class TestTwoLayerQNN(QiskitMachineLearningTestCase):
"""Two Layer QNN Tests."""
def setUp(self):
super().setUp()
# specify "run configuration"
backend = Aer.get_backend('statevector_simulator')
quantum_instance = QuantumInstance(backend)
# define QNN
feature_map = ZZFeatureMap(2)
var_form = RealAmplitudes(2, reps=1)
self.qnn = TwoLayerQNN(2,
feature_map=feature_map,
var_form=var_form,
quantum_instance=quantum_instance)
def test_two_layer_qnn1(self):
""" Opflow QNN Test """
input_data = np.zeros(self.qnn.num_inputs)
weights = np.zeros(self.qnn.num_weights)
# test forward pass
result = self.qnn.forward(input_data, weights)
self.assertEqual(result.shape, (1, *self.qnn.output_shape))
# test backward pass
result = self.qnn.backward(input_data, weights)
self.assertEqual(result[0].shape,
(1, *self.qnn.output_shape, self.qnn.num_inputs))
self.assertEqual(result[1].shape,
(1, *self.qnn.output_shape, self.qnn.num_weights))
def test_batch_gradients(self):
"""Test backward pass for batch input."""
# construct random data set
num_inputs = 2
num_samples = 10
x = np.random.rand(num_samples, num_inputs)
# set up QNN
qnn = TwoLayerQNN(num_qubits=num_inputs,
quantum_instance=self.sv_quantum_instance)
# set up PyTorch module
initial_weights = np.random.rand(qnn.num_weights)
model = TorchConnector(qnn, initial_weights=initial_weights)
# test single gradient
w = model.weights.detach().numpy()
res_qnn = qnn.forward(x[0, :], w)
# construct finite difference gradient for weights
eps = 1e-4
grad = np.zeros(w.shape)
for k in range(len(w)):
delta = np.zeros(w.shape)
delta[k] += eps
f_1 = qnn.forward(x[0, :], w + delta)
f_2 = qnn.forward(x[0, :], w - delta)
grad[k] = (f_1 - f_2) / (2 * eps)
grad_qnn = qnn.backward(x[0, :], w)[1][0, 0, :]
self.assertAlmostEqual(np.linalg.norm(grad - grad_qnn), 0.0, places=4)
model.zero_grad()
res_model = model(Tensor(x[0, :]))
self.assertAlmostEqual(np.linalg.norm(res_model.detach().numpy() -
res_qnn[0]),
0.0,
places=4)
res_model.backward()
grad_model = model.weights.grad
self.assertAlmostEqual(np.linalg.norm(grad_model.detach().numpy() -
grad_qnn),
0.0,
places=4)
# test batch input
batch_grad = np.zeros((*w.shape, num_samples, 1))
for k in range(len(w)):
delta = np.zeros(w.shape)
delta[k] += eps
f_1 = qnn.forward(x, w + delta)
f_2 = qnn.forward(x, w - delta)
batch_grad[k] = (f_1 - f_2) / (2 * eps)
batch_grad = np.sum(batch_grad, axis=1)
batch_grad_qnn = np.sum(qnn.backward(x, w)[1], axis=0)
self.assertAlmostEqual(np.linalg.norm(batch_grad -
batch_grad_qnn.transpose()),
0.0,
places=4)
model.zero_grad()
batch_res_model = sum(model(Tensor(x)))
batch_res_model.backward()
self.assertAlmostEqual(np.linalg.norm(model.weights.grad.numpy() -
batch_grad.transpose()[0]),
0.0,
places=4)