Suppress superfluous warnings from numpy (#6599)

* Suppress superfluous warnings from numpy

- On MacOS, with numpy ~1.26, np.linalg.det gives
spurious warnings when passed a complex array.
- Suppress these warnings, as they are just annoying
and do not signify anything.

Author: dstrain115

cirq-core/cirq/linalg/decompositions.py

@@ -222,8 +222,9 @@ def kron_factor_4x4_to_2x2s(matrix: np.ndarray) -> Tuple[complex, np.ndarray, np
             f2[(a & 1) ^ i, (b & 1) ^ j] = matrix[a ^ i, b ^ j]
 
     # Rescale factors to have unit determinants.
-    f1 /= np.sqrt(np.linalg.det(f1)) or 1
-    f2 /= np.sqrt(np.linalg.det(f2)) or 1
+    with np.errstate(divide="ignore", invalid="ignore"):
+        f1 /= np.sqrt(np.linalg.det(f1)) or 1
+        f2 /= np.sqrt(np.linalg.det(f2)) or 1
 
     # Determine global phase.
     g = matrix[a, b] / (f1[a >> 1, b >> 1] * f2[a & 1, b & 1])
@@ -965,7 +966,8 @@ def kak_vector(
     # The algorithm in the appendix mentioned above is slightly incorrect in
     # that it only works for elements of SU(4). A phase correction must be
     # added to deal with U(4).
-    phases = np.log(-1j * np.linalg.det(unitary)).imag + np.pi / 2
+    with np.errstate(divide="ignore", invalid="ignore"):
+        phases = np.log(-1j * np.linalg.det(unitary)).imag + np.pi / 2
     evals *= np.exp(-1j * phases / 2)[..., np.newaxis]
 
     # The following steps follow the appendix exactly.

cirq-core/cirq/linalg/diagonalize.py

@@ -255,10 +255,11 @@ def bidiagonalize_unitary_with_special_orthogonals(
     )
 
     # Convert to special orthogonal w/o breaking diagonalization.
-    if np.linalg.det(left) < 0:
-        left[0, :] *= -1
-    if np.linalg.det(right) < 0:
-        right[:, 0] *= -1
+    with np.errstate(divide="ignore", invalid="ignore"):
+        if np.linalg.det(left) < 0:
+            left[0, :] *= -1
+        if np.linalg.det(right) < 0:
+            right[:, 0] *= -1
 
     diag = combinators.dot(left, mat, right)
 

cirq-core/cirq/linalg/predicates.py

@@ -91,9 +91,10 @@ def is_special_orthogonal(matrix: np.ndarray, *, rtol: float = 1e-5, atol: float
     Returns:
         Whether the matrix is special orthogonal within the given tolerance.
     """
-    return is_orthogonal(matrix, rtol=rtol, atol=atol) and (
-        matrix.shape[0] == 0 or np.allclose(np.linalg.det(matrix), 1, rtol=rtol, atol=atol)
-    )
+    with np.errstate(divide="ignore", invalid="ignore"):
+        return is_orthogonal(matrix, rtol=rtol, atol=atol) and (
+            matrix.shape[0] == 0 or np.allclose(np.linalg.det(matrix), 1, rtol=rtol, atol=atol)
+        )
 
 
 def is_unitary(matrix: np.ndarray, *, rtol: float = 1e-5, atol: float = 1e-8) -> bool:
@@ -128,9 +129,10 @@ def is_special_unitary(matrix: np.ndarray, *, rtol: float = 1e-5, atol: float =
         Whether the matrix is unitary with unit determinant within the given
         tolerance.
     """
-    return is_unitary(matrix, rtol=rtol, atol=atol) and (
-        matrix.shape[0] == 0 or np.allclose(np.linalg.det(matrix), 1, rtol=rtol, atol=atol)
-    )
+    with np.errstate(divide="ignore", invalid="ignore"):
+        return is_unitary(matrix, rtol=rtol, atol=atol) and (
+            matrix.shape[0] == 0 or np.allclose(np.linalg.det(matrix), 1, rtol=rtol, atol=atol)
+        )
 
 
 def is_normal(matrix: np.ndarray, *, rtol: float = 1e-5, atol: float = 1e-8) -> bool:

cirq-core/cirq/linalg/transformations.py

@@ -592,7 +592,8 @@ def to_special(u: np.ndarray) -> np.ndarray:
     Returns:
         the special unitary matrix
     """
-    return u * (np.linalg.det(u) ** (-1 / len(u)))
+    with np.errstate(divide="ignore", invalid="ignore"):
+        return u * (np.linalg.det(u) ** (-1 / len(u)))
 
 
 def state_vector_kronecker_product(t1: np.ndarray, t2: np.ndarray) -> np.ndarray:

cirq-core/cirq/testing/lin_alg_utils.py

@@ -133,7 +133,8 @@ def random_special_unitary(
         The sampled special unitary.
     """
     r = random_unitary(dim, random_state=random_state)
-    r[0, :] /= np.linalg.det(r)
+    with np.errstate(divide="ignore", invalid="ignore"):
+        r[0, :] /= np.linalg.det(r)
     return r
 
 
@@ -152,8 +153,9 @@ def random_special_orthogonal(
         The sampled special orthogonal matrix.
     """
     m = random_orthogonal(dim, random_state=random_state)
-    if np.linalg.det(m) < 0:
-        m[0, :] *= -1
+    with np.errstate(divide="ignore", invalid="ignore"):
+        if np.linalg.det(m) < 0:
+            m[0, :] *= -1
     return m
 
 

cirq-core/cirq/transformers/analytical_decompositions/controlled_gate_decomposition.py

@@ -47,7 +47,9 @@ def _decompose_abc(matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarr
     See [1], chapter 4.
     """
     assert matrix.shape == (2, 2)
-    delta = np.angle(np.linalg.det(matrix)) * 0.5
+    with np.errstate(divide="ignore", invalid="ignore"):
+        # On MacOS, np.linalg.det emits superflous warnings
+        delta = np.angle(np.linalg.det(matrix)) * 0.5
     alpha = np.angle(matrix[0, 0]) + np.angle(matrix[0, 1]) - 2 * delta
     beta = np.angle(matrix[0, 0]) - np.angle(matrix[0, 1])