.0, label_to_pauli('I' * num_qubits)]]) -
edge_operator_bi(edge_list, mode_number))
num_operator.scaling_coeff(0.5)
return num_operator
def vacuum_operator(fer_op):
"""Use the stabilizers to find the vacuum state in bravyi_kitaev_fast.
Args:
fer_op (FermionicOperator): the fermionic operator in the second quanitzed form
Returns:
Operator: the qubit operator
"""
edge_list = bravyi_kitaev_fast_edge_list(fer_op)
num_qubits = edge_list.shape[1]
vac_operator = Operator(paulis=[[1.0, Pauli.from_label('I' * num_qubits)]])
g = networkx.Graph()
g.add_edges_from(tuple(edge_list.transpose()))
stabs = np.asarray(networkx.cycle_basis(g))
for stab in stabs:
a = Operator(paulis=[[1.0, Pauli.from_label('I' * num_qubits)]])
stab = np.asarray(stab)
for i in range(np.size(stab)):
a = a * edge_operator_aij(edge_list, stab[i],
stab[(i + 1) % np.size(stab)])
a.scaling_coeff(1j)
a += Operator(paulis=[[1.0, Pauli.from_label('I' * num_qubits)]])
vac_operator = vac_operator * a
vac_operator.scaling_coeff(np.sqrt(2))
return vac_operator
def vacuum_operator(fer_op):
"""Use the stabilizers to find the vacuum state in BKSF.
This operator can be used to generate the vaccum state for
BKSF mapping.
Upon having this operator, operate it on `orignal` vaccum state |000...>,
and resulted state is the vacuum state for bksf mapping.
Args:
fer_op (FermionicOperator): the fermionic operator in the second quanitzed form
Returns:
Operator: the qubit operator
"""
edge_list = bksf_edge_list(fer_op)
num_qubits = edge_list.shape[1]
vac_operator = Operator(paulis=[[1.0, label_to_pauli('I' * num_qubits)]])
g = networkx.Graph()
g.add_edges_from(tuple(edge_list.transpose()))
stabs = np.asarray(networkx.cycle_basis(g))
for stab in stabs:
a = Operator(paulis=[[1.0, label_to_pauli('I' * num_qubits)]])
stab = np.asarray(stab)
for i in range(np.size(stab)):
a = a * edge_operator_aij(edge_list, stab[i],
stab[(i + 1) % np.size(stab)])
a.scaling_coeff(1j)
a += Operator(paulis=[[1.0, label_to_pauli('I' * num_qubits)]])
vac_operator = vac_operator * a
vac_operator.scaling_coeff(np.sqrt(2))
return vac_operator
def stabilizers(fer_op):
edge_list = bravyi_kitaev_fast_edge_list(fer_op)
num_qubits = edge_list.shape[1]
# vac_operator = Operator(paulis=[[1.0, Pauli.from_label('I' * num_qubits)]])
g = networkx.Graph()
g.add_edges_from(tuple(edge_list.transpose()))
stabs = np.asarray(networkx.cycle_basis(g))
stabilizers = []
for stab in stabs:
a = Operator(paulis=[[1.0, Pauli.from_label('I' * num_qubits)]])
stab = np.asarray(stab)
for i in range(np.size(stab)):
a = a * edge_operator_aij(edge_list, stab[i],
stab[(i + 1) % np.size(stab)])
a.scaling_coeff(1j)
stabilizers.append(a)
return stabilizers
def test_scaling_coeff(self):
"""
test scale
"""
pauli_a = 'IXYZ'
pauli_b = 'ZYIX'
coeff_a = 0.5
coeff_b = 0.5
pauli_term_a = [coeff_a, label_to_pauli(pauli_a)]
pauli_term_b = [coeff_b, label_to_pauli(pauli_b)]
opA = Operator(paulis=[pauli_term_a])
opB = Operator(paulis=[pauli_term_b])
opA += opB
self.assertEqual(2, len(opA.paulis))
opA.scaling_coeff(0.7)
self.assertEqual(2, len(opA.paulis))
self.assertEqual(0.35, opA.paulis[0][0])
def test_scaling_coeff(self):
"""
test scale
"""
pauli_a = 'IXYZ'
pauli_b = 'ZYIX'
coeff_a = 0.5
coeff_b = 0.5
pauli_term_a = [coeff_a, Pauli.from_label(pauli_a)]
pauli_term_b = [coeff_b, Pauli.from_label(pauli_b)]
op_a = Operator(paulis=[pauli_term_a])
op_b = Operator(paulis=[pauli_term_b])
op_a += op_b
self.assertEqual(2, len(op_a.paulis))
op_a.scaling_coeff(0.7)
self.assertEqual(2, len(op_a.paulis))
self.assertEqual(0.35, op_a.paulis[0][0])
def test_negation_operator(self):
paulis = ['IXYZ', 'XXZY', 'IIZZ', 'XXYY', 'ZZXX', 'YYYY']
coeffs = [0.2, 0.6, 0.8, -0.2, -0.6, -0.8]
op1 = Operator(paulis=[])
for coeff, pauli in zip(coeffs, paulis):
pauli_term = [coeff, label_to_pauli(pauli)]
op1 += Operator(paulis=[pauli_term])
paulis = ['IXYZ', 'XXZY', 'IIZZ', 'XXYY', 'ZZXX', 'YYYY']
coeffs = [-0.2, -0.6, -0.8, 0.2, 0.6, 0.8]
op2 = Operator(paulis=[])
for coeff, pauli in zip(coeffs, paulis):
pauli_term = [coeff, label_to_pauli(pauli)]
op2 += Operator(paulis=[pauli_term])
self.assertNotEqual(op1, op2)
self.assertEqual(op1, -op2)
self.assertEqual(-op1, op2)
op1.scaling_coeff(-1.0)
self.assertEqual(op1, op2)