diff --git a/examples/qualcomm/custom_op/custom_ops_1.py b/examples/qualcomm/custom_op/custom_ops_1.py
index 31b3b6ff3ec..ed99eabc9c8 100644
--- a/examples/qualcomm/custom_op/custom_ops_1.py
+++ b/examples/qualcomm/custom_op/custom_ops_1.py
@@ -70,11 +70,10 @@ def annotate_custom(gm: torch.fx.GraphModule) -> None:
     This function is specific for custom op.
     The source_fn of the rewritten nn module turns out to be "my_ops.mul3.default"
     """
-    from executorch.backends.qualcomm.quantizer.annotators import _is_annotated
-
     from executorch.backends.qualcomm.quantizer.qconfig import (
         get_ptq_per_channel_quant_config,
     )
+    from executorch.backends.qualcomm.quantizer.rules import _is_annotated
     from torch.fx import Node
     from torchao.quantization.pt2e.quantizer import QuantizationAnnotation
     from torchao.quantization.pt2e.quantizer.quantizer import Q_ANNOTATION_KEY
diff --git a/examples/qualcomm/oss_scripts/fastvit.py b/examples/qualcomm/oss_scripts/fastvit.py
index 3e620ab0300..87d90bb61b7 100644
--- a/examples/qualcomm/oss_scripts/fastvit.py
+++ b/examples/qualcomm/oss_scripts/fastvit.py
@@ -12,16 +12,13 @@
 import numpy as np
 import torch
 
-from executorch.backends.qualcomm.quantizer.annotators import (
-    QuantizationConfig,
-    QuantizationSpec,
-)
 from executorch.backends.qualcomm.quantizer.observers.per_channel_param_observer import (
     PerChannelParamObserver,
 )
 from executorch.backends.qualcomm.quantizer.qconfig import (
     _derived_bias_quant_spec,
     MovingAverageMinMaxObserver,
+    QuantizationConfig,
 )
 
 from executorch.backends.qualcomm.quantizer.quantizer import QuantDtype
@@ -40,6 +37,7 @@
     SimpleADB,
     topk_accuracy,
 )
+from torchao.quantization.pt2e.quantizer import QuantizationSpec
 
 
 def get_instance(repo_path: str, checkpoint_path: str):