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Remove complex warning from operator matrices #1802

Merged
merged 11 commits into from
Oct 27, 2021
Merged

Remove complex warning from operator matrices #1802

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0ef30e7
Update operator matrices to remove complex casting warning
josh146 Oct 25, 2021
da3cdef
more operators
josh146 Oct 25, 2021
60d2544
more operators
josh146 Oct 25, 2021
7e4ace0
Update tests/ops/qubit/test_hamiltonian.py
josh146 Oct 25, 2021
f4508b3
Merge branch 'master' into remove-complex-warning
josh146 Oct 25, 2021
ce33ada
Merge branch 'master' into remove-complex-warning
josh146 Oct 25, 2021
befe446
Merge branch 'master' into remove-complex-warning
josh146 Oct 26, 2021
ee75d10
Merge branch 'master' into remove-complex-warning
josh146 Oct 26, 2021
d50d01f
Fix autograd warning in orbital rotation.
glassnotes Oct 26, 2021
11c0319
Update variable dtype in torch test to remove warning.
glassnotes Oct 26, 2021
d5e5bb2
Merge branch 'master' into remove-complex-warning
josh146 Oct 26, 2021
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7 changes: 6 additions & 1 deletion pennylane/devices/default_qubit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -487,6 +487,7 @@ def expval(self, observable, shot_range=None, bin_size=None):
# Compute <psi| H |psi> via sum_i coeff_i * <psi| PauliWord |psi> using a sparse
# representation of the Pauliword
res = qml.math.cast(qml.math.convert_like(0.0, observable.data), dtype=complex)
interface = qml.math.get_interface(self.state)

# Note: it is important that we use the Hamiltonian's data and not the coeffs attribute.
# This is because the .data attribute may be 'unwrapped' as required by the interfaces,
Expand All @@ -502,7 +503,11 @@ def expval(self, observable, shot_range=None, bin_size=None):
* Hmat
* qml.math.gather(self.state, coo.col)
)
c = qml.math.cast(qml.math.convert_like(coeff, product), "complex128")
c = qml.math.convert_like(coeff, product)

if interface == "tensorflow":
c = qml.math.cast(c, "complex128")
Comment on lines +508 to +509
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I tracked down another complex warning here. @glassnotes, I fear more are hiding still 😆

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I'm very confused about this one, is it not doing the exact same thing but in two lines? Or does doing the casting in the other frameworks spit out a warning while tensorflow does not? 😕

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Yep! during the backwards pass, the reverse will happen - a complex value will be cast to real. Which... generates the warning in PyTorch and Autograd 🤦

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🤦‍♀️ indeed


res = qml.math.convert_like(res, product) + qml.math.sum(c * product)

else:
Expand Down
55 changes: 28 additions & 27 deletions pennylane/ops/qubit/parametric_ops.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,9 @@ def _matrix(cls, *params):

js = -1j * s

return qml.math.stack([qml.math.stack([c, js]), qml.math.stack([js, c])])
return qml.math.diag([c, c]) + qml.math.stack(
[qml.math.stack([0, js]), qml.math.stack([js, 0])]
)

def adjoint(self):
return RX(-self.data[0], wires=self.wires)
Expand Down Expand Up @@ -122,7 +124,9 @@ def _matrix(cls, *params):
c = qml.math.cos(theta / 2)
s = qml.math.sin(theta / 2)

return qml.math.stack([qml.math.stack([c, -s]), qml.math.stack([s, c])])
return qml.math.diag([c, c]) + qml.math.stack(
[qml.math.stack([0, -s]), qml.math.stack([s, 0])]
)

def adjoint(self):
return RY(-self.data[0], wires=self.wires)
Expand Down Expand Up @@ -838,18 +842,23 @@ def label(self, decimals=None, base_label=None):
@classmethod
def _matrix(cls, *params):
theta = params[0]
interface = qml.math.get_interface(theta)

c = qml.math.cos(theta / 2)
s = qml.math.sin(theta / 2)
z = qml.math.zeros([4], like=interface)

if qml.math.get_interface(theta) == "tensorflow":
if interface == "tensorflow":
c = qml.math.cast_like(c, 1j)
s = qml.math.cast_like(s, 1j)
z = qml.math.cast_like(z, 1j)

js = -1j * s

mat = [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, c, js], [0, 0, js, c]]
return qml.math.stack([qml.math.stack(row) for row in mat])
mat = qml.math.diag([1, 1, c, c])
return mat + qml.math.stack(
[z, z, qml.math.stack([0, 0, 0, js]), qml.math.stack([0, 0, js, 0])]
)

@staticmethod
def decomposition(theta, wires):
Expand Down Expand Up @@ -928,12 +937,16 @@ def label(self, decimals=None, base_label=None):
@classmethod
def _matrix(cls, *params):
theta = params[0]
interface = qml.math.get_interface(theta)

c = qml.math.cos(theta / 2)
s = qml.math.sin(theta / 2)
z = qml.math.zeros([4], like=interface)

mat = [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, c, -s], [0, 0, s, c]]
return qml.math.stack([qml.math.stack(row) for row in mat])
mat = qml.math.diag([1, 1, c, c])
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Is this necessary here, since CRY is purely real?

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weirdly yes, for the same reason that OrbitalRotation also raises the warning. I'm not entirely sure why this is an issue though?? So weird.

return mat + qml.math.stack(
[z, z, qml.math.stack([0, 0, 0, -s]), qml.math.stack([0, 0, s, 0])]
)

@staticmethod
def decomposition(theta, wires):
Expand Down Expand Up @@ -1396,20 +1409,15 @@ def _matrix(cls, *params):

c = qml.math.cos(phi / 2)
s = qml.math.sin(phi / 2)
Y = qml.math.convert_like(np.eye(4)[::-1].copy(), phi)

if qml.math.get_interface(phi) == "tensorflow":
c = qml.math.cast_like(c, 1j)
s = qml.math.cast_like(s, 1j)
Y = qml.math.cast_like(Y, 1j)

js = -1j * s

mat = [
[c, 0, 0, js],
[0, c, js, 0],
[0, js, c, 0],
[js, 0, 0, c],
]

return qml.math.stack([qml.math.stack(row) for row in mat])
mat = qml.math.diag([c, c, c, c]) - 1j * s * Y
return mat

@staticmethod
def decomposition(phi, wires):
Expand Down Expand Up @@ -1471,21 +1479,14 @@ def _matrix(cls, *params):

c = qml.math.cos(phi / 2)
s = qml.math.sin(phi / 2)
Y = qml.math.convert_like(np.diag([1, -1, -1, 1])[::-1].copy(), phi)

if qml.math.get_interface(phi) == "tensorflow":
c = qml.math.cast_like(c, 1j)
s = qml.math.cast_like(s, 1j)
Y = qml.math.cast_like(Y, 1j)

js = 1j * s

mat = [
[c, 0.0, 0.0, js],
[0.0, c, -js, 0.0],
[0.0, -js, c, 0.0],
[js, 0.0, 0.0, c],
]

return qml.math.stack([qml.math.stack(row) for row in mat])
return qml.math.diag([c, c, c, c]) + 1j * s * Y

def adjoint(self):
(phi,) = self.parameters
Expand Down
94 changes: 25 additions & 69 deletions pennylane/ops/qubit/qchem_ops.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -95,8 +95,9 @@ def _matrix(cls, *params):
c = qml.math.cos(theta / 2)
s = qml.math.sin(theta / 2)

mat = [[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, 1]]
return qml.math.stack([qml.math.stack(row) for row in mat])
mat = qml.math.diag([1, c, c, 1])
off_diag = qml.math.convert_like(np.diag([0, 1, -1, 0])[::-1].copy(), theta)
return mat + s * qml.math.cast_like(off_diag, s)

@staticmethod
def decomposition(theta, wires):
Expand Down Expand Up @@ -163,9 +164,9 @@ def _matrix(cls, *params):
s = qml.math.cast_like(s, 1j)

e = qml.math.exp(-1j * theta / 2)

mat = [[e, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, e]]
return qml.math.stack([qml.math.stack(row) for row in mat])
mat = qml.math.diag([e, 0, 0, e]) + qml.math.diag([0, c, c, 0])
off_diag = qml.math.convert_like(np.diag([0, 1, -1, 0])[::-1].copy(), theta)
return mat + s * qml.math.cast_like(off_diag, s)

@staticmethod
def decomposition(theta, wires):
Expand Down Expand Up @@ -238,9 +239,9 @@ def _matrix(cls, *params):
s = qml.math.cast_like(s, 1j)

e = qml.math.exp(1j * theta / 2)

mat = [[e, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0], [0, 0, 0, e]]
return qml.math.stack([qml.math.stack(row) for row in mat])
mat = qml.math.diag([e, 0, 0, e]) + qml.math.diag([0, c, c, 0])
off_diag = qml.math.convert_like(np.diag([0, 1, -1, 0])[::-1].copy(), theta)
return mat + s * qml.math.cast_like(off_diag, s)

@staticmethod
def decomposition(theta, wires):
Expand Down Expand Up @@ -332,26 +333,10 @@ def _matrix(cls, *params):
c = qml.math.cos(theta / 2)
s = qml.math.sin(theta / 2)

mat = [
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, c, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -s, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, s, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, c, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
]

return qml.math.stack([qml.math.stack(row) for row in mat])
mat = qml.math.diag([1.0] * 3 + [c] + [1.0] * 8 + [c] + [1.0] * 3)
mat = qml.math.scatter_element_add(mat, (3, 12), -s)
mat = qml.math.scatter_element_add(mat, (12, 3), s)
return mat

@staticmethod
def decomposition(theta, wires):
Expand Down Expand Up @@ -454,26 +439,12 @@ def _matrix(cls, *params):

e = qml.math.exp(1j * theta / 2)

mat = [
[e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, c, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -s, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, s, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, c, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e],
]

return qml.math.stack([qml.math.stack(row) for row in mat])
mat = qml.math.diag([e] * 3 + [0] + [e] * 8 + [0] + [e] * 3)
mat = qml.math.scatter_element_add(mat, (3, 3), c)
mat = qml.math.scatter_element_add(mat, (3, 12), -s)
mat = qml.math.scatter_element_add(mat, (12, 3), s)
mat = qml.math.scatter_element_add(mat, (12, 12), c)
return mat

def adjoint(self):
(theta,) = self.parameters
Expand Down Expand Up @@ -539,27 +510,12 @@ def _matrix(cls, *params):
s = qml.math.cast_like(s, 1j)

e = qml.math.exp(-1j * theta / 2)

mat = [
[e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, c, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -s, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, s, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, c, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, e],
]

return qml.math.stack([qml.math.stack(row) for row in mat])
mat = qml.math.diag([e] * 3 + [0] + [e] * 8 + [0] + [e] * 3)
mat = qml.math.scatter_element_add(mat, (3, 3), c)
mat = qml.math.scatter_element_add(mat, (3, 12), -s)
mat = qml.math.scatter_element_add(mat, (12, 3), s)
mat = qml.math.scatter_element_add(mat, (12, 12), c)
return mat

def adjoint(self):
(theta,) = self.parameters
Expand Down
2 changes: 1 addition & 1 deletion tests/ops/qubit/test_hamiltonian.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -46,7 +46,7 @@
try:
import torch

COEFFS_PARAM_INTERFACE.append((torch.tensor([-0.05, 0.17]), torch.tensor([1.7]), "torch"))
COEFFS_PARAM_INTERFACE.append((torch.tensor([-0.05, 0.17]), torch.tensor(1.7), "torch"))
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except ImportError:
pass

Expand Down