def get_angles(x):
beta0 = 2 * np.arcsin(np.sqrt(x[1] ** 2) / np.sqrt(x[0] ** 2 + x[1] ** 2 + 1e-12))
beta1 = 2 * np.arcsin(np.sqrt(x[3] ** 2) / np.sqrt(x[2] ** 2 + x[3] ** 2 + 1e-12))
beta2 = 2 * np.arcsin(
np.sqrt(x[2] ** 2 + x[3] ** 2) / np.sqrt(x[0] ** 2 + x[1] ** 2 + x[2] ** 2 + x[3] ** 2)
)
return np.array([beta2, -beta1 / 2, beta1 / 2, -beta0 / 2, beta0 / 2])
def get_angles(x):
"""Compute rotation angles for state preparation routine.
Args:
x: original input vector
Returns:
array[float]: feature vector of angles
"""
beta0 = 2 * np.arcsin(np.sqrt(x[1]) ** 2 / np.sqrt(x[0] ** 2 + x[1] ** 2 + 1e-12) )
beta1 = 2 * np.arcsin(np.sqrt(x[3]) ** 2 / np.sqrt(x[2] ** 2 + x[3] ** 2 + 1e-12) )
beta2 = 2 * np.arcsin(np.sqrt(x[2] ** 2 + x[3] ** 2) / np.sqrt(x[0] ** 2 + x[1] ** 2 + x[2] ** 2 + x[3] ** 2))
return np.array([beta2, -beta1 / 2, beta1 / 2, -beta0 / 2, beta0 / 2])
def _recursive_compute_beta(input_vector, betas):
if len(input_vector) > 1:
new_x = []
beta = []
for k in range(0, len(input_vector), 2):
norm = np.sqrt(input_vector[k]**2 + input_vector[k + 1]**2) # pylint: disable=no-member
new_x.append(norm)
if norm == 0:
beta.append(0)
else:
if input_vector[k] < 0:
beta.append(2 * np.pi -
2 * np.arcsin(input_vector[k + 1] / norm)) # pylint: disable=no-member
else:
beta.append(2 * np.arcsin(input_vector[k + 1] / norm)) # pylint: disable=no-member
Encoding._recursive_compute_beta(new_x, betas)
betas.append(beta)
class AutogradBox(qml.math.TensorBox):
"""Implements the :class:`~.TensorBox` API for ``pennylane.numpy`` tensors.
For more details, please refer to the :class:`~.TensorBox` documentation.
"""
abs = wrap_output(lambda self: np.abs(self.data))
angle = wrap_output(lambda self: np.angle(self.data))
arcsin = wrap_output(lambda self: np.arcsin(self.data))
cast = wrap_output(lambda self, dtype: np.tensor(self.data, dtype=dtype))
diag = staticmethod(wrap_output(lambda values, k=0: np.diag(values, k=k)))
expand_dims = wrap_output(
lambda self, axis: np.expand_dims(self.data, axis=axis))
ones_like = wrap_output(lambda self: np.ones_like(self.data))
reshape = wrap_output(lambda self, shape: np.reshape(self.data, shape))
sqrt = wrap_output(lambda self: np.sqrt(self.data))
sum = wrap_output(lambda self, axis=None, keepdims=False: np.sum(
self.data, axis=axis, keepdims=keepdims))
T = wrap_output(lambda self: self.data.T)
squeeze = wrap_output(lambda self: self.data.squeeze())
@staticmethod
def astensor(tensor):
return np.tensor(tensor)
@staticmethod
@wrap_output
def concatenate(values, axis=0):
return np.concatenate(AutogradBox.unbox_list(values), axis=axis)
@staticmethod
@wrap_output
def dot(x, y):
x, y = AutogradBox.unbox_list([x, y])
if x.ndim == 0 and y.ndim == 0:
return x * y
if x.ndim == 2 and y.ndim == 2:
return x @ y
return np.dot(x, y)
@property
def interface(self):
return "autograd"
def numpy(self):
if hasattr(self.data, "_value"):
# Catches the edge case where the data is an Autograd arraybox,
# which only occurs during backpropagation.
return self.data._value
return self.data.numpy()
@property
def requires_grad(self):
return self.data.requires_grad
@wrap_output
def scatter_element_add(self, index, value):
size = self.data.size
flat_index = np.ravel_multi_index(index, self.shape)
t = [0] * size
t[flat_index] = value
self.data = self.data + np.array(t).reshape(self.shape)
return self.data
@property
def shape(self):
return self.data.shape
@staticmethod
@wrap_output
def stack(values, axis=0):
return np.stack(AutogradBox.unbox_list(values), axis=axis)
@wrap_output
def take(self, indices, axis=None):
indices = self.astensor(indices)
if axis is None:
return self.data.flatten()[indices]
fancy_indices = [slice(None)] * axis + [indices]
return self.data[tuple(fancy_indices)]
@staticmethod
@wrap_output
def where(condition, x, y):
return np.where(condition, *AutogradBox.unbox_list([x, y]))
def test_arcsin(t):
"""Test that the arcsin function works for a variety
of input"""
res = fn.arcsin(t)
assert fn.allequal(res, np.arcsin([1, 0.2, -0.5]))
