def test_pauli_evolution(self):
"""Test the generation of Pauli blocks."""
encoding = PauliFeatureMap()
time = 1.4
with self.subTest(pauli_string='ZZ'):
evo = QuantumCircuit(2)
evo.cx(0, 1)
evo.p(2 * time, 1)
evo.cx(0, 1)
pauli = encoding.pauli_evolution('ZZ', time)
self.assertTrue(Operator(pauli).equiv(evo))
with self.subTest(pauli_string='XYZ'):
evo = QuantumCircuit(3)
# X on the most-significant, bottom qubit, Z on the top
evo.h(2)
evo.rx(np.pi / 2, 1)
evo.cx(0, 1)
evo.cx(1, 2)
evo.p(2 * time, 2)
evo.cx(1, 2)
evo.cx(0, 1)
evo.rx(-np.pi / 2, 1)
evo.h(2)
pauli = encoding.pauli_evolution('XYZ', time)
self.assertTrue(Operator(pauli).equiv(evo))
with self.subTest(pauli_string='I'):
evo = QuantumCircuit(1)
pauli = encoding.pauli_evolution('I', time)
self.assertTrue(Operator(pauli).equiv(evo))
def test_parameter_prefix(self):
"""Test the Parameter prefix"""
encoding_pauli = PauliFeatureMap(feature_dimension=2,
reps=2,
paulis=["ZY"],
parameter_prefix="p")
encoding_z = ZFeatureMap(feature_dimension=2,
reps=2,
parameter_prefix="q")
encoding_zz = ZZFeatureMap(feature_dimension=2,
reps=2,
parameter_prefix="r")
x = ParameterVector("x", 2)
y = Parameter("y")
self.assertEqual(
str(encoding_pauli.parameters),
"ParameterView([ParameterVectorElement(p[0]), ParameterVectorElement(p[1])])",
)
self.assertEqual(
str(encoding_z.parameters),
"ParameterView([ParameterVectorElement(q[0]), ParameterVectorElement(q[1])])",
)
self.assertEqual(
str(encoding_zz.parameters),
"ParameterView([ParameterVectorElement(r[0]), ParameterVectorElement(r[1])])",
)
encoding_pauli_param_x = encoding_pauli.assign_parameters(x)
encoding_z_param_x = encoding_z.assign_parameters(x)
encoding_zz_param_x = encoding_zz.assign_parameters(x)
self.assertEqual(
str(encoding_pauli_param_x.parameters),
"ParameterView([ParameterVectorElement(x[0]), ParameterVectorElement(x[1])])",
)
self.assertEqual(
str(encoding_z_param_x.parameters),
"ParameterView([ParameterVectorElement(x[0]), ParameterVectorElement(x[1])])",
)
self.assertEqual(
str(encoding_zz_param_x.parameters),
"ParameterView([ParameterVectorElement(x[0]), ParameterVectorElement(x[1])])",
)
encoding_pauli_param_y = encoding_pauli.assign_parameters({1, y})
encoding_z_param_y = encoding_z.assign_parameters({1, y})
encoding_zz_param_y = encoding_zz.assign_parameters({1, y})
self.assertEqual(str(encoding_pauli_param_y.parameters),
"ParameterView([Parameter(y)])")
self.assertEqual(str(encoding_z_param_y.parameters),
"ParameterView([Parameter(y)])")
self.assertEqual(str(encoding_zz_param_y.parameters),
"ParameterView([Parameter(y)])")
def feature_map():
# BUILD FEATURE MAP HERE - START
feature_dim = 3 # equal to the dimension of the data
# from qiskit.circuit import QuantumCircuit, ParameterVector
# num_qubits = 3
# reps = 1 # number of times you'd want to repeat the circuit
# x = ParameterVector('x', length=num_qubits) # creating a list of Parameters
# custom_circ = QuantumCircuit(num_qubits)
# # defining our parametric form
# for _ in range(reps):
# for i in range(num_qubits):
# custom_circ.rx(x[i], i)
# for i in range(num_qubits):
# for j in range(0, 1):
# custom_circ.cx(i, j)
# if i in {1,0} or j in {1,0}:
# custom_circ.u1(x[i] * x[j], j)
# custom_circ.cx(i, j)
# import required qiskit libraries if additional libraries are required
# build the feature map
# feature_map = ZFeatureMap(feature_dimension=feature_dim, reps=5)
feature_map = PauliFeatureMap(feature_dimension=feature_dim,
entanglement='full',
reps=1,
paulis=['X', 'Y', 'XY'])
# feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=5,entanglement='linear', insert_barriers=True)
# BUILD FEATURE MAP HERE - END
#return the feature map which is either a FeatureMap or QuantumCircuit object
return feature_map
def feature_map():
# BUILD FEATURE MAP HERE - START
# import required qiskit libraries if additional libraries are required
def custom_map(x):
coeff = 0
if len(x) == 1:
coeff = x[0]
elif len(x) == 3:
coeff = functools.reduce(lambda l, m, n: 1 * m * n, np.pi - x)
return coeff
# build the feature map
feature_map = None
feature_map = PauliFeatureMap(feature_dimension=3,
reps=5,
entanglement="full",
paulis=['Z', 'ZZ'],
data_map_func=custom_map)
# BUILD FEATURE MAP HERE - END
#return the feature map which is either a FeatureMap or QuantumCircuit object
return feature_map
def test_pauli_empty(self):
"""Test instantiating an empty Pauli expansion."""
encoding = PauliFeatureMap()
with self.subTest(msg='equal to empty circuit'):
self.assertTrue(Operator(encoding).equiv(QuantumCircuit()))
with self.subTest(msg='rotation blocks is H gate'):
self.assertEqual(len(encoding.rotation_blocks), 1)
self.assertIsInstance(encoding.rotation_blocks[0].data[0][0], HGate)
def test_pauli_evolution(self):
"""Test the generation of Pauli blocks."""
encoding = PauliFeatureMap()
time = 1.4
with self.subTest(pauli_string="ZZ"):
evo = QuantumCircuit(2)
evo.cx(0, 1)
evo.p(2 * time, 1)
evo.cx(0, 1)
pauli = encoding.pauli_evolution("ZZ", time)
self.assertTrue(Operator(pauli).equiv(evo))
with self.subTest(pauli_string="XYZ"):
# q_0: ─────────────■────────────────────────■──────────────
# ┌─────────┐┌─┴─┐ ┌─┴─┐┌──────────┐
# q_1: ┤ Rx(π/2) ├┤ X ├──■──────────────■──┤ X ├┤ Rx(-π/2) ├
# └──┬───┬──┘└───┘┌─┴─┐┌────────┐┌─┴─┐├───┤└──────────┘
# q_2: ───┤ H ├────────┤ X ├┤ P(2.8) ├┤ X ├┤ H ├────────────
# └───┘ └───┘└────────┘└───┘└───┘
evo = QuantumCircuit(3)
# X on the most-significant, bottom qubit, Z on the top
evo.h(2)
evo.rx(np.pi / 2, 1)
evo.cx(0, 1)
evo.cx(1, 2)
evo.p(2 * time, 2)
evo.cx(1, 2)
evo.cx(0, 1)
evo.rx(-np.pi / 2, 1)
evo.h(2)
pauli = encoding.pauli_evolution("XYZ", time)
self.assertTrue(Operator(pauli).equiv(evo))
with self.subTest(pauli_string="I"):
evo = QuantumCircuit(1)
pauli = encoding.pauli_evolution("I", time)
self.assertTrue(Operator(pauli).equiv(evo))
def feature_map():
# BUILD FEATURE MAP HERE - START
# import required qiskit libraries if additional libraries are required
# build the feature map
feature_map = PauliFeatureMap(3, reps=1, paulis = ['ZZ', 'Y', 'X']) # linear = full
#
#ZZFeatureMap(feature_dimension = 3, reps = 1, entanglement='full')
# BUILD FEATURE MAP HERE - END
#return the feature map which is either a FeatureMap or QuantumCircuit object
return feature_map
def feature_map():
# BUILD FEATURE MAP HERE - START
# import required qiskit libraries if additional libraries are required
# build the feature map
feature_map = PauliFeatureMap(feature_dimension=3,
reps=2,
insert_barriers=True,
paulis=['Y', 'Z', 'ZZ', 'ZZZ'])
# BUILD FEATURE MAP HERE - END
#return the feature map which is either a FeatureMap or QuantumCircuit object
return feature_map
def test_num_parameters(self, num_qubits, reps, pauli_strings):
"""Test the number of parameters equals the number of qubits, independent of reps."""
encoding = PauliFeatureMap(num_qubits, paulis=pauli_strings, reps=reps)
self.assertEqual(encoding.num_parameters, num_qubits)
self.assertEqual(encoding.num_parameters_settable, num_qubits)
def test_pauli_alpha(self):
"""Test Pauli rotation factor (getter, setter)."""
encoding = PauliFeatureMap()
self.assertEqual(encoding.alpha, 2.0)
encoding.alpha = 1.4
self.assertEqual(encoding.alpha, 1.4)
return coeff
feature_map = ZZFeatureMap(feature_dimension=feature_dim,
reps=2,
data_map_func=custom_data_map_func)
qsvm = QSVM(feature_map=feature_map,
training_dataset=training_dataset,
test_dataset=test_dataset)
result = qsvm.run(quantum_instance)
print("testing success ratio: ", result['testing_accuracy'])
feature_map = PauliFeatureMap(feature_dimension=feature_dim,
reps=2,
paulis=['Z', 'Y', 'ZZ'])
qsvm = QSVM(feature_map=feature_map,
training_dataset=training_dataset,
test_dataset=test_dataset)
result = qsvm.run(quantum_instance)
print("testing success ratio: ", result['testing_accuracy'])
feature_dim = 3
sample_Total_b, training_dataset_b, test_dataset_b, class_labels = ad_hoc_data(
training_size=20, test_size=10, n=feature_dim, gap=0.3, plot_data=False)
feature_map = PauliFeatureMap(feature_dimension=feature_dim,
reps=2,