Bumping tensorflow-macos to v2.15 (#375)

**Context:**
Bumping `tensorflow-macos` to v2.15

.github/CHANGELOG.md

@@ -1,4 +1,31 @@
-# Release 0.7.1 (current release)
+# Release 0.7.2 (current release)
+
+### New features
+* Added a function ``to_fock`` to map different representations into Fock representation. 
+  [(#355)](https://github.com/XanaduAI/MrMustard/pull/355)
+
+* Added a new Abc triple for s-parametrized displacement gate. 
+  [(#368)](https://github.com/XanaduAI/MrMustard/pull/368)
+
+### Breaking changes
+
+### Improvements
+
+### Bug fixes
+
+### Documentation
+
+### Tests
+
+### Contributors
+[Samuele Ferracin](https://github.com/SamFerracin),
+[Yuan Yao](https://github.com/sylviemonet)
+[Filippo Miatto](https://github.com/ziofil)
+
+
+---
+
+# Release 0.7.1
 
 ### New features
 * Added functions to generate the ``(A, b, c)`` triples for the Fock-Bargmann representation of

mrmustard/math/lattice/strategies/compactFock/inputValidation.py

@@ -18,7 +18,42 @@
 from mrmustard.math.lattice.strategies.compactFock.singleLeftoverMode_grad import (
     fock_representation_1leftoverMode_grad,
 )
-from thewalrus._hafnian import input_validation
+
+
+def input_validation(A, rtol=1e-05, atol=1e-08):
+    """Checks that the matrix A satisfies the requirements for Hafnian calculation.
+    These include:
+    * That the ``A`` is a NumPy array
+    * That ``A`` is square
+    * That ``A`` does not contain any NaNs
+    * That ``A`` is symmetric
+
+    .. note:: This function is an adaptation of the of an analogous method in The Walrus.
+
+    Args:
+        A (array): a NumPy array.
+        rtol (float): the relative tolerance parameter used in ``np.allclose``
+        atol (float): the absolute tolerance parameter used in ``np.allclose``
+
+    Returns:
+        bool: returns ``True`` if the matrix satisfies all requirements
+    """
+
+    if not isinstance(A, np.ndarray):
+        raise TypeError("Input matrix must be a NumPy array.")
+
+    n = A.shape
+
+    if n[0] != n[1]:
+        raise ValueError("Input matrix must be square.")
+
+    if np.isnan(A).any():
+        raise ValueError("Input matrix must not contain NaNs.")
+
+    if not np.allclose(A, A.T, rtol=rtol, atol=atol):
+        raise ValueError("Input matrix must be symmetric.")
+
+    return True
 
 
 def hermite_multidimensional_diagonal(A, B, G0, cutoffs, rtol=1e-05, atol=1e-08):

mrmustard/physics/gaussian.py

@@ -18,8 +18,6 @@
 
 from typing import Any, Optional, Sequence, Tuple, Union
 
-from thewalrus.quantum import is_pure_cov
-
 from mrmustard import math, settings
 from mrmustard.math.tensor_wrappers.xptensor import XPMatrix, XPVector
 from mrmustard.utils.typing import Matrix, Scalar, Vector
@@ -660,11 +658,6 @@ def number_cov(cov: Matrix, means: Vector) -> Matrix:
     )
 
 
-def is_mixed_cov(cov: Matrix) -> bool:  # TODO: deprecate
-    r"""Returns ``True`` if the covariance matrix is mixed, ``False`` otherwise."""
-    return not is_pure_cov(math.asnumpy(cov))
-
-
 def trace(cov: Matrix, means: Vector, Bmodes: Sequence[int]) -> Tuple[Matrix, Vector]:
     r"""Returns the covariances and means after discarding the specified modes.