Fixes for new autoray version

pennylane/math/multi_dispatch.py

@@ -769,12 +769,19 @@ def add(*args, like=None, **kwargs):
     """Add arguments element-wise."""
     if like == "scipy":
         return onp.add(*args, **kwargs)  # Dispatch scipy add to numpy backed specifically.
-    try:
-        return np.add(*args, **kwargs)
-    except TypeError:
-        # catch arg1 = torch, arg2=numpy error
-        # works fine with opposite order
-        return np.add(args[1], args[0], *args[2:], **kwargs)
+
+    if like == "torch":
+        # In autoray 0.6.5, np.add dispatches to torch instead of
+        # numpy if one parameter is a torch tensor and the other is
+        # a numpy array. torch.add raises an Exception if one of the
+        # arguments is a numpy array, so here we cast both arguments
+        # to be tensors.
+        dev = getattr(args[0], "device", None) or getattr(args[1], "device")
+        arg0 = np.asarray(args[0], device=dev, like=like)
+        arg1 = np.asarray(args[1], device=dev, like=like)
+        return np.add(arg0, arg1, *args[2:], **kwargs)
+
+    return np.add(*args, **kwargs)
 
 
 @multi_dispatch()

tests/devices/qubit/test_apply_operation.py

@@ -168,7 +168,7 @@ def test_phaseshift(self, method, wire, ml_framework):
         initial_state = qml.math.asarray(initial_state, like=ml_framework)
 
         phase = qml.math.asarray(-2.3, like=ml_framework)
-        shift = np.exp(qml.math.multiply(1j, phase))
+        shift = qml.math.exp(1j * qml.math.cast(phase, np.complex128))
 
         new_state = method(qml.PhaseShift(phase, wire), initial_state)