diff --git a/backends/qualcomm/quantizer/utils.py b/backends/qualcomm/quantizer/utils.py
index dc3d2a6841b..223b068375f 100644
--- a/backends/qualcomm/quantizer/utils.py
+++ b/backends/qualcomm/quantizer/utils.py
@@ -229,14 +229,29 @@ def get_default_8bit_qnn_ptq_config(
 ) -> QuantizationConfig:
     extra_args: Dict[str, Any] = {"eps": 2**-12}
 
-    act_quantization_spec = QuantizationSpec(
-        dtype=torch.uint8,
-        qscheme=(
-            torch.per_tensor_symmetric if act_symmetric else torch.per_tensor_affine
-        ),
-        ch_axis=0,
-        observer_or_fake_quant_ctr=act_observer.with_args(**extra_args),
-    )
+    if act_symmetric:
+        # If zero_point is 128, htp can do optimizations.
+        # If we keep quant_min and quant_max none, observer will default use 128 as zero_point.
+        # If we provide uint8 quant_min/max, it will use 127 as zero_point, which is undesired.
+        act_quantization_spec = QuantizationSpec(
+            dtype=torch.uint8,
+            qscheme=torch.per_tensor_symmetric,
+            ch_axis=0,
+            observer_or_fake_quant_ctr=act_observer.with_args(**extra_args),
+        )
+    else:
+        # PyTorch will remove redundant observers based on attributes such as:
+        # dtype, quant_min, quant_max, ch_axis, etc.
+        # Providing values like quant_min and quant_max can help observers compare
+        # and further reduce the number of observers.
+        act_quantization_spec = QuantizationSpec(
+            dtype=torch.uint8,
+            quant_min=torch.iinfo(torch.uint8).min,
+            quant_max=torch.iinfo(torch.uint8).max,
+            qscheme=torch.per_tensor_affine,
+            ch_axis=0,
+            observer_or_fake_quant_ctr=act_observer.with_args(**extra_args),
+        )
 
     weight_quantization_spec = QuantizationSpec(
         dtype=torch.int8,
@@ -409,6 +424,7 @@ def get_ptq_per_channel_quant_config(
         quant_min=torch.iinfo(act_dtype).min,
         quant_max=torch.iinfo(act_dtype).max,
         qscheme=torch.per_tensor_affine,
+        ch_axis=0,
         observer_or_fake_quant_ctr=MovingAverageMinMaxObserver.with_args(**extra_args),
     )
 
diff --git a/examples/qualcomm/utils.py b/examples/qualcomm/utils.py
index 06225be2d1c..ae5444023a4 100755
--- a/examples/qualcomm/utils.py
+++ b/examples/qualcomm/utils.py
@@ -348,7 +348,9 @@ def histogram(golden, predict):
     return (pa, mpa, miou, cls_iou)
 
 
-def get_imagenet_dataset(dataset_path, data_size, image_shape, crop_size=None):
+def get_imagenet_dataset(
+    dataset_path, data_size, image_shape, crop_size=None, shuffle=True
+):
     from torchvision import datasets, transforms
 
     def get_data_loader():
@@ -365,7 +367,7 @@ def get_data_loader():
         imagenet_data = datasets.ImageFolder(dataset_path, transform=preprocess)
         return torch.utils.data.DataLoader(
             imagenet_data,
-            shuffle=True,
+            shuffle=shuffle,
         )
 
     # prepare input data