diff --git a/merlin/pcvl_pytorch/locirc_to_tensor.py b/merlin/pcvl_pytorch/locirc_to_tensor.py
index ba7f82c6..9fefd709 100644
--- a/merlin/pcvl_pytorch/locirc_to_tensor.py
+++ b/merlin/pcvl_pytorch/locirc_to_tensor.py
@@ -23,9 +23,12 @@
 from __future__ import annotations
 
 import random
+import ast
 
 import torch
 from multipledispatch import dispatch
+
+from perceval.utils import Expression
 from perceval.components import (
     BS,
     PERM,
@@ -443,11 +446,13 @@ def _compute_tensor(self, comp: AComponent) -> torch.Tensor:  # type: ignore[no-
             NotImplementedError: If BS convention is not supported
         """
         param_values = []
-
-        for _index, param in enumerate(comp.get_parameters(all_params=True)):
+        for index, param in enumerate(comp.get_parameters(all_params=True, expressions=True)):
             if param.is_variable:
-                (tensor_id, idx_in_tensor) = self.param_mapping[param.name]
-                param_values.append(self.torch_params[tensor_id][..., idx_in_tensor])
+                if isinstance(param, Expression):
+                    param_values.append(self._parse_expression(param))
+                else:
+                    (tensor_id, idx_in_tensor) = self.param_mapping[param.name]
+                    param_values.append(self.torch_params[tensor_id][..., idx_in_tensor])
             else:
                 param_values.append(
                     torch.tensor(
@@ -509,8 +514,11 @@ def _compute_tensor(self, comp: AComponent) -> torch.Tensor:  # type: ignore[no-
             Batched 1x1 phase tensor of shape (batch_size, 1, 1)
         """
         if comp.param("phi").is_variable:
-            (tensor_id, idx_in_tensor) = self.param_mapping[comp.param("phi").name]
-            phase = self.torch_params[tensor_id][..., idx_in_tensor]
+            if isinstance(comp.param("phi"), Expression):
+                phase = self._parse_expression(comp.param("phi"))
+            else:
+                (tensor_id, idx_in_tensor) = self.param_mapping[comp.param("phi").name]
+                phase = self.torch_params[tensor_id][..., idx_in_tensor]
         else:
             phase = torch.tensor(
                 comp.param("phi")._value, dtype=self.tensor_fdtype, device=self.device
@@ -525,3 +533,21 @@ def _compute_tensor(self, comp: AComponent) -> torch.Tensor:  # type: ignore[no-
             -1, 1
         )  # reshape so that in any case, we have 2 dim
         return unitary_tensor.unsqueeze(-1)  # to change shape of tensor to (b, 1, 1)
+
+    def _parse_expression(self, expression: Expression) -> torch.Tensor:
+        """Returns value of a given expression by parsing its name."""
+        # Base params in expression
+        param_list = expression.parameters
+        tensor_ids_and_indices = [self.param_mapping[p.name] for p in param_list]
+
+        assign_params = {
+            param_list[i].name: self.torch_params[id][..., idx]
+            for i, (id, idx) in enumerate(tensor_ids_and_indices)
+        }
+        
+        # Use ast to parse expression name & assign value
+        tree = ast.parse(expression.name, mode="eval")
+        code = compile(tree, "<string>", mode="eval")
+        value = eval(code, {}, assign_params)
+        
+        return value