fix_permute_basis

pyqtorch/composite/sequence.py

@@ -85,7 +85,7 @@ def __init__(self, operations: list[Module]):
 
     @property
     def qubit_support(self) -> tuple:
-        return self._qubit_support
+        return tuple(sorted(self._qubit_support))
 
     def __iter__(self) -> Iterator:
         return iter(self.operations)

pyqtorch/utils.py

@@ -395,6 +395,7 @@ def expand_operator(
     by explicitly filling in identity matrices on all remaining qubits.
     """
     full_support = tuple(sorted(full_support))
+    qubit_support = tuple(sorted(qubit_support))
     if not set(qubit_support).issubset(set(full_support)):
         raise ValueError(
             "Expanding tensor operation requires a `full_support` argument "
@@ -405,7 +406,7 @@ def expand_operator(
         qubit_support += (i,)
         other = IMAT.clone().to(device=device, dtype=dtype).unsqueeze(2)
         operator = torch.kron(operator.contiguous(), other)
-    operator = permute_basis(operator, qubit_support)
+    operator = permute_basis(operator, qubit_support, inv=True)
     return operator
 
 
@@ -445,7 +446,7 @@ def promote_operator(operator: Tensor, target: int, n_qubits: int) -> Tensor:
     return operator
 
 
-def permute_basis(operator: Tensor, qubit_support: tuple) -> Tensor:
+def permute_basis(operator: Tensor, qubit_support: tuple, inv=False) -> Tensor:
     """Takes an operator tensor and permutes the rows and
     columns according to the order of the qubit support.
 
@@ -457,15 +458,20 @@ def permute_basis(operator: Tensor, qubit_support: tuple) -> Tensor:
         Tensor: Permuted operator.
     """
     ordered_support = argsort(qubit_support)
+    ranked_support = argsort(ordered_support)
     n_qubits = len(qubit_support)
-    if all(a == b for a, b in zip(ordered_support, list(range(n_qubits)))):
+    if all(a == b for a, b in zip(ranked_support, list(range(n_qubits)))):
         return operator
     batch_size = operator.size(-1)
     operator = operator.view([2] * 2 * n_qubits + [batch_size])
 
     perm = list(
-        tuple(ordered_support) + tuple(ordered_support + n_qubits) + (2 * n_qubits,)
+        tuple(ranked_support) + tuple(ranked_support + n_qubits) + (2 * n_qubits,)
     )
+
+    if inv:
+        perm = np.argsort(perm).tolist()
+
     return operator.permute(perm).reshape([2**n_qubits, 2**n_qubits, batch_size])