diff --git a/hls4ml/model/optimizer/passes/linear.py b/hls4ml/model/optimizer/passes/linear.py
index b1aee7adc..ce0308eb6 100644
--- a/hls4ml/model/optimizer/passes/linear.py
+++ b/hls4ml/model/optimizer/passes/linear.py
@@ -40,7 +40,6 @@ def transform(self, model, node):
         # if the activation has a quantizer (usually from a QONNX Quant node), set the previous node's output precision
         if quantizer is not None:
             prev_node.set_attr("quantizer", quantizer)
-            prev_node.types['result_t'] = quantizer.hls_type
             prev_node.get_output_variable().type.precision = quantizer.hls_type
         model.remove_node(node)
         return True
diff --git a/hls4ml/model/optimizer/passes/merge_const.py b/hls4ml/model/optimizer/passes/merge_const.py
index a75ed27ac..bdf744783 100644
--- a/hls4ml/model/optimizer/passes/merge_const.py
+++ b/hls4ml/model/optimizer/passes/merge_const.py
@@ -54,7 +54,6 @@ def transform(self, model, node):
         const_node0.set_attr('quantizer', quantizer)  # overwrite the quantizer
         if quantizer:
             const_node0.set_attr('quantizer', quantizer)
-            const_node0.types['result_t'] = quantizer.hls_type
             const_node0.get_output_variable().type.precision = quantizer.hls_type
         const_node0.set_attr('value', new_val)
 
diff --git a/hls4ml/model/optimizer/passes/quant_opt.py b/hls4ml/model/optimizer/passes/quant_opt.py
index cac29b504..e2e3d1401 100644
--- a/hls4ml/model/optimizer/passes/quant_opt.py
+++ b/hls4ml/model/optimizer/passes/quant_opt.py
@@ -194,7 +194,6 @@ def transform(self, model, node):
 
         const_node = node.get_input_node(node.inputs[0])
         const_node.set_attr('quantizer', quantizer)
-        const_node.set_attr('result_t', precision)
         const_node.get_output_variable().type.precision = precision
 
         # Should we update the configuration to reflect the new precision? I don't think it's necessary
@@ -331,7 +330,6 @@ def transform(self, model, node):
         const_node.set_attr('value', new_val)
         const_node.set_attr('quantizer', quantizer)
 
-        const_node.types['result_t'].precision = precision
         const_node.get_output_variable().type.precision = precision
 
         inshape = node.get_input_variable().shape