def test_one(self):
feature_map = FixedLengthQubitEncoding(4, 4)
X_train = [
[4.4, -9.53],
[18.42, 1.0]
]
y_train = [0, 1]
input = [2.043, 13.84]
X_train_in_feature_space = [feature_map.map(x) for x in X_train]
X_train_in_feature_space_qubit_notation = [["{:b}".format(i).zfill(2 * 9) for i, _ in elem.keys()] for elem in
X_train_in_feature_space]
input_in_feature_space = feature_map.map(input)
input_in_feature_space_qubit_notation = ["{:b}".format(i).zfill(2 * 9) for i, _ in
input_in_feature_space.keys()]
logger.info("Training samples in feature space: {}".format(X_train_in_feature_space_qubit_notation))
logger.info("Input sample in feature space: {}".format(input_in_feature_space_qubit_notation))
circuit = QmlBinaryDataStateCircuitBuilder(CCXMöttönen())
qc = circuit.build_circuit('test', X_train=X_train_in_feature_space, y_train=y_train, X_input=input_in_feature_space)
self.assertIsNotNone(qc)
self.assertIsNotNone(qc.data)
self.assertEqual(30, len(qc.data))
self.assertListEqual(["h"], [qc.data[0].name])
self.assertListEqual(["h"], [qc.data[1].name])
self.assertListEqual(["ccx_uni_rot", [0], "(QuantumRegister(18, 'f^S'), 3)"], extract_gate_info(qc, 2))
self.assertListEqual(["ccx_uni_rot", [0], "(QuantumRegister(18, 'f^S'), 4)"], extract_gate_info(qc, 3))
self.assertListEqual(["ccx_uni_rot", [0], "(QuantumRegister(18, 'f^S'), 7)"], extract_gate_info(qc, 4))
self.assertListEqual(["ccx_uni_rot", [0], "(QuantumRegister(18, 'f^S'), 8)"], extract_gate_info(qc, 5))
self.assertListEqual(["ccx_uni_rot", [0], "(QuantumRegister(18, 'f^S'), 10)"], extract_gate_info(qc, 6))
self.assertListEqual(["ccx_uni_rot", [0], "(QuantumRegister(18, 'f^S'), 11)"], extract_gate_info(qc, 7))
self.assertListEqual(["ccx_uni_rot", [0], "(QuantumRegister(18, 'f^S'), 15)"], extract_gate_info(qc, 8))
self.assertListEqual(["ccx_uni_rot", [1], "(QuantumRegister(18, 'f^S'), 0)"], extract_gate_info(qc, 9))
self.assertListEqual(["ccx_uni_rot", [1], "(QuantumRegister(18, 'f^S'), 2)"], extract_gate_info(qc, 10))
self.assertListEqual(["ccx_uni_rot", [1], "(QuantumRegister(18, 'f^S'), 3)"], extract_gate_info(qc, 11))
self.assertListEqual(["ccx_uni_rot", [1], "(QuantumRegister(18, 'f^S'), 4)"], extract_gate_info(qc, 12))
self.assertListEqual(["ccx_uni_rot", [1], "(QuantumRegister(18, 'f^S'), 6)"], extract_gate_info(qc, 13))
self.assertListEqual(["ccx_uni_rot", [1], "(QuantumRegister(18, 'f^S'), 7)"], extract_gate_info(qc, 14))
self.assertListEqual(["ccx_uni_rot", [1], "(QuantumRegister(18, 'f^S'), 14)"], extract_gate_info(qc, 15))
self.assertListEqual(["ccx_uni_rot", [2], "(QuantumRegister(18, 'f^S'), 4)"], extract_gate_info(qc, 16))
self.assertListEqual(["ccx_uni_rot", [2], "(QuantumRegister(18, 'f^S'), 10)"], extract_gate_info(qc, 17))
self.assertListEqual(["ccx_uni_rot", [2], "(QuantumRegister(18, 'f^S'), 11)"], extract_gate_info(qc, 18))
self.assertListEqual(["ccx_uni_rot", [2], "(QuantumRegister(18, 'f^S'), 13)"], extract_gate_info(qc, 19))
self.assertListEqual(["ccx_uni_rot", [2], "(QuantumRegister(18, 'f^S'), 16)"], extract_gate_info(qc, 20))
self.assertListEqual(["ccx_uni_rot", [2], "(QuantumRegister(1, 'l^q'), 0)"], extract_gate_info(qc, 21))
self.assertListEqual(["ccx_uni_rot", [3], "(QuantumRegister(18, 'f^S'), 0)"], extract_gate_info(qc, 22))
self.assertListEqual(["ccx_uni_rot", [3], "(QuantumRegister(18, 'f^S'), 2)"], extract_gate_info(qc, 23))
self.assertListEqual(["ccx_uni_rot", [3], "(QuantumRegister(18, 'f^S'), 3)"], extract_gate_info(qc, 24))
self.assertListEqual(["ccx_uni_rot", [3], "(QuantumRegister(18, 'f^S'), 4)"], extract_gate_info(qc, 25))
self.assertListEqual(["ccx_uni_rot", [3], "(QuantumRegister(18, 'f^S'), 6)"], extract_gate_info(qc, 26))
self.assertListEqual(["ccx_uni_rot", [3], "(QuantumRegister(18, 'f^S'), 7)"], extract_gate_info(qc, 27))
self.assertListEqual(["ccx_uni_rot", [3], "(QuantumRegister(18, 'f^S'), 14)"], extract_gate_info(qc, 28))
self.assertListEqual(["ccx_uni_rot", [3], "(QuantumRegister(1, 'l^q'), 0)"], extract_gate_info(qc, 29))
def test_three(self):
feature_map = FixedLengthQubitEncoding(4, 4)
X_train = [
[4.4, -9.53],
[18.42, 1.0]
]
y_train = [0, 1]
input = [2.043, 13.84]
X_train_in_feature_space = [feature_map.map(x) for x in X_train]
X_train_in_feature_space_qubit_notation = [["{:b}".format(i).zfill(2 * 9) for i, _ in elem.keys()] for elem in
X_train_in_feature_space]
input_in_feature_space = feature_map.map(input)
input_in_feature_space_qubit_notation = ["{:b}".format(i).zfill(2 * 9) for i, _ in
input_in_feature_space.keys()]
logger.info("Training samples in feature space: {}".format(X_train_in_feature_space_qubit_notation))
logger.info("Input sample in feature space: {}".format(input_in_feature_space_qubit_notation))
circuit = QmlBinaryDataStateCircuitBuilder(CCXToffoli())
qc = circuit.build_circuit('test', X_train=X_train_in_feature_space, y_train=y_train, X_input=input_in_feature_space)
self.assertIsNotNone(qc)
self.assertIsNotNone(qc.data)
def test_five(self):
X_train = np.asarray([[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0],
[1.0, -1.0]])
y_train = [0, 1, 0, 1]
X_test = np.asarray([[0.2, 0.4], [0.4, -0.8]])
y_test = [0, 1]
class MyFeatureMap(FeatureMap):
def map(self, input_vector: list) -> sparse.dok_matrix:
result = sparse.dok_matrix((4, 1))
index = 0
if input_vector[0] > 0 and input_vector[1] > 0:
index = 0
if input_vector[0] < 0 and input_vector[1] > 0:
index = 1
if input_vector[0] < 0 and input_vector[1] < 0:
index = 2
if input_vector[0] > 0 and input_vector[1] < 0:
index = 3
result[index, 0] = 1.0
return result
initial_state_builder = QmlBinaryDataStateCircuitBuilder(CCXToffoli())
feature_map = MyFeatureMap()
X_train_in_feature_space = [feature_map.map(s) for s in X_train]
X_test_in_feature_space = [feature_map.map(s) for s in X_test]
qc = initial_state_builder.build_circuit('test',
X_train_in_feature_space,
y_train,
X_test_in_feature_space[0])
self.assertIsNotNone(qc)
self.assertIsNotNone(qc.data)
# self.assertEqual(len(qc.data), 28)
# self.assertListEqual(["h"], [qc.data[0].name])
# self.assertListEqual(["h"], [qc.data[1].name])
qregs = qc.qregs
cregs = [ClassicalRegister(qr.size, 'c_' + qr.name) for qr in qregs]
qc2 = QuantumCircuit(*qregs, *cregs, name='test2')
qc2.data = qc.data
for i in range(len(qregs)):
measure(qc2, qregs[i], cregs[i])
execution_backend = qiskit.Aer.get_backend(
'qasm_simulator') # type: BaseBackend
qobj = qiskit.compile([qc2], execution_backend, shots=8192)
result = execution_backend.run(qobj) # type. BaseJob
counts = result.result().get_counts() # type: dict
self.assertListEqual(
sorted([
'00 0 00 00 0', '00 0 00 00 1', '00 1 01 01 0', '00 1 00 01 1',
'00 0 10 10 0', '00 0 00 10 1', '00 1 11 11 0', '00 1 00 11 1'
]), sorted(counts.keys()))
def runTest(self):
log.info(
"Testing 'QmlHadamardNeighborClassifier' with CNOT Preparation.")
execution_backend = qiskit.Aer.get_backend(
'qasm_simulator') # type: BaseBackend
X_train = numpy.asarray([[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0],
[1.0, -1.0]])
y_train = [0, 1, 0, 1]
X_test = numpy.asarray([[0.2, 0.4], [0.4, -0.8]])
y_test = [0, 1]
class MyFeatureMap(FeatureMap):
def map(self, input_vector: list) -> sparse.dok_matrix:
result = sparse.dok_matrix((4, 1))
index = 0
if input_vector[0] > 0 and input_vector[1] > 0:
index = 0
if input_vector[0] < 0 < input_vector[1]:
index = 1
if input_vector[0] < 0 and input_vector[1] < 0:
index = 2
if input_vector[0] > 0 > input_vector[1]:
index = 3
result[index, 0] = 1.0
return result
feature_map = MyFeatureMap()
initial_state_builder = QmlBinaryDataStateCircuitBuilder(CCXToffoli())
qml = QmlHadamardNeighborClassifier(
backend=execution_backend,
shots=100 * 8192,
feature_map=feature_map,
classifier_circuit_factory=initial_state_builder)
qml.fit(X_train, y_train)
prediction = qml.predict(X_test)
self.assertEqual(len(prediction), len(y_test))
self.assertListEqual(prediction, y_test)
def test_two(self):
feature_map = FixedLengthQubitEncoding(2, 2)
X_train = [
[4.4, -9.53],
[18.42, 1.0]
]
y_train = [0, 1]
input = [2.043, 13.84]
X_train_in_feature_space = [feature_map.map(x) for x in X_train]
X_train_in_feature_space_qubit_notation = [["{:b}".format(i).zfill(2*5) for i, _ in elem.keys()] for elem in X_train_in_feature_space]
input_in_feature_space = feature_map.map(input)
input_in_feature_space_qubit_notation = ["{:b}".format(i).zfill(2*5) for i, _ in input_in_feature_space.keys()]
logger.info("Training samples in feature space: {}".format(X_train_in_feature_space_qubit_notation))
logger.info("Input sample in feature space: {}".format(input_in_feature_space_qubit_notation))
circuit = QmlBinaryDataStateCircuitBuilder(CCXMöttönen())
qc = circuit.build_circuit('test', X_train=X_train_in_feature_space, y_train=y_train, X_input=input_in_feature_space)
self.assertIsNotNone(qc)
self.assertIsNotNone(qc.data)
self.assertEqual(len(qc.data), 22)
self.assertListEqual(["h"], [qc.data[0].name])
self.assertListEqual(["h"], [qc.data[1].name])
self.assertListEqual(["ccx_uni_rot", [0], "(QuantumRegister(10, 'f^S'), 1)"], extract_gate_info(qc, 2))
self.assertListEqual(["ccx_uni_rot", [0], "(QuantumRegister(10, 'f^S'), 3)"], extract_gate_info(qc, 3))
self.assertListEqual(["ccx_uni_rot", [0], "(QuantumRegister(10, 'f^S'), 4)"], extract_gate_info(qc, 4))
self.assertListEqual(["ccx_uni_rot", [0], "(QuantumRegister(10, 'f^S'), 5)"], extract_gate_info(qc, 5))
self.assertListEqual(["ccx_uni_rot", [0], "(QuantumRegister(10, 'f^S'), 8)"], extract_gate_info(qc, 6))
self.assertListEqual(["ccx_uni_rot", [1], "(QuantumRegister(10, 'f^S'), 0)"], extract_gate_info(qc, 7))
self.assertListEqual(["ccx_uni_rot", [1], "(QuantumRegister(10, 'f^S'), 1)"], extract_gate_info(qc, 8))
self.assertListEqual(["ccx_uni_rot", [1], "(QuantumRegister(10, 'f^S'), 2)"], extract_gate_info(qc, 9))
self.assertListEqual(["ccx_uni_rot", [1], "(QuantumRegister(10, 'f^S'), 3)"], extract_gate_info(qc, 10))
self.assertListEqual(["ccx_uni_rot", [1], "(QuantumRegister(10, 'f^S'), 8)"], extract_gate_info(qc, 11))
self.assertListEqual(["ccx_uni_rot", [2], "(QuantumRegister(10, 'f^S'), 2)"], extract_gate_info(qc, 12))
self.assertListEqual(["ccx_uni_rot", [2], "(QuantumRegister(10, 'f^S'), 5)"], extract_gate_info(qc, 13))
self.assertListEqual(["ccx_uni_rot", [2], "(QuantumRegister(10, 'f^S'), 8)"], extract_gate_info(qc, 14))
self.assertListEqual(["ccx_uni_rot", [2], "(QuantumRegister(1, 'l^q'), 0)"], extract_gate_info(qc, 15))
self.assertListEqual(["ccx_uni_rot", [3], "(QuantumRegister(10, 'f^S'), 0)"], extract_gate_info(qc, 16))
self.assertListEqual(["ccx_uni_rot", [3], "(QuantumRegister(10, 'f^S'), 1)"], extract_gate_info(qc, 17))
self.assertListEqual(["ccx_uni_rot", [3], "(QuantumRegister(10, 'f^S'), 2)"], extract_gate_info(qc, 18))
self.assertListEqual(["ccx_uni_rot", [3], "(QuantumRegister(10, 'f^S'), 3)"], extract_gate_info(qc, 19))
self.assertListEqual(["ccx_uni_rot", [3], "(QuantumRegister(10, 'f^S'), 8)"], extract_gate_info(qc, 20))
self.assertListEqual(["ccx_uni_rot", [3], "(QuantumRegister(1, 'l^q'), 0)"], extract_gate_info(qc, 21))
qregs = qc.qregs
cregs = [ClassicalRegister(qr.size, 'c' + qr.name) for qr in qregs]
qc2 = QuantumCircuit(*qregs, *cregs, name='test2')
qc2.data = qc.data
for i in range(len(qregs)):
measure(qc2, qregs[i], cregs[i])
execution_backend = qiskit.Aer.get_backend('qasm_simulator') # type: BaseBackend
qobj = qiskit.compile([qc2], execution_backend, shots=8192)
result = execution_backend.run(qobj) # type: BaseJob
counts = result.result().get_counts() # type: dict
self.assertListEqual(sorted(counts.keys()), sorted(['0 0100111010 0 0', '0 0100001111 0 1', '1 0100100100 1 0', '1 0100001111 1 1']))
def test_four(self):
encoding_map = FixedLengthQubitEncoding(2, 2)
X_train = [[4.4, -9.53], [18.42, 1.0]]
y_train = [0, 1]
input = [2.043, 13.84]
X_train_in_encoded_space = [encoding_map.map(x) for x in X_train]
X_train_in_encoded_space_qubit_notation = [[
"{:b}".format(i).zfill(2 * 5) for i, _ in elem.keys()
] for elem in X_train_in_encoded_space]
input_in_encoded_space = encoding_map.map(input)
input_in_encoded_space_qubit_notation = [
"{:b}".format(i).zfill(2 * 5)
for i, _ in input_in_encoded_space.keys()
]
logger.info("Training samples in encoded space: {}".format(
X_train_in_encoded_space_qubit_notation))
logger.info("Input sample in encoded space: {}".format(
input_in_encoded_space_qubit_notation))
circuit = QmlBinaryDataStateCircuitBuilder(CCXToffoli())
qc = circuit.build_circuit('test',
X_train=X_train_in_encoded_space,
y_train=y_train,
X_input=input_in_encoded_space)
self.assertIsNotNone(qc)
self.assertIsNotNone(qc.data)
# TODO: adjust ASAP
# self.assertEqual(28, len(qc.data))
#
# self.assertListEqual(["h"], [qc.data[0].name])
# self.assertListEqual(["h"], [qc.data[1].name])
#
# self.assertListEqual(["x", [], "(QuantumRegister(1, 'a'), 0)"], extract_gate_info(qc, 2))
# self.assertListEqual(["x", [], "(QuantumRegister(1, 'i'), 0)"], extract_gate_info(qc, 3))
#
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 1)"], extract_gate_info(qc, 4))
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 3)"], extract_gate_info(qc, 5))
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 4)"], extract_gate_info(qc, 6))
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 5)"], extract_gate_info(qc, 7))
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 8)"], extract_gate_info(qc, 8))
#
# self.assertListEqual(["x", [], "(QuantumRegister(1, 'a'), 0)"], extract_gate_info(qc, 9))
#
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 0)"], extract_gate_info(qc, 10))
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 1)"], extract_gate_info(qc, 11))
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 2)"], extract_gate_info(qc, 12))
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 3)"], extract_gate_info(qc, 13))
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 8)"], extract_gate_info(qc, 14))
#
# self.assertListEqual(["x", [], "(QuantumRegister(1, 'i'), 0)"], extract_gate_info(qc, 15))
#
# self.assertListEqual(["x", [], "(QuantumRegister(1, 'a'), 0)"], extract_gate_info(qc, 16))
#
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 2)"], extract_gate_info(qc, 17))
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 5)"], extract_gate_info(qc, 18))
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 8)"], extract_gate_info(qc, 19))
# self.assertListEqual(["ccx", [], "(QuantumRegister(1, 'l^q'), 0)"], extract_gate_info(qc, 20))
#
# self.assertListEqual(["x", [], "(QuantumRegister(1, 'a'), 0)"], extract_gate_info(qc, 21))
#
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 0)"], extract_gate_info(qc, 22))
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 1)"], extract_gate_info(qc, 23))
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 2)"], extract_gate_info(qc, 24))
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 3)"], extract_gate_info(qc, 25))
# self.assertListEqual(["ccx", [], "(QuantumRegister(10, 'f^S'), 8)"], extract_gate_info(qc, 26))
#
# self.assertListEqual(["ccx", [], "(QuantumRegister(1, 'l^q'), 0)"], extract_gate_info(qc, 27))
cregs = [ClassicalRegister(qr.size, 'c' + qr.name) for qr in qc.qregs]
qc2 = QuantumCircuit(*qc.qregs, *cregs, name='test2')
qc2.data = qc.data
for i in range(len(qc.qregs)):
measure(qc2, qc.qregs[i], cregs[i])
execution_backend = qiskit.Aer.get_backend(
'qasm_simulator') # type: BaseBackend
job = qiskit.execute([qc2], execution_backend, shots=8192)
counts = job.result().get_counts() # type: dict
self.assertListEqual(
sorted([
'0 0100111010 0 0', '0 0100001111 0 1', '1 0100100100 1 0',
'1 0100001111 1 1'
]), sorted(counts.keys()))
