Skip to content

Fix #855: don't call cirq.unitary() on non-unitary matrices #857

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
mhucka merged 4 commits into from
Aug 12, 2025
Merged

Fix #855: don't call cirq.unitary() on non-unitary matrices #857

Changes from 3 commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
e141008
Fix #855: don't call cirq.unitary() on non-unitary matrices
mhucka Aug 10, 2025
a82b6ec
Fix formatting complaint from black
mhucka Aug 10, 2025
3576529
Add comment explaining what's happening
mhucka Aug 11, 2025
4f45403
Update qsimcirq_test.py
mhucka Aug 12, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
22 changes: 16 additions & 6 deletions qsimcirq_tests/qsimcirq_test.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1057,7 +1057,17 @@ def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)

def _mixture_(self):
return [(prob, cirq.unitary(op)) for prob, op, in self._prob_op_pairs]
# Cirq's mixture() function in mixture_protocol.py returns tuples of
# the form (probability, unitary peration). It does this by applying
Copy link
Copy Markdown
Collaborator

@pavoljuhas pavoljuhas Aug 11, 2025

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

peration --> operation

# Cirq's unitary() function to the second elements of the tuples
# returned from here. Now, the values in self._prob_op_pairs will be
# tuples of the form (probability, NoiseStep). NoiseStep defines a
# _unitary_() method that simply returns the array as-is. Thus, when
# Cirq's mixture() function gets the value returned here and calls
# unitary() on those NoiseStep objects, the values unitary() returns
# will not actually be unitary. This is done knowingly. The nonunitary
# values are eventually normalized in test_multi_qubit_noise().
return [(prob, op) for prob, op, in self._prob_op_pairs]
Copy link
Copy Markdown

@dstrain115 dstrain115 Aug 11, 2025

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Is _mixture a standard protocol for qsim or is this only used for this test? This is changing the return type for this method.

Copy link
Copy Markdown

@dstrain115 dstrain115 Aug 11, 2025

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Oh, I didn't realize op was already a np.array.

Copy link
Copy Markdown
Collaborator Author

@mhucka mhucka Aug 11, 2025

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Hmm. It's true that the 2nd element in the tuples in self._prob_op_pairs are actually NoiseStep objects. Lemme see if I can improve this.

Copy link
Copy Markdown
Collaborator Author

@mhucka mhucka Aug 11, 2025

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

OK, with the way this all routed between different functions, I can't find a way to avoid changing this return type without rewriting test_multi_qubit_noise(). Given that this object class is only used in this file and only for that one function, that seems like it would be poor ROI.

I added a comment explaning what is happening. @dstrain115 can you check that it makes sense?

Copy link
Copy Markdown
Collaborator Author

@mhucka mhucka Aug 11, 2025

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Oh, I didn't realize op was already a np.array.

Actuallly, I was wrong about that in my earlier comment (which I deleted too slowly to save you typing that reply -- sorry about that). At this point in the flow of things, it's not an ndarray. The whole flow is a bit more complicated, which hopefully the new comment clarifies.



@pytest.mark.parametrize(
Expand Down Expand Up @@ -2025,11 +2035,11 @@ def test_qsimcirq_identity_expectation_value():
for w, pauli in objs:
pauli = pauli[::-1]
hamiltonian += float(w) * cirq.PauliString(
cirq.I(cirq.LineQubit(i))
if p == "I"
else cirq.Z(cirq.LineQubit(i))
if p == "Z"
else None
(
cirq.I(cirq.LineQubit(i))
if p == "I"
else cirq.Z(cirq.LineQubit(i)) if p == "Z" else None
)
for i, p in enumerate(pauli)
)

Expand Down
Loading