Replace the type checking using property return_type with direct is…

.github/CHANGELOG.md

@@ -6,6 +6,9 @@
 
 ### Improvements
 
+* Replace the type checking using the property `return_type` of `MeasurementProcess` with direct `isinstance` checks.
+  [(#1044)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1044)
+
 * Update `qml.MultiControlledX` tests following the latest updates in PennyLane.
   [(#1040)](https://github.com/PennyLaneAI/pennylane-lightning/pull/1040)
 

pennylane_lightning/core/_adjoint_jacobian_base.py

@@ -21,7 +21,7 @@
 import numpy as np
 import pennylane as qml
 from pennylane import BasisState, QuantumFunctionError, StatePrep
-from pennylane.measurements import Expectation, MeasurementProcess, State
+from pennylane.measurements import ExpectationMP, MeasurementProcess, StateMP
 from pennylane.operation import Operation
 from pennylane.tape import QuantumTape
 
@@ -84,10 +84,10 @@
         if not measurements:
             return None
 
-        if len(measurements) == 1 and measurements[0].return_type is State:
-            return State
+        if len(measurements) == 1 and isinstance(measurements[0], StateMP):
+            return "state"
 
-        return Expectation
+        return "expval"
 
     def _process_jacobian_tape(self, tape: QuantumTape, split_obs: bool = False):
         """Process a tape, serializing and building a dictionary proper for
@@ -184,7 +184,7 @@
                 # the tape does not have measurements
                 return True
 
-            if tape_return_type is State:
+            if tape_return_type is "state":
                 raise QuantumFunctionError(
                     "Adjoint differentiation method does not support measurement StateMP."
                 )
@@ -194,12 +194,12 @@
                 # the tape does not have measurements or the gradient is 0.0
                 return True
 
-            if tape_return_type is State:
+            if tape_return_type is "state":
                 raise QuantumFunctionError(
                     "Adjoint differentiation does not support State measurements."
                 )
 
-        if any(m.return_type is not Expectation for m in tape.measurements):
+        if any(not isinstance(m, ExpectationMP) for m in tape.measurements):
             raise QuantumFunctionError(
                 "Adjoint differentiation method does not support expectation return type "
                 "mixed with other return types"

pennylane_lightning/core/_version.py

@@ -16,4 +16,4 @@
    Version number (major.minor.patch[-label])
 """
 
-__version__ = "0.41.0-dev5"
+__version__ = "0.41.0-dev6"

pennylane_lightning/core/lightning_base.py

@@ -23,7 +23,7 @@
 import pennylane as qml
 from pennylane import BasisState, StatePrep
 from pennylane.devices import QubitDevice
-from pennylane.measurements import Expectation, MeasurementProcess, State
+from pennylane.measurements import ExpectationMP, MeasurementProcess, StateMP
 from pennylane.operation import Operation
 from pennylane.ops import Prod, Projector, SProd, Sum
 from pennylane.wires import Wires
@@ -348,22 +348,22 @@
         if not measurements:
             return None
 
-        if len(measurements) == 1 and measurements[0].return_type is State:
+        if len(measurements) == 1 and isinstance(measurements[0], StateMP):
             # return State
             raise qml.QuantumFunctionError(
                 "Adjoint differentiation does not support State measurements."
             )
 
         # The return_type of measurement processes must be expectation
-        if any(m.return_type is not Expectation for m in measurements):
+        if any(not isinstance(m, ExpectationMP) for m in measurements):
             raise qml.QuantumFunctionError(
                 "Adjoint differentiation method does not support expectation return type "
                 "mixed with other return types"
             )
 
         for measurement in measurements:
             LightningBase._assert_adjdiff_no_projectors(measurement.obs)
-        return Expectation
+        return "expval"
 
     @staticmethod
     def _adjoint_jacobian_processing(jac):

tests/test_measurements.py

@@ -23,7 +23,7 @@
 from conftest import LightningDevice as ld
 from conftest import device_name, lightning_ops, validate_measurements
 from flaky import flaky
-from pennylane.measurements import Expectation, Shots, Variance
+from pennylane.measurements import ExpectationMP, Shots, VarianceMP
 
 if not ld._CPP_BINARY_AVAILABLE:
     pytest.skip("No binary module found. Skipping.", allow_module_level=True)
@@ -387,12 +387,12 @@ def circuit():
             circuit()
 
     def test_observable_return_type_is_expectation(self, dev):
-        """Test that the return type of the observable is :attr:`ObservableReturnTypes.Expectation`"""
+        """Test that the return type of the observable is :class:`ExpectationMP`"""
 
         @qml.qnode(dev)
         def circuit():
             res = qml.expval(qml.PauliZ(0))
-            assert res.return_type is Expectation
+            assert isinstance(res, ExpectationMP)
             return res
 
         circuit()
@@ -488,12 +488,12 @@ def circuit():
             circuit()
 
     def test_observable_return_type_is_variance(self, dev):
-        """Test that the return type of the observable is :attr:`ObservableReturnTypes.Variance`"""
+        """Test that the return type is :class:`VarianceMP`"""
 
         @qml.qnode(dev)
         def circuit():
             res = qml.var(qml.PauliZ(0))
-            assert res.return_type is Variance
+            assert isinstance(res, VarianceMP)
             return res
 
         circuit()