MPS Synthesis #7752
MPS Synthesis #7752
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- Fixed `_parser.py` formatting issues.
- Added testers for `cp`, `c3x`, `c4x`, `csx`, and `c3sqrtx` gates. - Fixed u0 gate. - Removed `r`, `ryy`, `cu2`, and `iswap` as they are not part of qelib 1.
- Fixes gate definition for `id` to address git-blame note. - Fixes gate definition for `p` to use ZPowGate. - Adds rccx and rc3x using `cirq.MatrixGate`. - Adds missing testers for cry and crz.
- Fixed failed CIs.
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #7752 +/- ##
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- Coverage 99.38% 99.38% -0.01%
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Files 1090 1093 +3
Lines 98248 98388 +140
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+ Hits 97643 97782 +139
- Misses 605 606 +1 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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I added single qubit state prep manually, it should be removed when cirq gets state preparation added. I'll be sure to remind in the next cirq cynq. |
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I need to check a few things with colleagues; I will have an update early next week. |
pavoljuhas
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pavoljuhas
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Please see inline comments. Otherwise LGTM, thank you for contributing this.
PS: I'd like to loop in @tanujkhattar for more comments after we converge on the initial review.
| derived from the input matrix. If a column is (approximately) zero, it | ||
| is replaced with a random vector. | ||
| """ | ||
| num_rows, num_columns = matrix.shape |
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The only caller of this function passes a square matrix.
Can we require the matrix to be square here?
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Okay if I add this for safety? I'll add it for now, and later before merge I can remove it if you don't want it.
if matrix.shape[0] != matrix.shape[1]:
raise ValueError(
"Input matrix is expected to be square. "
f"Got shape {matrix.shape} instead. "
"If this is not an error, please file an issue at "
"https://github.com/quantumlib/Cirq/issues"
)| unitary = np.zeros((num_rows, num_columns), dtype=np.complex128) | ||
| orthonormal_basis: list[NDArray[np.complex128]] = [] |
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Please rewrite using scipy.linalg.org and scipy.linalg.null_space
| state = np.random.rand(2**N) + 1j * np.random.rand(2**N) | ||
| state /= np.linalg.norm(state) |
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Please reuse cirq.testing.random_superposition instead
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Something I noticed is that cirq.testing.random_superposition is not sampling from a uniform distribution, but a normal distribution. Is it okay if I make a quick PR to change np.random.randn to np.random.rand to fix it?
3 participants
Closes #7650 .