def test_ones_like():
"""Test that all ones arrays are correctly created"""
x = np.array([[1, 2, 3], [4, 5, 6]])
xT = qml.proc.TensorBox(x)
res = xT.ones_like()
expected = np.ones_like(x)
assert isinstance(res, AutogradBox)
assert np.all(res == expected)
def parameter_shift(weights):
"""Compute the gradient of the variational circuit given by the
ansatz function using the parameter-shift rule.
Write your code below between the # QHACK # markers—create a device with
the correct number of qubits, create a QNode that applies the above ansatz,
and compute the gradient of the provided ansatz using the parameter-shift rule.
Args:
weights (array): An array of floating-point numbers with size (2, 3).
Returns:
array: The gradient of the variational circuit. The shape should match
the input weights array.
"""
dev = qml.device("default.qubit", wires=3)
@qml.qnode(dev)
def vq_circuit(weights):
for i in range(len(weights)):
qml.RX(weights[i, 0], wires=0)
qml.RY(weights[i, 1], wires=1)
qml.RZ(weights[i, 2], wires=2)
qml.CNOT(wires=[0, 1])
qml.CNOT(wires=[1, 2])
qml.CNOT(wires=[2, 0])
return qml.expval(qml.PauliY(0) @ qml.PauliZ(2))
gradient = np.zeros_like(weights)
# QHACK #
shifted_par = weights.copy()
unit_vec = np.ones_like(weights)
for i in range(len(weights)): #row
for j in range(len(weights[0])): #column
shifted_par = weights.copy()
shifted_par[i][j] += np.pi / 2
fwd_shift = vq_circuit(shifted_par)
shifted_par[i][j] -= np.pi
bkwd_shift = vq_circuit(shifted_par)
gradient[i][j] = 0.5 * (fwd_shift - bkwd_shift)
# QHACK #
return gradient
def expand_num_freq(num_freq, param):
if np.isscalar(num_freq):
num_freq = [num_freq] * len(param)
expanded = []
for _num_freq, par in zip(num_freq, param):
if np.isscalar(_num_freq) and np.isscalar(par):
expanded.append(_num_freq)
elif np.isscalar(_num_freq):
expanded.append(np.ones_like(par) * _num_freq)
elif np.isscalar(par):
raise ValueError(f"{num_freq}\n{param}\n{_num_freq}\n{par}")
elif len(_num_freq) == len(par):
expanded.append(_num_freq)
else:
raise ValueError()
return expanded
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]))
#
# The latter two observables, on the other hand, are seemingly
# unaffected by the noise at all.
#
# We can see that even when noise is present, there may still be subspaces
# or observables which are minimally affected or unaffected.
# This gives us some hope that variational algorithms can learn
# to find and exploit such noise-free substructures on otherwise
# noisy devices.
#
# We can also plot the CHSH observable in the noisy case. Remember,
# values greater than 2 can safely be considered "quantum".
plt.plot(noise_vals, CHSH_expvals, label="CHSH")
plt.plot(noise_vals,
2 * np.ones_like(noise_vals),
label="Quantum-classical boundary")
plt.xlabel('Noise parameter')
plt.ylabel('CHSH Expectation value')
plt.legend()
plt.show()
##############################################################################
# Too much noise (around 0.2 in this example), and we lose the
# quantumness we created in our circuit. But if we only have a little
# noise, the quantumness undeniably remains. So there is still hope
# that quantum algorithms can do something useful, even on noisy
# near-term devices, so long as the noise is not high.
#
# .. note::
#
def ones_like(self):
return AutogradBox(np.ones_like(self.data))
