Implement ExpectationMP.process_counts

Fixes PennyLaneAI#5241

doc/releases/changelog-dev.md

@@ -179,6 +179,9 @@
 
 <h3>Improvements 🛠</h3>
 
+* Implemented the method `process_counts` in `ExpectationMp`.
+  [(#5241)](https://github.com/PennyLaneAI/pennylane/issues/5241)
+
 <h4>Faster gradients with VJPs and other performance improvements</h4>
 
 * Adjoint device VJP's are now supported with `jax.jacobian`. `device_vjp=True` is
@@ -683,4 +686,5 @@ Lee J. O'Riordan,
 Mudit Pandey,
 Alex Preciado,
 Matthew Silverman,
-Jay Soni.
+Jay Soni,
+Tarun Kumar Allamsetty.

pennylane/measurements/expval.py

@@ -136,3 +136,7 @@ def process_state(self, state: Sequence[complex], wire_order: Wires):
             prob = qml.probs(wires=self.wires).process_state(state=state, wire_order=wire_order)
         # In case of broadcasting, `prob` has two axes and this is a matrix-vector product
         return qml.math.dot(prob, eigvals)
+
+    def process_counts(self, counts: dict, wire_order: Wires):
+        probs = qml.probs(wires=self.wires).process_counts(counts=counts, wire_order=wire_order)
+        return qml.math.dot(probs, self.eigvals())

tests/measurements/test_expval.py

@@ -318,3 +318,19 @@ def cost_circuit(params):
         energy_batched = cost_circuit(params)
 
         assert qml.math.allequal(energy_batched, energy)
+
+    @pytest.mark.parametrize(
+        "wire, expected",
+        [
+            (0, 0.0),
+            (1, 1.0),
+        ],
+    )
+    def test_estimate_expectation_with_counts(self, wire, expected):
+        counts = {"000": 100, "100": 100}
+
+        wire_order = qml.wires.Wires((0, 1, 2))
+
+        res = qml.expval(qml.Z(wire)).process_counts(counts=counts, wire_order=wire_order)
+
+        assert np.allclose(res, expected)