diff --git a/nncf/experimental/common/quantization/algorithms/post_training/algorithm.py b/nncf/experimental/common/quantization/algorithms/post_training/algorithm.py
index efbc5bb7449..ac2d86a3056 100644
--- a/nncf/experimental/common/quantization/algorithms/post_training/algorithm.py
+++ b/nncf/experimental/common/quantization/algorithms/post_training/algorithm.py
@@ -47,7 +47,7 @@ def __init__(
         weights_range_estimator_params: Optional[RangeEstimatorParameters] = None,
     ):
         """
-        :param quantizer: NNCFQuantizer to use in MiMaxRageInit algorithm.
+        :param quantizer: NNCFQuantizer to use in MiMaxRangeInit algorithm.
         :param subset_size: Size of a subset to calculate activations
             statistics used for quantization.
         :param fast_bias_correction: Setting this option to `False` enables a different
diff --git a/nncf/experimental/common/quantization/algorithms/post_training/pipeline.py b/nncf/experimental/common/quantization/algorithms/post_training/pipeline.py
index dab7b2be856..5c8e521f65a 100644
--- a/nncf/experimental/common/quantization/algorithms/post_training/pipeline.py
+++ b/nncf/experimental/common/quantization/algorithms/post_training/pipeline.py
@@ -44,7 +44,7 @@ def experimental_create_ptq_pipeline(
         2) MinMaxRangeInit
         3) FastBiasCorrection or BiasCorrection
 
-    :param quantizer: NNCFQuantizer to use in MiMaxRageInit algorithm.
+    :param quantizer: NNCFQuantizer to use in MiMaxRangeInit algorithm.
     :param subset_size: Size of a subset to calculate activations
         statistics used for quantization.
     :param fast_bias_correction: Setting this option to `False` enables a different
@@ -66,7 +66,7 @@ def experimental_create_ptq_pipeline(
     if smooth_quant_params is None:
         smooth_quant_params = AdvancedSmoothQuantParameters()
 
-    if smooth_quant and smooth_quant_params.convolution >= 0 or smooth_quant_params.matmul >= 0:
+    if smooth_quant and (smooth_quant_params.convolution >= 0 or smooth_quant_params.matmul >= 0):
         alpha_map = {"convolution": smooth_quant_params.convolution, "matmul": smooth_quant_params.matmul}
         pipeline_steps.append([SmoothQuant(subset_size, False, alpha_map=alpha_map)])
 
diff --git a/nncf/experimental/common/quantization/algorithms/quantizer/fx_quantizer.py b/nncf/experimental/common/quantization/algorithms/quantizer/fx_quantizer.py
index db0ae167132..33e0ef94a79 100644
--- a/nncf/experimental/common/quantization/algorithms/quantizer/fx_quantizer.py
+++ b/nncf/experimental/common/quantization/algorithms/quantizer/fx_quantizer.py
@@ -41,7 +41,8 @@ def __init__(self, quantizer: Quantizer):
     def get_quantization_setup(self, model: torch.fx.GraphModule, nncf_graph: NNCFGraph) -> SingleConfigQuantizerSetup:
         anotated_model = deepcopy(model)
 
-        self._quantizer.transform_for_annotation(anotated_model)
+        # self._quantizer.transform_for_annotation is called in the nncf quantize_pt2e method
+        # before the nncf_graph building.
         self._quantizer.annotate(anotated_model)
         self._quantizer.validate(anotated_model)
         return self.get_quantizer_config_from_anotated_model(anotated_model)