Fixing dismatch between dense matrix() and sparse repr sparse_matrix() of GlobalPhase

pennylane/math/matrix_manipulation.py

@@ -113,9 +113,12 @@
     if (wire_order is None) or (wire_order == wires):
         return mat
 
-    if not wires and qml.math.shape(mat) == (qudit_dim, qudit_dim):
+    if not wires:
         # global phase
-        wires = wire_order[0:1]
+        if qml.math.shape(mat) == (qudit_dim, qudit_dim):
+            wires = wire_order[0:1]
+        elif qml.math.shape(mat) == (1, 1):
+            return complex(mat[0, 0])
 
     wires = list(wires)
     wire_order = list(wire_order)

pennylane/ops/identity.py

@@ -455,6 +455,10 @@ def matrix(self, wire_order=None):
         n_wires = len(wire_order) if wire_order else len(self.wires)
         return self.compute_matrix(self.data[0], n_wires=n_wires)
 
+    def sparse_matrix(self, wire_order=None):
+        n_wires = len(wire_order) if wire_order else len(self.wires)
+        return self.compute_sparse_matrix(self.data[0], n_wires=n_wires)
+
     def adjoint(self):
         return GlobalPhase(-1 * self.data[0], self.wires)
 

tests/ops/qubit/test_parametric_ops.py

@@ -129,6 +129,7 @@
 ]
 
 ALL_OPERATIONS = NON_PARAMETRIZED_OPERATIONS + PARAMETRIZED_OPERATIONS
+SPARSE_OPERATIONS = [op for op in ALL_OPERATIONS if op.has_sparse_matrix]
 
 
 def dot_broadcasted(a, b):
@@ -139,6 +140,15 @@ def multi_dot_broadcasted(matrices):
     return reduce(dot_broadcasted, matrices)
 
 
+class TestSparseOperators:
+    @pytest.mark.parametrize("op", SPARSE_OPERATIONS)
+    def test_validity(self, op):
+        """Test that the operations are valid."""
+        assert np.allclose(
+            op.sparse_matrix().toarray(), qml.math.asarray(op.matrix(), like="numpy")
+        )
+
+
 class TestOperations:
     @pytest.mark.parametrize("op", ALL_OPERATIONS + BROADCASTED_OPERATIONS)
     def test_parametrized_op_copy(self, op, tol):