diff --git a/src/brevitas/quant_tensor/__init__.py b/src/brevitas/quant_tensor/__init__.py
index c66690c50..bfbe81446 100644
--- a/src/brevitas/quant_tensor/__init__.py
+++ b/src/brevitas/quant_tensor/__init__.py
@@ -22,11 +22,11 @@
 
 class QuantTensorBase(NamedTuple):
     value: Tensor
-    scale: Optional[Tensor]
-    zero_point: Optional[Tensor]
-    bit_width: Optional[Tensor]
-    signed_t: Optional[Tensor]
-    training_t: Optional[Tensor]
+    scale: Tensor
+    zero_point: Tensor
+    bit_width: Tensor
+    signed_t: Tensor
+    training_t: Tensor
 
 
 def _unpack_quant_tensor(input_data):
@@ -61,17 +61,11 @@ def __new__(cls, value, scale, zero_point, bit_width, signed, training):
 
     @property
     def signed(self):
-        if self.signed_t is not None:
-            return self.signed_t.item()
-        else:
-            return None
+        return self.signed_t.item()
 
     @property
     def training(self):
-        if self.training_t is not None:
-            return self.training_t.item()
-        else:
-            return None
+        return self.training_t.item()
 
     def __torch_function__(self, func, types, args=(), kwargs=None):
         if kwargs is None:
@@ -129,8 +123,7 @@ def device(self):
         value_device = self.value.device
         is_same_device = True
         for t in [self.scale, self.zero_point, self.bit_width]:
-            if t is not None:
-                is_same_device &= value_device == t.device
+            is_same_device &= value_device == t.device
         if not is_same_device:
             raise RuntimeError("Value and metadata are on different devices")
         return value_device
@@ -193,13 +186,13 @@ def is_zero_zero_point(tensor):
         return (tensor.zero_point == 0.).all()
 
     def check_scaling_factors_same(self, other):
-        if self.training is not None and self.training:
+        if self.training:
             return True
         if not torch.allclose(self.scale, other.scale):
             raise RuntimeError("Scaling factors are different")
 
     def check_zero_points_same(self, other):
-        if self.training is not None and self.training:
+        if self.training:
             return True
         if not torch.allclose(self.zero_point, other.zero_point):
             raise RuntimeError("Zero points are different")
@@ -226,7 +219,7 @@ def transpose(self, *args, **kwargs):
         tensor_meta = {
             'scale': self.scale, 'zero_point': self.zero_point, 'bit_width': self.bit_width}
         for k, tm in tensor_meta.items():
-            if tm is not None and len(value.shape) == len(tm.shape):
+            if len(value.shape) == len(tm.shape):
                 tensor_meta[k] = tm.transpose(*args, **kwargs)
         return self.set(value=value, **tensor_meta)
 
@@ -235,7 +228,7 @@ def permute(self, *args, **kwargs):
         tensor_meta = {
             'scale': self.scale, 'zero_point': self.zero_point, 'bit_width': self.bit_width}
         for k, tm in tensor_meta.items():
-            if tm is not None and len(value.shape) == len(tm.shape):
+            if len(value.shape) == len(tm.shape):
                 tensor_meta[k] = tm.permute(*args, **kwargs)
         return self.set(value=value, **tensor_meta)