diff --git a/pennylane/devices/qubit/apply_operation.py b/pennylane/devices/qubit/apply_operation.py
index 1fd21dfaee4..c64725f9e9b 100644
--- a/pennylane/devices/qubit/apply_operation.py
+++ b/pennylane/devices/qubit/apply_operation.py
@@ -403,14 +403,9 @@ def apply_pauliz(op: qml.Z, state, is_state_batched: bool = False, debugger=None
 def apply_phaseshift(op: qml.PhaseShift, state, is_state_batched: bool = False, debugger=None, **_):
     """Apply PhaseShift to state."""
 
-    axis = op.wires[0] + is_state_batched
-    n_dim = math.ndim(state)
-
-    if n_dim >= 9 and math.get_interface(state) == "tensorflow":
-        return apply_operation_tensordot(op, state, is_state_batched=is_state_batched)
-
-    params = qml.math.atleast_1d(op.parameters[0])
+    params = op.parameters[0]
     if is_state_batched or (op.batch_size is not None and len(params) > 1):
+        params = math.atleast_1d(params)
         slices = []
         for i, p in enumerate(params):
             slices.append(
@@ -422,11 +417,17 @@ def apply_phaseshift(op: qml.PhaseShift, state, is_state_batched: bool = False,
             )
         return math.stack(slices, axis=0)
 
+    axis = op.wires[0] + is_state_batched
+    n_dim = math.ndim(state)
+
+    if n_dim >= 9 and math.get_interface(state) == "tensorflow":
+        return apply_operation_tensordot(op, state, is_state_batched=is_state_batched)
+
     sl_0 = _get_slice(0, axis, n_dim)
     sl_1 = _get_slice(1, axis, n_dim)
 
     # must be first state and then -1 because it breaks otherwise
-    state1 = math.cast(state[sl_1], dtype=complex) * math.exp(1.0j * params)
+    state1 = math.multiply(state[sl_1], math.exp(1j * params))
     state = math.stack([state[sl_0], state1], axis=axis)
     if op.batch_size == 1:
         state = math.stack([state], axis=0)
diff --git a/tests/devices/qubit/test_apply_operation.py b/tests/devices/qubit/test_apply_operation.py
index 3e517879b78..5d6f1da11bf 100644
--- a/tests/devices/qubit/test_apply_operation.py
+++ b/tests/devices/qubit/test_apply_operation.py
@@ -802,7 +802,7 @@ def test_broadcasted_op(self, op, method, ml_framework):
     @pytest.mark.parametrize("op", unbroadcasted_ops)
     def test_broadcasted_state(self, op, method, ml_framework):
         """Tests that unbatched operations are applied correctly to a batched state."""
-        state = np.ones((3, 2, 2, 2)) / np.sqrt(8)
+        state = np.ones((3, 2, 2, 2), dtype=complex) / np.sqrt(8)
 
         res = method(op, qml.math.asarray(state, like=ml_framework), is_state_batched=True)
         missing_wires = 3 - len(op.wires)
@@ -819,7 +819,7 @@ def test_broadcasted_op_broadcasted_state(self, op, method, ml_framework):
         if method is apply_operation_tensordot:
             pytest.skip("Tensordot doesn't support batched operator and batched state.")
 
-        state = np.ones((3, 2, 2, 2)) / np.sqrt(8)
+        state = np.ones((3, 2, 2, 2), dtype=complex) / np.sqrt(8)
 
         res = method(op, qml.math.asarray(state, like=ml_framework), is_state_batched=True)
         missing_wires = 3 - len(op.wires)