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Add dimension checking to QubitUnitary. #1439

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merged 15 commits into from
Jul 7, 2021
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@glassnotes glassnotes commented Jun 29, 2021

Context: Development of #1427 uncovered a bug where QubitUnitary was not checking whether the number of wires matched with the size of the unitary matrix.

Description of the Change: Adds an __init__ method to QubitUnitary to verify dimensionality and unitarity. Changes up the _matrix method accordingly. Also, converts QubitUnitary functions to use qml.math, and separates QubitUnitary tests into separate integration tests for all interfaces.

Benefits: Fixes the bug and clears up ambiguous behaviour such as

qml.QubitUnitary(qml.Hadamard(wires=0).matrix, wires=[0, 1])

which would previously run without warnings.

Possible Drawbacks: None

Related GitHub Issues: None

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Hello. You may have forgotten to update the changelog!
Please edit .github/CHANGELOG.md with:

  • A one-to-two sentence description of the change. You may include a small working example for new features.
  • A link back to this PR.
  • Your name (or GitHub username) in the contributors section.

@glassnotes glassnotes added the bug 🐛 Something isn't working label Jun 29, 2021
@@ -125,6 +125,9 @@ def test_heisenberg(self, test_class, tol):
def test_operation_init(self, test_class, monkeypatch):
"Operation subclass initialization."

if test_class == qml.QubitUnitary:
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I had to write a separate test for the new __init__ method (see below). I'm actually not sure why it worked before; the issue was that in line 135, the number of wires is computed and then used to construct a list of wires, but QubitUnitary works on any number of wires so it was yielding a list of length 0 and causing the test to fail.

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🤔

op = qml.QubitUnitary(U, wires=wires)

assert op.name == qml.QubitUnitary.__name__
assert np.allclose([U], op.data)
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This line and the next line are maybe redundant?

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😆

@glassnotes glassnotes requested a review from josh146 June 29, 2021 15:36
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codecov bot commented Jun 29, 2021

Codecov Report

Merging #1439 (259482d) into master (63f493d) will increase coverage by 0.00%.
The diff coverage is 100.00%.

Impacted file tree graph

@@           Coverage Diff           @@
##           master    #1439   +/-   ##
=======================================
  Coverage   98.23%   98.23%           
=======================================
  Files         163      163           
  Lines       12076    12082    +6     
=======================================
+ Hits        11863    11869    +6     
  Misses        213      213           
Impacted Files Coverage Δ
pennylane/ops/qubit.py 98.65% <100.00%> (+<0.01%) ⬆️

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Thanks @glassnotes! I left some minor comments in the review itself, but have a bigger request here:

Rather than having a unit test for __init__ (which seems a bit redundant), perhaps it makes more sense to have an integration test, which does the following:

  • Tests the validation catches incorrect input and allows correct input using NumPy arrays and tensors
  • Tests that ControlledQubitUnitary continues to avoid the validation?

# For pure QubitUnitary operations (not controlled), check that the number
# of wires fits the dimensions of the matrix
if not isinstance(self, ControlledQubitUnitary):
U = np.asarray(params[0])
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I realize that the previous version of the code does this, but, I only just noticed it 😆

We can't assume that params[0] will be a NumPy array - it could very easily be a TensorFlow or a PyTorch tensor. As a result, best to use qml.math here. E.g.,

U = params[0]
shape = qml.math.shape(U)

(and then down below, qml.math.dot and qml.math.conj)


@classmethod
def _matrix(cls, *params):
return np.asarray(params[0])
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Same comment here, this should probably just return params[0]?

@@ -125,6 +125,9 @@ def test_heisenberg(self, test_class, tol):
def test_operation_init(self, test_class, monkeypatch):
"Operation subclass initialization."

if test_class == qml.QubitUnitary:
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🤔

op = qml.QubitUnitary(U, wires=wires)

assert op.name == qml.QubitUnitary.__name__
assert np.allclose([U], op.data)
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😆

@glassnotes
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Thanks @glassnotes! I left some minor comments in the review itself, but have a bigger request here:

Rather than having a unit test for __init__ (which seems a bit redundant), perhaps it makes more sense to have an integration test, which does the following:

* Tests the validation catches incorrect input and allows correct input using NumPy arrays and tensors

* Tests that `ControlledQubitUnitary` continues to _avoid_ the validation?

@josh146 probably, then, it would be best to split out the QubitUnitary tests into their own test class like the excitation operations, since otherwise that will be quite a big chunk of test living in the TestOperation class. Furthermore, the extensive decomposition tests from #1427 could be included in the new class as well.

@pytest.mark.parametrize(
"U", [np.array([0]), np.array([1, 0, 0, 1]), np.array([[[1, 0], [0, 1]]])]
)
def test_qubit_unitary_not_matrix_exception(self, U):
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I removed this test because the case is already covered in the unitary_exceptions test, where U[1:] is passed as input.

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Nice 👍

@glassnotes glassnotes requested a review from josh146 July 7, 2021 14:51
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@josh146 probably, then, it would be best to split out the QubitUnitary tests into their own test class like the excitation operations, since otherwise that will be quite a big chunk of test living in the TestOperation class. Furthermore, the extensive decomposition tests from #1427 could be included in the new class as well.

I think this makes a lot of sense, it has annoyed me for a while that that test file is so large and hard to add to!

Also new tests look 💯, thanks so much @glassnotes for adding them. It will make the QubitUnitary operation a lot more bug resistant going forward.

@pytest.mark.parametrize(
"U", [np.array([0]), np.array([1, 0, 0, 1]), np.array([[[1, 0], [0, 1]]])]
)
def test_qubit_unitary_not_matrix_exception(self, U):
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Nice 👍

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