Add DeviceDerivatives jacobian product calculator

doc/releases/changelog-dev.md

@@ -99,9 +99,10 @@
   [(#4628)](https://github.com/PennyLaneAI/pennylane/pull/4628)
   [(#4649)](https://github.com/PennyLaneAI/pennylane/pull/4649)
 
-* The `JacobianProductCalculator` abstract base class and implementation `TransformJacobianProducts`
-  have been added to `pennylane.interfaces.jacobian_products`.
+* The `JacobianProductCalculator` abstract base class and implementations `TransformJacobianProducts`
+  and `DeviceDerivatives` have been added to `pennylane.interfaces.jacobian_products`.
   [(#4435)](https://github.com/PennyLaneAI/pennylane/pull/4435)
+  [(#4527)](https://github.com/PennyLaneAI/pennylane/pull/4527)
 
 * Extended ``qml.qchem.import_state`` to import wavefunctions from MPS DMRG and SHCI classical
   calculations performed with the Block2 and Dice libraries, incorporating new tests and wavefunction

pennylane/_device.py

@@ -560,6 +560,9 @@ def execute_and_gradients(self, circuits, method="jacobian", **kwargs):
             tuple[list[array[float]], list[array[float]]]: Tuple containing list of measured value(s)
             and list of Jacobians. Returned Jacobians should be of shape ``(output_shape, num_params)``.
         """
+        if self.tracker.active:
+            self.tracker.update(execute_and_derivative_batches=1, derivatives=len(circuits))
+            self.tracker.record()
         gradient_method = getattr(self, method)
 
         res = []
@@ -593,6 +596,9 @@ def gradients(self, circuits, method="jacobian", **kwargs):
             list[array[float]]: List of Jacobians. Returned Jacobians should be of
             shape ``(output_shape, num_params)``.
         """
+        if self.tracker.active:
+            self.tracker.update(derivatives=len(circuits))
+            self.tracker.record()
         gradient_method = getattr(self, method)
         return [gradient_method(circuit, **kwargs) for circuit in circuits]
 

pennylane/gradients/hadamard_gradient.py

@@ -207,6 +207,10 @@ def hadamard_grad(
     assert_no_state_returns(tape.measurements, transform_name)
     assert_no_variance(tape.measurements, transform_name)
     assert_no_tape_batching(tape, transform_name)
+    if len(tape.measurements) > 1 and tape.shots.has_partitioned_shots:
+        raise NotImplementedError(
+            "hadamard gradient does not support multiple measurements with partitioned shots."
+        )
 
     if argnum is None and not tape.trainable_params:
         return _no_trainable_grad(tape)
@@ -323,12 +327,14 @@ def _expval_hadamard_grad(tape, argnum, aux_wire):
     multi_params = len(tape.trainable_params) > 1
 
     def processing_fn(results):  # pylint: disable=too-many-branches
-        final_res = [
-            [qml.math.convert_like(2 * coeff * r, r) for r in res]
-            if isinstance(res, tuple)
-            else qml.math.convert_like(2 * coeff * res, res)
-            for coeff, res in zip(coeffs, results)
-        ]
+        """Post processing function for computing a hadamard gradient."""
+        final_res = []
+        for coeff, res in zip(coeffs, results):
+            if isinstance(res, tuple):
+                new_val = [qml.math.convert_like(2 * coeff * r, r) for r in res]
+            else:
+                new_val = qml.math.convert_like(2 * coeff * res, res)
+            final_res.append(new_val)
 
         # Post process for probs
         if measurements_probs:

pennylane/interfaces/__init__.py

@@ -48,6 +48,7 @@
 
     ~interfaces.jacobian_products.JacobianProductCalculator
     ~interfaces.jacobian_products.TransformJacobianProducts
+    ~interfaces.jacobian_products.DeviceDerivatives
 
 """
 from .execution import cache_execute, execute, INTERFACE_MAP, SUPPORTED_INTERFACES