diff --git a/TrainingExtensions/torch/src/python/aimet_torch/v2/experimental/onnx/_export.py b/TrainingExtensions/torch/src/python/aimet_torch/v2/experimental/onnx/_export.py
index 551b56b4768..a1ed887f7ac 100644
--- a/TrainingExtensions/torch/src/python/aimet_torch/v2/experimental/onnx/_export.py
+++ b/TrainingExtensions/torch/src/python/aimet_torch/v2/experimental/onnx/_export.py
@@ -225,21 +225,12 @@ def remove_quantization_nodes_from_onnx_graph(model: onnx.ModelProto):
     """
     tensor_to_encoding_map = {}
     name_to_producer, name_to_consumer = _get_producer_consumer_info_from_onnx_graph(model)
-    node_list = list(model.graph.node)
-
-    for node in node_list:
-        if node.op_type not in ONNX_QUANTIZER_OP_TYPES:
-            continue
+    qtzr_nodes = list(node for node in model.graph.node if node.op_type in ONNX_QUANTIZER_OP_TYPES)
 
+    for node in qtzr_nodes:
         # Get quantizer name in torch model
         encoding = _get_encoding_from_onnx_node(model, node)
 
-        # Remove qdq node from graph
-        model.graph.node.remove(node)
-
-        # Remove scale and offset from onnx graph
-        _remove_constants(model, node.input[1:])
-
         # Connect next node to the prev node of quantizer node
         if node.output[0] in name_to_consumer:
             tensor_to_encoding_map[node.input[0]] = encoding
@@ -265,6 +256,13 @@ def remove_quantization_nodes_from_onnx_graph(model: onnx.ModelProto):
         else:
             raise ValueError(f"Cannot find prev node and next node for quantization node {node.name}")
 
+    for node in qtzr_nodes:
+        # Remove qdq node from graph
+        model.graph.node.remove(node)
+
+        # Remove scale and offset from onnx graph
+        _remove_constants(model, node.input[1:])
+
     return tensor_to_encoding_map
 
 
diff --git a/TrainingExtensions/torch/test/python/v2/experimental/test_onnx.py b/TrainingExtensions/torch/test/python/v2/experimental/test_onnx.py
index af20b95abe7..8ca82ae98fe 100644
--- a/TrainingExtensions/torch/test/python/v2/experimental/test_onnx.py
+++ b/TrainingExtensions/torch/test/python/v2/experimental/test_onnx.py
@@ -43,10 +43,11 @@
 import torch
 import onnx
 import tempfile
+from aimet_common.quantsim_config.utils import get_path_for_per_channel_config
 from aimet_common import quantsim as quantsim_common
 import aimet_torch.v2 as aimet
 import aimet_torch.v2.quantization as Q
-from aimet_torch.v2.quantsim import quantsim, QuantizationSimModel
+from aimet_torch.v2.quantsim import QuantizationSimModel
 from torchvision.models import resnet18, mobilenet_v3_small
 from aimet_torch.v2.experimental.onnx._export import export as _export
 from aimet_torch.utils import get_all_quantizers
@@ -208,9 +209,11 @@ def forward(self, x: Q.QuantizedTensor):
 
 
 @torch.no_grad()
-@pytest.mark.parametrize("model_factory, input_shape", [(resnet18, (1, 3, 224, 224)),
-                                                        (mobilenet_v3_small, (1, 3, 224, 224)),
-                                                        ])
+@pytest.mark.parametrize(
+    "model_factory,      input_shape", [
+    (resnet18,           (1, 3, 224, 224)),
+    (mobilenet_v3_small, (1, 3, 224, 224)),
+])
 def test_export_torchvision_models(model_factory, input_shape):
     """
     When: Export quantized torchvision model
@@ -218,7 +221,7 @@ def test_export_torchvision_models(model_factory, input_shape):
     x = torch.randn(input_shape)
     model = model_factory().eval()
     model = prepare_model(model)
-    model = QuantizationSimModel(model, x).model
+    model = QuantizationSimModel(model, x, config_file=get_path_for_per_channel_config()).model
 
     with aimet.nn.compute_encodings(model):
         model(x)
@@ -276,7 +279,7 @@ def test_quantsim_export_torchvision_models(model_factory, input_shape, encoding
     x = torch.randn(input_shape)
     model = model_factory().eval()
     model = prepare_model(model)
-    sim = QuantizationSimModel(model, x)
+    sim = QuantizationSimModel(model, x, config_file=get_path_for_per_channel_config())
 
     sim.compute_encodings(lambda m, _: m(x), None)