def test_swap_overlap_orthogonal(self):
random_rotations = generate_random_rotation_batch(
self.num_qubits_of_a_state, self.circuit_batch_size)
circuit_input1 = tfq.convert_to_tensor(
act_gate_batch_on_qubits(random_rotations,
self.state1,
as_circuits=True))
circuit_input2 = tfq.convert_to_tensor(
act_gate_batch_on_qubits(random_rotations,
self.state2,
as_circuits=True))
circuit_input2 = tfq_utility_ops.tfq_append_circuit(
tfq.convert_to_tensor(
[cirq.Circuit([cirq.X.on_each(self.state2)])] *
self.circuit_batch_size), circuit_input2)
swap_layer: SWAPTestLayer = SWAPTestLayer(self.state1, self.state2)
swap_test: tf.Tensor = swap_layer([circuit_input1, circuit_input2])
output: tf.Tensor = tfq.layers.Expectation()(
swap_test, operators=[cirq.Z(swap_layer.auxiliary_qubit)])
print(output)
self.assertTrue(
tf_allclose(output,
tf.convert_to_tensor([[0.0]] *
self.circuit_batch_size),
rtol=1e-5,
atol=1e-5))
def test_swap_overlap_orthogonal(self):
random_rotations = [
General1BitRotation(sympy.Symbol(f"theta_q{i}_1"),
sympy.Symbol(f"theta_q{i}_2"),
sympy.Symbol(f"theta_q{i}_3"))
for i in range(self.num_qubits_of_a_state)
]
circuit_input1 = tfq.convert_to_tensor([cirq.Circuit()] *
self.circuit_batch_size)
circuit_input2 = tfq.convert_to_tensor(
[cirq.Circuit(cirq.X.on_each(self.state2))] *
self.circuit_batch_size)
swap_layer = SWAPTestOutputLayer(
circuit_prepend_of_state1=act_gate_batch_on_qubits(
[random_rotations], self.state1, as_circuits=True)[0],
circuit_prepend_of_state2=act_gate_batch_on_qubits(
[random_rotations], self.state2, as_circuits=True)[0],
state1=self.state1,
state2=self.state2)
output: tf.Tensor = swap_layer([circuit_input1, circuit_input2])
print(output)
self.assertTrue(
tf_allclose(output,
tf.convert_to_tensor([[0.0]] *
self.circuit_batch_size),
rtol=1e-5,
atol=1e-5))
def test_swap_overlap_identical(self):
random_rotations = generate_random_rotation_batch(
self.num_qubits_of_a_state, self.circuit_batch_size)
circuit_input1 = tfq.convert_to_tensor(
act_gate_batch_on_qubits(random_rotations,
self.state1,
as_circuits=True))
circuit_input2 = tfq.convert_to_tensor(
act_gate_batch_on_qubits(random_rotations,
self.state2,
as_circuits=True))
swap_layer: SWAPTestOutputLayer = SWAPTestOutputLayer(
circuit_prepend_of_state1=cirq.Circuit(
[cirq.rx(sympy.Symbol("theta"))(self.state1[0])]),
circuit_prepend_of_state2=cirq.Circuit(
[cirq.rx(sympy.Symbol("theta"))(self.state2[0])]),
state1=self.state1,
state2=self.state2)
output: tf.Tensor = swap_layer([circuit_input1, circuit_input2])
print(output)
self.assertTrue(
tf_allclose(output,
tf.convert_to_tensor([[1.0]] *
self.circuit_batch_size),
rtol=1e-2))
def test_swap_overlap_random_rotations(self):
circuit_input1 = tfq.convert_to_tensor(
act_gate_batch_on_qubits(generate_random_rotation_batch(
self.num_qubits_of_a_state, self.circuit_batch_size),
self.state1,
as_circuits=True))
circuit_input2 = tfq.convert_to_tensor(
[cirq.Circuit([cirq.I.on_each(*self.state2)])] *
self.circuit_batch_size)
swap_layer: SWAPTestLayer = SWAPTestLayer(self.state1, self.state2)
swap_test: tf.Tensor = swap_layer([circuit_input1, circuit_input2])
output: tf.Tensor = tfq.layers.Expectation()(
swap_test, operators=[cirq.Z(swap_layer.auxiliary_qubit)])
print(output)
def test_swap_overlap_random_rotations(self):
circuit_input1 = tfq.convert_to_tensor(
act_gate_batch_on_qubits(generate_random_rotation_batch(
self.num_qubits_of_a_state, self.circuit_batch_size),
self.state1,
as_circuits=True))
circuit_input2 = tfq.convert_to_tensor([cirq.Circuit()] *
self.circuit_batch_size)
swap_layer = SWAPTestOutputLayer(
circuit_prepend_of_state1=cirq.Circuit(
[cirq.rx(sympy.Symbol("theta1"))(self.state1[0])]),
circuit_prepend_of_state2=cirq.Circuit(
[cirq.rx(sympy.Symbol("theta2"))(self.state2[0])]),
circuit_append=cirq.Circuit([
cirq.rx(-sympy.Symbol("theta1"))(self.state1[0]),
cirq.rx(-sympy.Symbol("theta2"))(self.state2[0])
]),
state1=self.state1,
state2=self.state2)
output: tf.Tensor = swap_layer([circuit_input1, circuit_input2])
print(output)
self.assertEqual(output.shape, (5, 1))