Fix qml.math.get_interface for scipy input

doc/releases/changelog-dev.md

@@ -419,6 +419,10 @@
 
 <h3>Bug fixes 🐛</h3>
 
+* `qml.math.get_interface` now correctly extracts the `"scipy"` interface if provided a list/array
+  of sparse matrices. 
+  [(#7015)](https://github.com/PennyLaneAI/pennylane/pull/7015)
+
 * `qml.capture.PlxprInterpreter` now flattens pytree arguments before evaluation.
   [(#6975)](https://github.com/PennyLaneAI/pennylane/pull/6975)
 

pennylane/math/interface_utils.py

@@ -85,6 +85,7 @@ def __hash__(self):
 """list[Interface]: allowed interface names"""
 
 
+# pylint: disable=too-many-return-statements
 def get_interface(*values):
     """Determines the correct framework to dispatch to given a tensor-like object or a
     sequence of tensor-like objects.
@@ -139,17 +140,11 @@ def get_interface(*values):
             UserWarning,
         )
 
-    if "tensorflow" in interfaces:
-        return "tensorflow"
+    priority_interfaces = {"tensorflow", "torch", "jax", "autograd", "scipy"}
+    matching_interface = priority_interfaces.intersection(interfaces)
 
-    if "torch" in interfaces:
-        return "torch"
-
-    if "jax" in interfaces:
-        return "jax"
-
-    if "autograd" in interfaces:
-        return "autograd"
+    if matching_interface:
+        return matching_interface.pop()
 
     return "numpy"
 

pennylane/math/multi_dispatch.py

@@ -17,7 +17,6 @@
 from collections.abc import Sequence
 
 # pylint: disable=wrong-import-order
-import autoray as ar
 import numpy as onp
 from autograd.numpy.numpy_boxes import ArrayBox
 from autoray import numpy as np
@@ -165,12 +164,11 @@ def kron(*args, like=None, **kwargs):
 
     if like == "torch":
         mats = [
-            ar.numpy.asarray(arg, like="torch") if isinstance(arg, onp.ndarray) else arg
-            for arg in args
+            np.asarray(arg, like="torch") if isinstance(arg, onp.ndarray) else arg for arg in args
         ]
-        return ar.numpy.kron(*mats)
+        return np.kron(*mats)
 
-    return ar.numpy.kron(*args, like=like, **kwargs)
+    return np.kron(*args, like=like, **kwargs)
 
 
 @multi_dispatch(argnum=[0], tensor_list=[0])
@@ -309,11 +307,11 @@ def matmul(tensor1, tensor2, like=None):
     """Returns the matrix product of two tensors."""
     if like == "torch":
         if get_interface(tensor1) != "torch":
-            tensor1 = ar.numpy.asarray(tensor1, like="torch")
+            tensor1 = np.asarray(tensor1, like="torch")
         if get_interface(tensor2) != "torch":
-            tensor2 = ar.numpy.asarray(tensor2, like="torch")
+            tensor2 = np.asarray(tensor2, like="torch")
         tensor2 = cast_like(tensor2, tensor1)  # pylint: disable=arguments-out-of-order
-    return ar.numpy.matmul(tensor1, tensor2, like=like)
+    return np.matmul(tensor1, tensor2, like=like)
 
 
 @multi_dispatch(argnum=[0, 1])

pennylane/math/single_dispatch.py

@@ -55,17 +55,23 @@ def _builtins_shape(x):
     return ar.shape(x)
 
 
+def _builtins_coerce(x, like=None):
+    return ar.numpy.asarray(x, like=like)
+
+
 ar.register_function("builtins", "ndim", _builtins_ndim)
 ar.register_function("builtins", "shape", _builtins_shape)
+ar.register_function("builtins", "coerce", _builtins_coerce)
 ar.register_function("builtins", "logical_mod", lambda x, y: x % y)
 ar.register_function("builtins", "logical_xor", lambda x, y: x ^ y)
 
 # -------------------------------- SciPy --------------------------------- #
 # the following is required to ensure that SciPy sparse Hamiltonians passed to
 # qml.SparseHamiltonian are not automatically 'unwrapped' to dense NumPy arrays.
 ar.register_function("scipy", "to_numpy", lambda x: x)
-
+ar.register_function("scipy", "coerce", ar.numpy.coerce)
 ar.register_function("scipy", "shape", np.shape)
+ar.register_function("scipy", "dot", np.dot)
 ar.register_function("scipy", "conj", np.conj)
 ar.register_function("scipy", "transpose", np.transpose)
 ar.register_function("scipy", "ndim", np.ndim)

tests/math/test_functions.py

@@ -1017,12 +1017,20 @@ def test_expand_dims_tf(self, shape, axis, new_shape):
 @pytest.mark.parametrize("t,interface", interface_test_data)
 def test_get_interface(t, interface):
     """Test that the interface of a tensor-like object
-
     is correctly returned."""
     res = fn.get_interface(t)
     assert res == interface
 
 
+def test_get_interface_scipy():
+    """Test that the interface of a scipy sparse matrix is correctly returned."""
+    matrix = sci.sparse.csr_matrix([[0, 1], [1, 0]])
+
+    assert fn.get_interface(matrix) == "scipy"
+    assert fn.get_interface(matrix, matrix) == "scipy"
+    assert fn.get_interface(*[matrix, matrix]) == "scipy"
+
+
 # pylint: disable=too-few-public-methods
 class TestInterfaceEnum:
     """Test the Interface enum class"""