Updating qml.equal

pennylane/circuit_graph.py

@@ -298,15 +298,7 @@ def ancestors(self, ops):
         Returns:
             list[Operator]: ancestors of the given operators
         """
-        # anc = set(
-        #     self._graph.get_node_data(n)
-        #     for n in set().union(
-        #         # rx.ancestors() returns node indexes instead of node-values
-        #         *(rx.ancestors(self._graph, self._indices[id(o)]) for o in ops)
-        #     )
-        # )
-        # return anc - set(ops)
-        # rx.ancestors() returns node indexes instead of node-values
+        # rx.ancestors() returns node indices instead of node-values
         all_indices = set().union(*(rx.ancestors(self._graph, self._indices[id(o)]) for o in ops))
         double_op_indices = set(self._indices[id(o)] for o in ops)
         ancestor_indices = all_indices - double_op_indices
@@ -322,15 +314,7 @@ def descendants(self, ops):
         Returns:
             list[Operator]: descendants of the given operators
         """
-        # des = set(
-        #     self._graph.get_node_data(n)
-        #     for n in set().union(
-        #         # rx.descendants() returns node indexes instead of node-values
-        #         *(rx.descendants(self._graph, self._indices[id(o)]) for o in ops)
-        #     )
-        # )
-        # return des - set(ops)
-        # rx.descendants() returns node indexes instead of node-values
+        # rx.descendants() returns node indices instead of node-values
         all_indices = set().union(*(rx.descendants(self._graph, self._indices[id(o)]) for o in ops))
         double_op_indices = set(self._indices[id(o)] for o in ops)
         ancestor_indices = all_indices - double_op_indices
@@ -418,7 +402,6 @@ def parametrized_layers(self):
 
                 # check if any of the dependents are in the
                 # currently assembled layer
-                # if set(current.ops) & sub:
                 if any(o1 is o2 for o1 in current.ops for o2 in sub):
                     # operator depends on current layer, start a new layer
                     current = Layer([], [])

pennylane/ops/functions/equal.py

@@ -15,6 +15,7 @@
 This module contains the qml.equal function.
 """
 # pylint: disable=too-many-arguments,too-many-return-statements
+from collections.abc import Iterable
 from functools import singledispatch
 from typing import Union
 import pennylane as qml
@@ -378,7 +379,14 @@ def _equal_shadow_measurements(op1: ShadowExpvalMP, op2: ShadowExpvalMP, **kwarg
     """Determine whether two ShadowExpvalMP objects are equal"""
 
     wires_match = op1.wires == op2.wires
-    H_match = op1.H == op2.H
+
+    if isinstance(op1.H, Operator) and isinstance(op2.H, Operator):
+        H_match = qml.equal(op1.H, op2.H)
+    elif isinstance(op1.H, Iterable) and isinstance(op2.H, Iterable):
+        H_match = all(qml.equal(o1, o2) for o1, o2 in zip(op1.H, op2.H))
+    else:
+        return False
+
     k_match = op1.k == op2.k
 
     return wires_match and H_match and k_match

pennylane/transforms/metric_tensor.py

@@ -671,7 +671,6 @@ def _get_first_term_tapes(layer_i, layer_j, allow_nonunitary, aux_wire):
     tapes = []
     ids = []
     # Exclude the backwards cone of layer_i from the backwards cone of layer_j
-    # ops_between_cgens = [op for op in layer_j.pre_ops if op not in layer_i.pre_ops]
     ops_between_cgens = [
         op1 for op1 in layer_j.pre_ops if not any(op1 is op2 for op2 in layer_i.pre_ops)
     ]

tests/ops/functions/test_equal.py

@@ -127,8 +127,8 @@
     qml.expval(qml.PauliX(1)),
     qml.probs(wires=1),
     qml.probs(wires=0),
-    qml.probs(qml.PauliZ(0)),
-    qml.probs(qml.PauliZ(1)),
+    qml.probs(op=qml.PauliZ(0)),
+    qml.probs(op=qml.PauliZ(1)),
     qml.state(),
     qml.vn_entropy(wires=0),
     qml.vn_entropy(wires=0, log_base=np.e),
@@ -151,7 +151,8 @@
     qml.shadow_expval(
         H=qml.Hamiltonian(
             [1.0, 1.0], [qml.PauliX(0) @ qml.PauliX(1), qml.PauliZ(0) @ qml.PauliZ(1)]
-        )
+        ),
+        k=3
     ),
     ExpectationMP(eigvals=[1, -1]),
     ExpectationMP(eigvals=[1, 2]),