diff --git a/test/quantization/pt2e/test_quantize_pt2e.py b/test/quantization/pt2e/test_quantize_pt2e.py
index 0b5fd64120..5cd5e9d459 100644
--- a/test/quantization/pt2e/test_quantize_pt2e.py
+++ b/test/quantization/pt2e/test_quantize_pt2e.py
@@ -9,6 +9,7 @@
 # ruff: noqa: F841
 
 
+import copy
 import unittest
 
 import torch
@@ -21,6 +22,7 @@
     weight_observer_range_neg_127_to_127,
 )
 from torch.fx import Node
+from torch.testing import FileCheck
 from torch.testing._internal.common_quantization import (
     NodeSpec as ns,
 )
@@ -1630,6 +1632,101 @@ def forward(self, x):
                     if key != FROM_NODE_KEY:
                         self.assertEqual(n.meta[key], weight_meta[key])
 
+    def test_constant_folding_pass(self):
+        from torchao.quantization import (
+            MappingType,
+            PerGroup,
+            PerToken,
+        )
+        from torchao.quantization.pt2e._affine_quantization import (
+            AffineQuantizedMinMaxObserver,
+        )
+        from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e
+        from torchao.quantization.pt2e.quantizer import (
+            QuantizationAnnotation,
+            QuantizationSpec,
+            Quantizer,
+        )
+
+        class BackendAQuantizer(Quantizer):
+            def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+                for node in model.graph.nodes:
+                    if (
+                        node.op == "call_function"
+                        and node.target == torch.ops.aten.linear.default
+                    ):
+                        input_act = node.args[0]
+                        assert isinstance(input_act, torch.fx.Node)
+                        weight = node.args[1]
+                        assert isinstance(weight, torch.fx.Node)
+
+                        act_qspec = QuantizationSpec(
+                            dtype=torch.uint8,
+                            quant_min=0,
+                            quant_max=255,
+                            qscheme=None,
+                            is_dynamic=False,
+                            observer_or_fake_quant_ctr=AffineQuantizedMinMaxObserver.with_args(
+                                # TODO: maybe align the arg name here
+                                target_dtype=torch.uint8,
+                                mapping_type=MappingType.SYMMETRIC,
+                                granularity=PerToken(),
+                            ),
+                        )
+
+                        weight_qspec = QuantizationSpec(
+                            dtype=torch.uint8,
+                            quant_min=0,
+                            quant_max=255,
+                            qscheme=None,
+                            is_dynamic=False,
+                            observer_or_fake_quant_ctr=AffineQuantizedMinMaxObserver.with_args(
+                                target_dtype=torch.uint8,
+                                mapping_type=MappingType.SYMMETRIC,
+                                granularity=PerGroup(group_size=128),
+                            ),
+                        )
+                        node.meta["quantization_annotation"] = QuantizationAnnotation(
+                            input_qspec_map={
+                                input_act: act_qspec,
+                                weight: weight_qspec,
+                            },
+                            _annotated=True,
+                        )
+
+            def validate(self, model: torch.fx.GraphModule) -> None:
+                pass
+
+        class M(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.linear = torch.nn.Linear(128, 20)
+
+            def forward(self, x):
+                return self.linear(x)
+
+        example_inputs = (torch.randn(5, 128),)
+        model = M()
+        quantizer = BackendAQuantizer()
+        m = torch.export.export(model.eval(), example_inputs, strict=True).module()
+        m = prepare_pt2e(m, quantizer)
+        # Calibration
+        m(*example_inputs)
+        # Get the quantized model
+        m_fold = copy.deepcopy(m)
+        m_fold = convert_pt2e(m_fold, fold_quantize=True)
+
+        # If fold, check the graph only contains frozed params and no linear_weight
+        FileCheck().check("_frozen_param0").check_not("linear_weight").run(m_fold.code)
+
+        m_not_fold = copy.deepcopy(m)
+        m_not_fold = convert_pt2e(m_not_fold, fold_quantize=False)
+
+        # If not fold, check the graph doesn't contain frozed params and contain linear_weight
+        FileCheck().check_not("_frozen_param0").check("linear_weight").run(
+            m_not_fold.code
+        )
+
     def test_save_load(self):
         """Test save/load a quantized model"""
         m = self._get_pt2e_quantized_linear()