diff --git a/optimum/exporters/executorch/quantization.py b/optimum/exporters/executorch/quantization.py
index 7e32244..53c3d4e 100644
--- a/optimum/exporters/executorch/quantization.py
+++ b/optimum/exporters/executorch/quantization.py
@@ -18,6 +18,11 @@
 import torch
 
 
+# Applied to IntxWeightOnlyConfig and Int8DynamicActivationIntxWeightConfig.
+# Not applied to Int4WeightOnlyConfig (different API) or UIntxWeightOnlyConfig (no such param).
+DEFAULT_QPARAMS_ALGORITHM = "hqq_scale_only"
+
+
 def quantize_model_(
     eager_model: torch.nn.Module,
     qlinear_config: Optional[str] = None,
@@ -25,7 +30,12 @@ def quantize_model_(
     qlinear_packing_format: Optional[str] = None,
     qembedding_config: Optional[str] = None,
     qembedding_group_size: Optional[int] = 0,
+    qparams_algorithm: Optional[str] = None,
 ) -> torch.nn.Module:
+    # qparams_algorithm is applied to IntxWeightOnlyConfig and Int8DynamicActivationIntxWeightConfig.
+    # Not applied to Int4WeightOnlyConfig (different API) or UIntxWeightOnlyConfig (no such param).
+    if qparams_algorithm is None:
+        qparams_algorithm = DEFAULT_QPARAMS_ALGORITHM
     if not (qlinear_config or qembedding_config):
         return
 
@@ -54,10 +64,12 @@ def quantize_model_(
             "4w": IntxWeightOnlyConfig(
                 weight_dtype=torch.int4,
                 granularity=embedding_weight_granularity,
+                intx_choose_qparams_algorithm=qparams_algorithm,
             ),
             "8w": IntxWeightOnlyConfig(
                 weight_dtype=torch.int8,
                 granularity=embedding_weight_granularity,
+                intx_choose_qparams_algorithm=qparams_algorithm,
             ),
         }[qembedding_config]
 
@@ -75,6 +87,7 @@ def build_linear_config(quant_config_key: str, granularity: str, packing_format:
                 return Int8DynamicActivationIntxWeightConfig(
                     weight_dtype=torch.int4,
                     weight_granularity=granularity,
+                    intx_choose_qparams_algorithm=qparams_algorithm,
                 )
             if quant_config_key == "4w":
                 # Determine if we need to use Int4WeightOnlyConfig with int4_packing_format
@@ -88,16 +101,19 @@ def build_linear_config(quant_config_key: str, granularity: str, packing_format:
                     return IntxWeightOnlyConfig(
                         weight_dtype=torch.int4,
                         granularity=granularity,
+                        intx_choose_qparams_algorithm=qparams_algorithm,
                     )
             if quant_config_key == "8w":
                 return IntxWeightOnlyConfig(
                     weight_dtype=torch.int8,
                     granularity=granularity,
+                    intx_choose_qparams_algorithm=qparams_algorithm,
                 )
             if quant_config_key == "8da8w":
                 return Int8DynamicActivationIntxWeightConfig(
                     weight_dtype=torch.int8,
                     weight_granularity=PerAxis(0),
+                    intx_choose_qparams_algorithm=qparams_algorithm,
                 )
             if quant_config_key == "fpa4w":
                 # Need to import to load the ops