diff --git a/test/quantization/pt2e/test_quantize_pt2e.py b/test/quantization/pt2e/test_quantize_pt2e.py
index 730969ba9c..163f1ae5c3 100644
--- a/test/quantization/pt2e/test_quantize_pt2e.py
+++ b/test/quantization/pt2e/test_quantize_pt2e.py
@@ -2826,6 +2826,78 @@ def check_nn_module(node):
             if node.name == "mul":
                 check_nn_module(node)
 
+    def test_quantize_in_place_ops(self):
+        class TestQuantizer(Quantizer):
+            example_inputs = None
+
+            def set_example_inputs(self, example_inputs):
+                self.example_inputs = example_inputs
+
+            def transform_for_annotation(
+                self, model: torch.fx.GraphModule
+            ) -> torch.fx.GraphModule:
+                # Make a copy of the graph to ensure that we are using the
+                # return value of this function.
+                ep = torch.export.export(model, self.example_inputs)
+                ep = ep.run_decompositions({})
+                return ep.module()
+
+            def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+                act_qspec = QuantizationSpec(
+                    dtype=torch.uint8,
+                    quant_min=0,
+                    quant_max=255,
+                    qscheme=torch.per_tensor_affine,
+                    is_dynamic=False,
+                    observer_or_fake_quant_ctr=observer.default_observer
+                )
+                for node in model.graph.nodes:
+                    if (
+                        node.op == "call_function"
+                        and node.target == torch.ops.aten.add.Tensor
+                    ):
+                        input_act0 = node.args[0]
+                        assert isinstance(input_act0, torch.fx.Node)
+                        input_act1 = node.args[1]
+                        if isinstance(input_act1, torch.fx.Node):
+                            node.meta["quantization_annotation"] = QuantizationAnnotation(
+                                input_qspec_map={
+                                    input_act0: act_qspec,
+                                    input_act1: act_qspec,
+                                },
+                                output_qspec=act_qspec,
+                                _annotated=True,
+                            )
+                        else:
+                            # Handle case where second input is a constant
+                            node.meta["quantization_annotation"] = QuantizationAnnotation(
+                                input_qspec_map={
+                                    input_act0: act_qspec,
+                                },
+                                output_qspec=act_qspec,
+                                _annotated=True,
+                            )
+
+            def validate(self, model: torch.fx.GraphModule) -> None:
+                pass
+
+        class M(torch.nn.Module):
+            def forward(self, x):
+                return x + 3
+
+        m = M().eval()
+        quantizer = TestQuantizer()        
+        example_inputs = (torch.randn(1, 2, 3, 3),)
+        quantizer.set_example_inputs(example_inputs)
+        m = export_for_training(m, example_inputs, strict=True).module()
+        m = prepare_pt2e(m, quantizer)
+        m(*example_inputs)
+        m = convert_pt2e(m)
+        
+        # Verify the quantized model works
+        result = m(*example_inputs)
+        self.assertIsNotNone(result)
+
 
 @skipIfNoQNNPACK
 @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_7, "Requires torch 2.7+")