diff --git a/parity_tensor/parity_tensor.py b/parity_tensor/parity_tensor.py
index e224234..27ff0ef 100644
--- a/parity_tensor/parity_tensor.py
+++ b/parity_tensor/parity_tensor.py
@@ -7,6 +7,7 @@
 __all__ = ["ParityTensor"]
 
 import dataclasses
+import functools
 import typing
 import torch
 
@@ -20,11 +21,29 @@ class ParityTensor:
 
     edges: tuple[tuple[int, int], ...]
     tensor: torch.Tensor
+    mask: torch.Tensor | None = None
 
     def __post_init__(self) -> None:
         assert len(self.edges) == self.tensor.dim(), f"Edges length ({len(self.edges)}) must match tensor dimensions ({self.tensor.dim()})."
         for dim, (even, odd) in zip(self.tensor.shape, self.edges):
             assert even >= 0 and odd >= 0 and dim == even + odd, f"Dimension {dim} must equal sum of even ({even}) and odd ({odd}) parts, and both must be non-negative."
+        if self.mask is None:
+            self.mask = self._tensor_mask()
+
+    @classmethod
+    def _unqueeze(cls, tensor: torch.Tensor, index: int, dim: int) -> torch.Tensor:
+        return tensor.view([-1 if i == index else 1 for i in range(dim)])
+
+    @classmethod
+    def _edge_mask(cls, even: int, odd: int) -> torch.Tensor:
+        return torch.cat([torch.zeros(even, dtype=torch.bool), torch.ones(odd, dtype=torch.bool)])
+
+    def _tensor_mask(self) -> torch.Tensor:
+        return functools.reduce(
+            torch.logical_xor,
+            (self._unqueeze(self._edge_mask(even, odd), index, self.tensor.dim()) for index, (even, odd) in enumerate(self.edges)),
+            torch.ones_like(self.tensor, dtype=torch.bool),
+        )
 
     def _validate_edge_compatibility(self, other: ParityTensor) -> None:
         """